>> Narrator: theCUBE's live coverage is made possible by funding from Dell Technologies, creating technologies that drive human progress. (bright upbeat music plays) >> Welcome back to Barcelona, everybody. You're watching theCUBE's coverage of MWC '23, my name is Dave Vellante. Just left a meeting with the CEO of Cisco, Chuck Robbins, to meet with Jeetu Patel, who's our Executive Vice President and General Manager of security and collaboration at Cisco. Good to see you. >> You never leave a meeting with Chuck Robbins to meet with Jeetu Patel. >> Well, I did. >> That's a bad idea. >> Walked right out. I said, hey, I got an interview to do, right? So, and I'm excited about this. Thanks so much for coming on. >> Thank you for having me. It's a pleasure. >> So, I mean you run such an important part of the business. I mean, obviously the collaboration business but also security. So many changes going on in the security market. Maybe we could start there. I mean, there hasn't been a ton of security talk here Jeetu, because I think it's almost assumed. It was 45 minutes into the keynote yesterday before anybody even mentioned security. >> Huh. >> Right? And so, but it's the most important topic in the enterprise IT world. And obviously is important here. So why is it you think that it's not the first topic that people mention. >> You know, it's a complicated subject area and it's intimidating. And actually that's one of the things that the industry screwed up on. Where we need to simplify security so it actually gets to be relatable for every person on the planet. But, if you think about what's happening in security, it's not just important for business it's critical infrastructure that if you had a breach, you know lives are cost now. Because hospitals could go down, your water supply could go down, your electricity could go down. And so it's one of these things that we have to take pretty seriously. And, it's 51% of all breaches happen because of negligence, not because of malicious intent. >> It's that low. Interesting. I always- >> Someone else told me the same thing, that they though it'd be higher, yeah. >> I always say bad user behavior is going to trump good security every time. >> Every single time. >> You can't beat it. But, you know, it's funny- >> Jeetu: Every single time. >> Back, the earlier part of last decade, you could see that security was becoming a board level issue. It became, it was on the agenda every quarter. And, I remember doing some research at the time, and I asked, I was interviewing Robert Gates, former Defense Secretary, and I asked him, yeah, but we're getting attacked but don't we have the best offense? Can't we have the best technology? He said, yeah but we have so much critical infrastructure the risks to United States are higher. So we have to be careful about how we use security as an offensive weapon, you know? And now you're seeing the future of war involves security and what's going on in Ukraine. It's a whole different ballgame. >> It is, and the scales always tip towards the adversary, not towards the defender, because you have to be right every single time. They have to be right once. >> Yeah. And, to the other point, about bad user behavior. It's going now beyond the board level, to it's everybody's responsibility. >> That's right. >> And everybody's sort of aware of it, everybody's been hacked. And, that's where it being such a complicated topic is problematic. >> It is, and it's actually, what got us this far will not get us to where we need to get to if we don't simplify security radically. You know? The experience has to be almost invisible. And what used to be the case was sophistication had to get to a certain level, for efficacy to go up. But now, that sophistication has turned to complexity. And there's an inverse relationship between complexity and efficacy. So the simpler you make security, the more effective it gets. And so I'll give you an example. We have this great kind of innovation we've done around passwordless, right? Everyone hates passwords. You shouldn't have passwords in 2023. But, when you get to passwordless security, not only do you reduce a whole lot of friction for the user, you actually make the system safer. And that's what you need to do, is you have to make it simpler while making it more effective. And, I think that's what the future is going to hold. >> Yeah, and CISOs tell me that they're, you know zero trust before the pandemic was like, yeah, yeah zero trust. And now it's like a mandate. >> Yeah. >> Every CISO you talk to says, yes we're implementing a zero trust architecture. And a big part of that is that, if they can confirm zero trust, they can get to market a lot faster with revenue generating or critical projects. And many projects as we know are being pushed back, >> Yeah. >> you know? 'Cause of the macro. But, projects that drive revenue and value they want to accelerate, and a zero trust confirmation allows people to rubber stamp it and go faster. >> And the whole concept of zero trust is least privileged access, right? But what we want to make sure that we get to is continuous assessment of least privileged access, not just a one time at login. >> Dave: 'Cause things change so frequently. >> So, for example, if you happen to be someone that's logged into the system and now you start doing some anomalous behavior that doesn't sound like Dave, we want to be able to intercept, not just do it at the time that you're authenticating Dave to come in. >> So you guys got a good business. I mentioned the macro before. >> Yeah. >> The big theme is consolidating redundant vendors. So a company with a portfolio like Cisco's obviously has an advantage there. You know, you guys had great earnings. Palo Alto is another company that can consolidate. Tom Gillis, great pickup. Guy's amazing, you know? >> Love Tom. >> Great respect. Just had a little webinar session with him, where he was geeking out with the analyst and so- >> Yeah, yeah. >> Learned a lot there. Now you guys have some news, at the event event with Mercedes? >> We do. >> Take us through that, and I want to get your take on hybrid work and what's happening there. But what's going on with Mercedes? >> Yeah so look, it all actually stems from the hybrid work story, which is the future is going to be hybrid, people are going to work in mixed mode. Sometimes you'll be in the office, sometimes at home, sometimes somewhere in the middle. One of the places that people are working more and more from is their cars. And connected cars are getting to be a reality. And in fact, cars sometimes become an extension of your home office. And many a times I have found myself in a parking lot, because I didn't have enough time to get home and I was in a parking lot taking a conference call. And so we've made that section easier, because we have now partnered with Mercedes. And they aren't the first partner, but they're a very important partner where we are going to have Webex available, through the connected car, natively in Mercedes. >> Ah, okay. So I could take a call, I can do it all the time. I find good service, pull over, got to take the meeting. >> Yeah. >> I don't want to be driving. I got to concentrate. >> That's right. >> You know, or sometimes, I'll have the picture on and it's not good. >> That's right. >> Okay, so it'll be through the console, and all through the internet? >> It'll be through the console. And many people ask me like, how's safety going to work over that? Because you don't want to do video calls while you're driving. Exactly right. So when you're driving, the video automatically turns off. And you'll have audio going on, just like a conference call. But the moment you stop and put it in park, you can have video turned on. >> Now, of course the whole hybrid work trend, we, seems like a long time ago but it doesn't, you know? And it's really changed the security dynamic as well, didn't it? >> It has, it has. >> I mean, immediately you had to go protect new endpoints. And those changes, I felt at the time, were permanent. And I think it's still the case, but there's an equilibrium now happening. People as they come back to the office, you see a number of companies are mandating back to work. Maybe the central offices, or the headquarters, were underfunded. So what's going on out there in terms of that balance? >> Well firstly, there's no unanimous consensus on the way that the future is going to be, except that it's going to be hybrid. And the reason I say that is some companies mandate two days a week, some companies mandate five days a week, some companies don't mandate at all. Some companies are completely remote. But whatever way you go, you want to make sure that regardless of where you're working from, people can have an inclusive experience. You know? And, when they have that experience, you want to be able to work from a managed device or an unmanaged device, from a corporate network or from a Starbucks, from on the road or stationary. And whenever you do any of those things, we want to make sure that security is always handled, and you don't have to worry about that. And so the way that we say it is the company that created the VPN, which is Cisco, is the one that's going to kill it. Because what we'll do is we'll make it simple enough so that you don't, you as a user, never have to worry about what connection you're going to use to dial in to what app. You will have one, seamless way to dial into any application, public application, private application, or directly to the internet. >> Yeah, I got a love, hate with my VPN. I mean, it's protecting me, but it's in the way a lot. >> It's going to be simple as ever. >> Do you have kids? >> I do, I have a 12 year old daughter. >> Okay, so not quite high school age yet. She will be shortly. >> No, but she's already, I'm not looking forward to high school days, because she has a very, very strong sense of debate and she wins 90% of the arguments. >> So when my kids were that age, I've got four kids, but the local high school banned Wikipedia, they can't use Wikipedia for research. Many colleges, I presume high schools as well, they're banning Chat GPT, can't use it. Now at the same time, I saw recently on Medium a Wharton school professor said he's mandating Chat GPT to teach his students how to prompt in progressively more sophisticated prompts, because the future is interacting with machines. You know, they say in five years we're all going to be interacting in some way, shape, or form with AI. Maybe we already are. What's the intersection between AI and security? >> So a couple very, very consequential things. So firstly on Chat GPT, the next generation skill is going to be to learn how to go out and have the right questions to ask, which is the prompt revolution that we see going on right now. But if you think about what's happening in security, and there's a few areas which are, firstly 3,500 hundred vendors in this space. On average, most companies have 50 to 70 vendors in security. Not a single vendor owns more than 10% of the market. You take out a couple vendors, no one owns more than 5%. Highly fractured market. That's a problem. Because it's untenable for companies to go out and manage 70 policy engines. And going out and making sure that there's no contention. So as you move forward, one of the things that Chat GPT will be really good for is it's fundamentally going to change user experiences, for how software gets built. Because rather than it being point and click, it's going to be I'm going to provide an instruction and it's going to tell me what to do in natural language. Imagine Dave, when you joined a company if someone said, hey give Dave all the permissions that he needs as a direct report to Chuck. And instantly you would get all of the permissions. And it would actually show up in a screen that says, do you approve? And if you hit approve, you're done. The interfaces of the future will get more natural language kind of dominated. The other area that you'll see is the sophistication of attacks and the surface area of attacks is increasing quite exponentially. And we no longer can handle this with human scale. You have to handle it in machine scale. So detecting breaches, making sure that you can effectively and quickly respond in real time to the breaches, and remediate those breaches, is all going to happen through AI and machine learning. >> So, I agree. I mean, just like Amazon turned the data center into an API, I think we're now going to be interfacing with technology through human language. >> That's right. >> I mean I think it's a really interesting point you're making. Now, from a security standpoint as well, I mean, the state of the art today in my email is be careful, this person's outside your organization. I'm like, yeah I know. So it's a good warning sign, but it's really not automated in any way. So two part question. One is, can AI help? You know, with the phishing, obviously it can, but the bad guys have AI too. >> Yeah. >> And they're probably going to be smarter than I am about using it. >> Yeah, and by the way, Talos is our kind of threat detection and response >> Yes. >> kind of engine. And, they had a great kind of piece that came out recently where they talked about this, where Chat GPT, there is going to be more sophistication of the folks that are the bad actors, the adversaries in using Chat GPT to have more sophisticated phishing attacks. But today it's not something that is fundamentally something that we can't handle just yet. But you still need to do the basic hygiene. That's more important. Over time, what you will see is attacks will get more bespoke. And in order, they'll get more sophisticated. And, you will need to have better mechanisms to know that this was actually not a human being writing that to you, but it was actually a machine pretending to be a human being writing something to you. And that you'll have to be more clever about it. >> Oh interesting. >> And so, you will see attacks get more bespoke and we'll have to get smarter and smarter about it. >> The other thing I wanted to ask you before we close is you're right on. I mean you take the top security vendors and they got a single digit market share. And it's like it's untenable for organizations, just far too many tools. We have a partner at ETR, they do quarterly survey research and one of the things they do is survey emerging technology companies. And when we look at in the security sector just the number of emerging technology companies that are focused on cybersecurity is as many as there are out there already. And so, there's got to be consolidation. Maybe that's through M & A. I mean, what do you think happens? Are company's going to go out of business? There's going to be a lot of M & A? You've seen a lot of companies go private. You know, the big PE companies are sucking up all these security companies and may be ready to spit 'em out and go back public. How do you see the landscape? You guys are obviously an inquisitive company. What are your thoughts on that? >> I think there will be a little bit of everything. But the biggest change that you'll see is a shift that's going to happen with an integrated platform, rather than point solution vendors. So what's going to happen is the market's going to consolidate towards very few, less than a half a dozen, integrated platforms. We believe Cisco is going to be one. Microsoft will be one. There'll be others over there. But these, this platform will essentially be able to provide a unified kind of policy engine across a multitude of different services to protect multiple different entities within the organization. And, what we found is that platform will also be something that'll provide, through APIs, the ability for third parties to be able to get their technology incorporated in, and their telemetry ingested. So we certainly intend to do that. We don't believe, we are not arrogant enough to think that every single new innovation will be built by us. When there's someone else who has built that, we want to make sure that we can ingest that telemetry as well, because the real enemy is not the competitor. The real enemy is the adversary. And we all have to get together, so that we can keep humanity safe. >> Do you think there's been enough collaboration in the industry? I mean- >> Jeetu: Not nearly enough. >> We've seen companies, security companies try to monetize private data before, instead of maybe sharing it with competitors. And so I think the industry can do better there. >> Well I think the industry can do better. And we have this concept called the security poverty line. And the security poverty line is the companies that fall below the security poverty line don't have either the influence or the resources or the know how to keep themselves safe. And when they go unsafe, everyone else that communicates with them also gets that exposure. So it is in our collective interest for all of us to make sure that we come together. And, even if Palo Alto might be a competitor of ours, we want to make sure that we invite them to say, let's make sure that we can actually exchange telemetry between our companies. And we'll continue to do that with as many companies that are out there, because actually that's better for the market, that's better for the world. >> The enemy of the enemy is my friend, kind of thing. >> That's right. >> Now, as it relates to, because you're right. I mean I, I see companies coming up, oh, we do IOT security. I'm like, okay, but what about cloud security? Do you that too? Oh no, that's somebody else. But, so that's another stove pipe. >> That's a huge, huge advantage of coming with someone like Cisco. Because we actually have the entire spectrum, and the broadest portfolio in the industry of anyone else. From the user, to the device, to the network, to the applications, we provide the entire end-to-end story for security, which then has the least amount of cracks that you can actually go out and penetrate through. The biggest challenges that happen in security is you've got way too many policy engines with way too much contention between the policies from these different systems. And eventually there's a collision course. Whereas with us, you've actually got a broad portfolio that operates as one platform. >> We were talking about the cloud guys earlier. You mentioned Microsoft. They're obviously a big competitor in the security space. >> Jeetu: But also a great partner. >> So that's right. To my opinion, the cloud has been awesome as a first line of defense if you will. But the shared responsibility model it's different for each cloud, right? So, do you feel that those guys are working together or will work together to actually improve? 'Cause I don't see that yet. >> Yeah so if you think about, this is where we feel like we have a structural advantage in this, because what does a company like Cisco become in the future? I think as the world goes multicloud and hybrid cloud, what'll end up happening is there needs to be a way, today all the CSPs provide everything from storage to computer network, to security, in their own stack. If we can abstract networking and security above them, so that we can acquire and steer any and all traffic with our service providers and steer it to any of those CSPs, and make sure that the security policy transcends those clouds, you would actually be able to have the public cloud economics without the public cloud lock-in. >> That's what we call super cloud Jeetu. It's securing the super cloud. >> Yeah. >> Hey, thanks so much for coming to theCUBE. >> Thank you for having me. >> Really appreciate you coming on our editorial program. >> Such a pleasure. >> All right, great to see you again. >> Cheers. >> All right, keep it right there. Dave Vellante with David Nicholson and Lisa Martin. We'll be back, right after this short break from MWC '23 live, in the Fira, in Barcelona. (bright music resumes) (music fades out)

(introductory riff) >> Hey everyone. Welcome to theCUBE's presentation of the "AWS Startup Showcase." This is Season 2, Episode 3 of our ongoing series that features great partners in the massive AWS partner ecosystem. This series is focused on, "MarTech, Emerging Cloud-Scale Customer Experiences." I'm Lisa Martin, and I've got two guests here with me to talk about this. Please welcome Daisy Urfer, Cloud Alliance Sales Director at Algolia, and Jason Lang, the Head of Product for Apply Digital. These folks are here to talk with us today about how Algolia's Search and Discovery enables customers to create dynamic realtime user experiences for those oh so demanding customers. Daisy and Jason, it's great to have you on the program. >> Great to be here. >> Thanks for having us. >> Daisy, we're going to go ahead and start with you. Give the audience an overview of Algolia, what you guys do, when you were founded, what some of the gaps were in the market that your founders saw and fixed? >> Sure. It's actually a really fun story. We were founded in 2012. We are an API first SaaS solution for Search and Discovery, but our founders actually started off with a search tool for mobile platforms, so just for your phone and it quickly expanded, we recognize the need across the market. It's been a really fun place to grow the business. And we have 11,000 customers today and growing every day, with 30 billion searches a week. So we do a lot of business, it's fun. >> Lisa: 30 billion searches a week and I saw some great customer brands, Locost, NBC Universal, you mentioned over 11,000. Talk to me a little bit about some of the technologies, I see that you have a search product, you have a recommendation product. What are some of those key capabilities that the products deliver? 'Cause as we know, as users, when we're searching for something, we expect it to be incredibly fast. >> Sure. Yeah. What's fun about Algolia is we are actually the second largest search engine on the internet today to Google. So we are right below the guy who's made search of their verb. So we really provide an overall search strategy. We provide a dashboard for our end users so they can provide the best results to their customers and what their customers see. Customers want to see everything from Recommend, which is our recommended engine. So when you search for that dress, it shows you the frequently bought together shoes that match, things like that, to things like promoted items and what's missing in the search results. So we do that with a different algorithm today. Most in the industry rank and they'll stack what you would want to see. We do kind of a pair for pair ranking system. So we really compare what you're looking for and it gives a much better result. >> And that's incredibly critical for users these days who want results in milliseconds. Jason, you, Apply Digital as a partner of Algolia, talk to us about Apply Digital, what it is that you guys do, and then give us a little bit of insight on that partnership. >> Sure. So Apply Digital was originally founded in 2016 in Vancouver, Canada. And we have offices in Vancouver, Toronto, New York, LA, San Francisco, Mexico city, Sao Paulo and Amsterdam. And we are a digital experiences agency. So brands and companies, and startups, and all the way from startups to major global conglomerates who have this desire to truly create these amazing digital experiences, it could be a website, it could be an app, it could be a full blown marketing platform, just whatever it is. And they lack either the experience or the internal resources, or what have you, then they come to us. And and we are end-to-end, we strategy, design, product, development, all the way through the execution side. And to help us out, we partner with organizations like Algolia to offer certain solutions, like an Algolia's case, like search recommendation, things like that, to our various clients and customers who are like, "Hey, I want to create this experience and it's going to require search, or it's going to require some sort of recommendation." And we're like, "Well, we highly recommend that you use Algolia. They're a partner of ours, they've been absolutely amazing over the time that we've had the partnership. And that's what we do." And honestly, for digital experiences, search is the essence of the internet, it just is. So, I cannot think of a single digital experience that doesn't require some sort of search or recommendation engine attached to it. So, and Algolia has just knocked it out of the park with their experience, not only from a customer experience, but also from a development experience. So that's why they're just an amazing, amazing partner to have. >> Sounds like a great partnership. Daisy, let's point it back over to you. Talk about some of those main challenges, Jason alluded to them, that businesses are facing, whether it's e-commerce, SaaS, a startup or whatnot, where search and recommendations are concerned. 'Cause we all, I think I've had that experience, where we're searching for something, and Daisy, you were describing how the recommendation engine works. And when we are searching for something, if I've already bought a tent, don't show me more tent, show me things that would go with it. What are some of those main challenges that Algolia solution just eliminates? >> Sure. So I think, one of the main challenges we have to focus on is, most of our customers are fighting against the big guides out there that have hundreds of engineers on staff, custom building a search solution. And our consumers expect that response. You expect the same search response that you get when you're streaming video content looking for a movie, from your big retailer shopping experiences. So what we want to provide is the ability to deliver that result with much less work and hassle and have it all show up. And we do that by really focusing on the results that the customers need and what that view needs to look like. We see a lot of our customers just experiencing a huge loss in revenue by only providing basic search. And because as Jason put it, search is so fundamental to the internet, we all think it's easy, we all think it's just basic. And when you provide basic, you don't get the shoes with the dress, you get just the text response results back. And so we want to make sure that we're providing that back to our customers. What we see average is even, and everybody's going mobile. A lot of times I know I do all my shopping on my phone a lot of the time, and 40%-50% better relevancy results for our customers for mobile users. That's a huge impact to their use case. >> That is huge. And when we talked about patients wearing quite thin the last couple of years. But we have this expectation in our consumer lives and in our business lives if we're looking for SaaS or software, or whatnot, that we're going to be able to find what we want that's relevant to what we're looking for. And you mentioned revenue impact, customer churn, brand reputation, those are all things that if search isn't done well, to your point, Daisy, if it's done in a basic fashion, those are some of the things that customers are going to experience. Jason, talk to us about why Algolia, what was it specifically about that technology that really led Apply Digital to say, "This is the right partner to help eliminate some of those challenges that our customers could face?" >> Sure. So I'm in the product world. So I have the wonderful advantage of not worrying about how something's built, that is left, unfortunately, to the poor, poor engineers that have to work with us, mad scientist, product people, who are like, "I want, make it do this. I don't know how, but make it do this." And one of the big things is, with Algolia is the lift to implement is really, really light. Working closely with our engineering team, and even with our customers/users and everything like that, you kind of alluded to it a little earlier, it's like, at the end of the day, if it's bad search, it's bad search. It just is. It's terrible. And people's attention span can now be measured in nanoseconds, but they don't care how it works, they just want it to work. I push a button, I want something to happen, period. There's an entire universe that is behind that button, and that's what Algolia has really focused on, that universe behind that button. So there's two ways that we use them, on a web experience, there's the embedded Search widget, which is really, really easy to implement, documentation, and I cannot speak high enough about documentation, is amazing. And then from the web aspect, I'm sorry, from the mobile aspect, it's very API fort. And any type of API implementation where you can customize the UI, which obviously you can imagine our clients are like, "No we want to have our own front end. We want to have our own custom experience." We use Algolia as that engine. Again, the documentation and the light lift of implementation is huge. That is a massive, massive bonus for why we partnered with them. Before product, I was an engineer a very long time ago. I've seen bad documentation. And it's like, (Lisa laughing) "I don't know how to imple-- I don't know what this is. I don't know how to implement this, I don't even know what I'm looking at." But with Algolia and everything, it's so simple. And I know I can just hear the Apply Digital technology team, just grinding sometimes, "Why is a product guy saying that (mumbles)? He should do it." But it is, it just the lift, it's the documentation, it's the support. And it's a full blown partnership. And that's why we went with it, and that's what we tell our clients. It's like, listen, this is why we chose Algolia, because eventually this experience we're creating for them is theirs, ultimately it's theirs. And then they are going to have to pick it up after a certain amount of time once it's theirs. And having that transition of, "Look this is how easy it is to implement, here is all the documentation, here's all the support that you get." It just makes that transition from us to them beautifully seamless. >> And that's huge. We often talk about hard metrics, but ease of use, ease of implementation, the documentation, the support, those are all absolutely business critical for the organization who's implementing the software, the fastest time to value they can get, can be table stakes, and it can be on also a massive competitive differentiator. Daisy, I want to go back to you in terms of hard numbers. Algolia has a recent force or Total Economic Impact, or TEI study that really has some compelling stats. Can you share some of those insights with us? >> Yeah. Absolutely. I think that this is the one of the most fun numbers to share. We have a recent report that came out, it shared that there's a 382% Return on Investment across three years by implementing Algolia. So that's increase to revenue, increased conversion rate, increased time on your site, 382% Return on Investment for the purchase. So we know our pricing's right, we know we're providing for our customers. We know that we're giving them the results that we need. I've been in the search industry for long enough to know that those are some amazing stats, and I'm really proud to work for them and be behind them. >> That can be transformative for a business. I think we've all had that experience of trying to search on a website and not finding anything of relevance. And sometimes I scratch my head, "Why is this experience still like this? If I could churn, I would." So having that ability to easily implement, have the documentation that makes sense, and get such high ROI in a short time period is hugely differentiated for businesses. And I think we all know, as Jason said, we measure response time in nanoseconds, that's how much patience and tolerance we all have on the business side, on the consumer side. So having that, not just this fast search, but the contextual search is table stakes for organizations these days. I'd love for you guys, and on either one of you can take this, to share a customer example or two, that really shows the value of the Algolia product, and then also maybe the partnership. >> So I'll go. We have a couple of partners in two vastly different industries, but both use Algolia as a solution for search. One of them is a, best way to put this, multinational biotech health company that has this-- We built for them this internal portal for all of their healthcare practitioners, their HCPs, so that they could access information, data, reports, wikis, the whole thing. And it's basically, almost their version of Wikipedia, but it's all internal, and you can imagine the level of of data security that it has to be, because this is biotech and healthcare. So we implemented Algolia as an internal search engine for them. And the three main reasons why we recommended Algolia, and we implemented Algolia was one, HIPAA compliance. That's the first one, it's like, if that's a no, we're not playing. So HIPAA compliance, again, the ease of search, the whole contextual search, and then the recommendations and things like that. It was a true, it didn't-- It wasn't just like a a halfhearted implementation of an internal search engine to look for files thing, it is a full blown search engine, specifically for the data that they want. And I think we're averaging, if I remember the numbers correctly, it's north of 200,000 searches a month, just on this internal portal specifically for their employees in their company. And it's amazing, it's absolutely amazing. And then conversely, we work with a pretty high level adventure clothing brand, standard, traditional e-commerce, stable mobile application, Lisa, what you were saying earlier. It's like, "I buy everything on my phone," thing. And so that's what we did. We built and we support their mobile application. And they wanted to use for search, they wanted to do a couple of things which was really interesting. They wanted do traditional search, search catalog, search skews, recommendations, so forth and so on, but they also wanted to do a store finder, which was kind of interesting. So, we'd said, all right, we're going to be implementing Algolia because the lift is going to be so much easier than trying to do everything like that. And we did, and they're using it, and massively successful. They are so happy with it, where it's like, they've got this really contextual experience where it's like, I'm looking for a store near me. "Hey, I've been looking for these items. You know, I've been looking for this puffy vest, and I'm looking for a store near me." It's like, "Well, there's a store near me but it doesn't have it, but there's a store closer to me and it does have it." And all of that wraps around what it is. And all of it was, again, using Algolia, because like I said earlier, it's like, if I'm searching for something, I want it to be correct. And I don't just want it to be correct, I want it to be relevant. >> Lisa: Yes. >> And I want it to feel personalized. >> Yes. >> I'm asking to find something, give me something that I am looking for. So yeah. >> Yeah. That personalization and that relevance is critical. I keep saying that word "critical," I'm overusing it, but it is, we have that expectation that whether it's an internal portal, as you talked about Jason, or it's an adventure clothing brand, or a grocery store, or an e-commerce site, that what they're going to be showing me is exactly what I'm looking for, that magic behind there that's almost border lines on creepy, but we want it. We want it to be able to make our lives easier whether we are on the consumer side, whether we on the business side. And I do wonder what the Go To Market is. Daisy, can you talk a little bit about, where do customers go that are saying, "Oh, I need to Algolia, and I want to be able to do that." Now, what's the GTM between both of these companies? >> So where to find us, you can find us on AWS Marketplace which another favorite place. You can quickly click through and find, but you can connect us through Apply Digital as well. I think, we try to be pretty available and meet our customers where they are. So we're open to any options, and we love exploring with them. I think, what is fun and I'd love to talk about as well, in the customer cases, is not just the e-commerce space, but also the content space. We have a lot of content customers, things about news, organizations, things like that. And since that's a struggle to deliver results on, it's really a challenge. And also you want it to be relevant, so up-to-date content. So it's not just about e-commerce, it's about all of your solution overall, but we hope that you'll find us on AWS Marketplace or anywhere else. >> Got it. And that's a great point, that it's not just e-commerce, it's content. And that's really critical for some industry, businesses across industries. Jason and Daisy, thank you so much for joining me talking about Algolia, Apply Digital, what you guys are doing together, and the huge impact that you're making to the customer user experience that we all appreciate and know, and come to expect these days is going to be awesome. We appreciate your insights. >> Thank you. >> Thank you >> For Daisy and Jason, I'm Lisa Martin. You're watching "theCUBE," our "AWS Startup Showcase, MarTech Emerging Cloud-Scale Customer Experiences." Keep it right here on "theCUBE" for more great content. We're the leader in live tech coverage. (ending riff)

(upbeat music) >> Welcome to this CUBE Conversation. This is part of the second season of the AWS startup showcase, season two, episode one. I'm Dave Nicholson, and I am joined with a very special guest, CEO and co-founder of Tidelift, Mr. Donald Fischer. Donald, welcome to the CUBE. >> Thanks David. Really glad to be here. >> So, first and foremost, tell us about Tidelift. >> Happy to, yeah, so, at Tidelift we're on a mission. Our mission is to make open source software work better for everyone, and when we say that, we mean, make it work better for all the organizations and governments and everybody that depends on open source software to build the applications that we all rely on. But also part of our mission, is making open source work better for the creators of open source. The independent open source maintainers, who are behind so many of those building blocks, technology building blocks that our commerce industry and society is comprised of these days. They've got a hard task to hold up all of that stuff and make sure that it meets, you know, professional grade standards and that we can all rely on it. And so, we want to do our part to help both sides of that equation. >> Fantastic, well, I want to double click on a few of the things that you said, but I think I want to format this by starting out with a little role play between the two of us, if you don't mind. I know you're CEO, but for the sake of this, you're going to be the CIO and I'm going to be the CEO, and we're going to play off some recent events here. So, hey Donald, come on in, sit down. Listen, I want to talk to you about this whole log shell, log for something, or another thing that's going on. So, let me get this straight. Our multinational Fortune 500 company is dependent upon software, that's free, and somehow we've been running this and the people who maintain it, do it for free, we don't pay for it, but somehow this has opened us up to a threat from people who can log into a system we're using to keep track of stuff, and then, what's going on? By the way, you're fired, but I want to know if, I want to know if you can stay on for the next 90 days to train your replacement, but, explain to me what's going on with this whole open-source nonsense? >> Yeah. Don't panic boss. Only about 70 or 80% of the software in our enterprise that is third-party open source software. So, there's definitely, like 20 or 30% that's not, and we're on top of it. Now, yeah, I think it's a, you know, you're right to say, we are completely dependent on this software, that's being created by these, you know, amazing folks on the internet. Boss, you told me that we had to have a global corporation here with modern digital customer experience. We're not going to be able to do it using Microsoft front page from 1997, and there's no other path to take than to build with modern building blocks. And today in, you know, the modern era, that means building on open source packages and technologies across a whole slew of language, ecosystems, like JavaScript and Java PHP, Ruby, Python, .NET, Rust, Go, we use all of it here, boss, and, we don't get to have a business unless we do. >> Okay, so, I didn't understand a word that you just said, but it was enough to convince me to let you keep your job. So, end-scene, we're not getting paid scale wages to do this, Donald, so I think we can go back to our normal personas. So, how does Tidelift play into all of this? I'd really want to hear about this concept of what an open source maintainer is, because these are largely volunteers, aren't they, in terms of the maintenance that they're doing? >> Yeah, so, I mean, open source, there's a lot of different models for open source software development. There certainly are a number of foundational open source projects, certainly at the infrastructure level, like operating systems, databases and things like that, that tend to be, you know, predominantly driven by vendors, software vendors, you know, like you can think of Red Hat, VMware organizations like that. But when you get up to the application development world, teams, building, you know, websites, web applications, mobile applications, most of the building blocks at that tier in these a programming language ecosystems, most of the software there is actually being created, that enterprise organizations use, is being created by individual, independent, open source maintainers, where it's not their day job, it's a side hustle for them. And it's a really interesting question, like, how did we get here? You know, why are these folks doing it? It sort of rhymes with the question I asked myself years ago, like, who's typing all this stuff into Wikipedia, and why? Like, it's amazing resource, I'm so glad it's there, but why are they doing this, right? And it turns out that there's a bunch of motivations there's some cynical motivations for the open source maintainers that people attribute that are practical too, you know, people say your GitHub repository is your resume in as a modern developer, things like that helps you get a reputation, you can use that to get a job. But, when we've talked to the maintainers of the most widely used open source packages, and by that, I mean, thousands of packages that every major organization that builds software relies on, the main reason why they do it is actually impact. We find we've actually done direct surveys of this audience and the reason why they spend their nights and weekends and carve out time, where they could be, you know, getting paid to do something else or going skiing or going to the beach, is it really feels good to have this activity that they put out into the world, and, you know, they know that folks use this stuff and rely on it, and there's a pride in their work and the impact that they're making. But the challenge with this model is that when it's only an impact and pride, and sort of a, you know, a good feeling driven effort, it means that maybe all of the things that organizations might want their standards that organizations might want their software to meet doesn't get done, right? Like it's one thing, if you've got a job as a software engineer, building corporate software, or even as a, you know, a maintainer at a corporate open source company, and you have a checklist of, you know, standard enterprise software development, commercial grade software development tasks that you need to be completing, if you're doing it as a side hustle for good reasons, like impact and, you know, releasing your creative juice, you might not get to some of the more boring aspects of commercial software engineering, like security engineering and some of the documentation and release engineering and, you know, making sure there's structured metadata around all the elements of it. And then that's the gap that we're really trying to fill at Tidelift, by connecting these two audiences. >> Yeah. How? How? You want to fill the gap, you want to connect the audiences, but, how do you do that? >> Yeah, perfect, so, we do it by paying the maintainers, paying the open source maintainers, actual dollars, or the currency of their preference, and what we're paying them for is not just to sort of hack on their projects, or hack on their projects more, we're asking them to help us ensure that the software that the organizations that we work with depend on meets certain specific concrete enterprise standards, and those standards fall into three categories, security, licensing, and maintenance. So, on the security front, you know, a baseline standard, there is making sure that we have known versions of the open source packages that are free of known defects, right? So there's like a catalog of known security defects that the industry uses called the National Vulnerability Database, you may have seen the terminology CVE referred to in passing, that's the identifier for these things. So, we work with the open-source maintainers to make sure that we've figured out, mapped out, which versions of software packages are impacted by known security vulnerabilities. And then we also look forward and make sure that we have a plan in place for what happens in the future when there are security vulnerabilities. So, you know, traditional commercial software, there's a security response team, who's kind of standing by 24/7, ready to respond, and then there's a defined protocol of what's going to happen, in terms of what's called responsible disclosure, telling the right folks in the right sequence, that there is a vulnerability causing there to be a patch version of the software available, communicating that through, you know, traditional commercial software vendors for, you know, years have been doing that internally, that doesn't exist by default for volunteer, you know, part-time open source, independent open source maintainers. So we fill that gap and we pre-wire that with them to make sure that that first track security is can be buttoned up. >> So, you're paying them, are you and your co-founders wealthy philanthropists that are just doing this, or what's the business model here? Now you're pulling these people who were doing it for free, they're happy, but how does that translate into a business model for Tidelift. >> Perfect, so, the work that they're doing, you know, I talked a little bit about security, we also do similar things on those other attributes, like licensing, making sure that the licenses are completely accurate, and we kind of know who wrote the software, et cetera, and then maintenance, is it being proactively cared for going forward? Is somebody still on the case with these projects? Now, the result of all of that work, is we create a vetted catalog of known good open source releases that we've vetted with the experts, often the individuals and teams that wrote the code in the first place, usually, we vet that it meets these enterprise standards. That's a really useful tool for organizations that are building with that. So, the way that we convey that to organizations that are building software in a useful way is we have a SAS service software, that as a service platform, that's what Tidelift is, and basically, the teams that use this stuff, they plug us into their software development process, typically alongside other tools that they might have, like CI/CD tools that are running tests on their application logic, they'll plug in Tidelift into their release process to ensure that those, the 70 or 80% of the software that they ship, that comes from GitHub, comes from the Python package index, or NPM, or the Maven Central Repository for Java, we're vetting that that meets their enterprise standards and ensuring that the ingredients, the building blocks that go into their applications are known good and vetted to these concrete standards. And they are, you know, this is an unsolved problem for almost every serious organization. There's a couple of, you know, over-performing organizations, like Google has done some amazing internal work on this, Amazon has an incredible dedicated team that does this internally for Amazon developers, very few other organizations, even some of the largest multinational companies have a dedicated internal function doing this comprehensively and systematically. Tidelift is that function that these organizations can use. They can work with us and our network, our unique network of hundreds of these independent open source maintainers, to ensure that there is a feed of known good vetted packages to go into their applications. >> So, were maintainers going in and auditing, and editing, and vetting software that was essentially created by others? That's one question, and then the other question that kind of goes along with that is, are you vetting a gold copy of something and saying, this software meets certain criteria, you should feel okay using it, that's one thing. Validating that the actual distribution, you know, the actual code that's being executed in their enterprise is secure and hasn't been tampered with is another thing. So where do you sit in that distribution channel or that supply chain? >> Sure, so, on the distribution front, you can think of us, we're sort of a GPS system that your application developers can use to know which versions of software are going to meet your enterprise standards. We don't create a separate world where we have our own, you know, side copy of the entire development ecosystem. It's not what these organizations want. They don't want to use some weird enterprise world set of open source packages, they want to just, you know, type NPM install have the, you know, software flow into their organization, but they also want it to not have no insecurity vulnerabilities in it, and they don't want to get bitten two weeks or two years later with a license violation, because there was kind of fuzzy, or incomplete data around the open source license. So what we do is, we help them consume the open source software, you know, knowing that it's been vetted to these standards. And then we also work with the open source community to cause the software to be changed to meet those standards, right? So back to the first part of your question, We work with a lot of projects with the prime maintainers, often the authors, as I said, and we've actually been extending our model over the years to work with these open source maintainers to cover not just their own project, but, some of those neighboring projects, right? Like the core projects that their project depends on, other projects that are co-used with them, they have a lot of expertise, and also, you know, relationships with the surrounding open source community there. So, they're working with us as curators, if you will, our ambassadors that help us get on the community and cover as much of the landscape as possible. >> And, so, what's the relationship with AWS? This is, you know, we're talking here as part of the AWS startup showcase season two, episode one, which is, that's actually pretty cool. So we need to, you know, the challenge here is, season one was awesome, much like Ted Lasso, season two, we have big shoes to fill here, Donald. So, what's the-- >> We got to up our game. >> (laughs) What's the relationship with AWS? And, I mean, why would they call you out as someone interesting for us to talk to? >> Yeah, so, we've had a great relationship that we've been investing in, and working on together with AWS. So, every one of AWS's customers faces this challenge around the software workloads that they're deploying on AWS. You know, it's just, you can't argue against the fact that the vast majority of the application software in the modern world is comprised majority of this third-party open source software. And so, it's really important whether it's running on a device, you know, an Edge device, or whether it's running in a Cloud data center, that those applications meet these standards, especially on the security front. So, AWS recognizes this need and opportunity for their customers, and so we've been working really well jointly with them. We're glad to say that we're an ISV, and AWS ISV accelerate partner now, which gives us the ability to co-engage with AWS and work together to solve mutual customers challenges, and we've had a great time working with the AWS team to help scale up our efforts to get the word word out around this important area, and then more importantly, give organizations the tools to address it and make sure that they have a comprehensive strategy for managing their open source in place. >> Fantastic, Donald, we're up against time, but I do have a 10 second answer I'd like from you. Tidelift, is that a reference to a rising tide lifting all boats, or is it an admonishment not to build a house on the beach in Malibu? >> It's the former, you know, think about this network of independent open source maintainers, working together, a rising tide lifts all boats. >> Eight seconds, that was like four seconds. Perfect. Donald Fischer, from Tidelift, thank you so much. For me, Dave Nicholson here at the CUBE. This has been a CUBE Conversation, as part of AWS's startup showcase, season two, episode one. Come to the CUBE for the best in tech coverage. (soft music)

(melodious music) >> Voiceover: From around the globe. It's theCUBE with digital coverage of IBM Think 2021. Brought to you by IBM. >> Hi and welcome back to theCUBE's coverage of IBM Think 2021 virtual. I'm John Furrier, host of theCUBE. We're a virtual this year. But we'll be in real life soon, right around the corner, as we come out of COVID. We got a great guest, a CUBE alumni, Jerry Cuomo, IBM Fellow VP, CTO for IBM automation. Jerry, great to see you. Been on since almost since the early days of theCUBE. Good to see you >> Yeah, John. Thrilled to be back again. Thank you. >> What I love about our conversations, one is you're super technical. You've got patents under your belt. You're on the cutting edge. You've been involved in web services and web technologies for a long, long time. You're constantly riding the wave. And also you're a creator of a great podcast called "The Art of Automation", which is the subject of this discussion. As automation becomes central in cloud operations and Hybrid Cloud, which is the main theme of this event this year and the industry. So great to see you. First give us a little background for the folks that may not know you, about your history with IBM and who you are. >> Yeah. So thanks John. So I'm Jerry Cuomo. I've been with IBM for about three decades and I started my career at IBM research in Yorktown at the dawn of the internet. And I've been incredibly fortunate as you mentioned, to be on the forefront of many technology trends over the last three decades, internet software, middleware, including being one of the founding fathers of WebSphere software. I recently helped launch the IBM blockchain initiative and now all about AI powered automation. Which actually brings me back to my roots of studying AI in graduate school. So it's kind of come full circle for me, really enjoying the topic. >> It's funny you mentioned AI in graduate school. I was really kind of into AI when I was an undergraduate and get a master's degree in Computer Science. I kind of went the MBA route. But if you think about what was going on in the eighties during those systems times, a lot of the concepts of systems programming and cloud operations kind of gel well together. So you got this confluence of computer science and engineering AKA now DevOps, DevSecOps, coming together. This is actually a really unique time to bring back the best of the best concepts. Whether it's AI and systems and computer science and engineering into automation. Could you share your view on this? Because you're in a unique position, you've been there done that, now you're on the cutting edge. What's your thoughts? >> Yeah, absolutely John. And just when you think of automation and time, automation is not new. That literally, if you go into Wikipedia and you look up automation, you see patents and references to like steam engine regulators at the dawn of the industrial era, right? So automation has been around and in its simplest form, automation whether it was back then, whether it was in the eighties or today it's about applying technology that uses technology software to perform tasks that were once exclusively done by us, humans. But now what we're seeing is AI coming into the picture and changing the landscape in an interesting way. But I think at its essence, automation is this two-step dance of both eliminating repetitive mundane tasks that help reduce errors and free up our time. So we get back the gift of time, but also helps. It's not about taking jobs away at that point, it's a sentence of two-step dance. That's step one. But if you stop there, you're not getting the full value. Step two is to augment our skills. And to use automation, to help augment our skills. And we get speed, we get quality, we get lower costs, we get improved user experience. So whether it was back in the steam engine times or today with AI, automation is evolving with technology. >> And it's interesting too, as a student of the history of the computer industry, as you are and now a creator with your podcast, which we'll get to in a second. You're starting to see the intersection of these concerts and not bespoke as much as they used to be. You got transformation, digital transformation and innovation are connected and scale. If you think about those three concepts, they don't stand alone anymore. They can stand alone, but they work better together. Transformation. And it is the innovation, innovation provides cloud scale. So if you think about automation, automation is powering this dynamic of taking all that undifferentiated heavy lifting and moving the creativity and the skillset into higher integrated areas. Can you share your thoughts? >> Yeah, no, right on there. When you talk about transformation, jeez, look around us. The pandemic has made, transformation and specifically digital transformation, the default. So everything is digital. Whether it's ordering a pizza, visiting a doctor through telemedicine, or this zoom WebEx based workplace that we live in. But picture a telemedicine environment, talking about transformation and going digital. With 10 X more users, they can't hire 10 X more support staff. And think about it. I forgot my password, does this work on my version of the Apple iPhone or all of that kind of stuff? So their support desks are lit up, right? So as they scale digitally, automation is the relief that that comes into play, which is just in time. So the digital transformation needs automation. And John, I think about it like this, businesses like cars have become computers. So they're programmable. So automation software just like in the cars, it makes the car self-driving. I think about the Tesla model three, which I recently test drove. So with this digital acceleration, digital opens the door for automation. And now we can muse about self-driving business. We can muse about maybe that's step one, right? That's the remove repetitive work, but maybe we can actually augment business to have an autopilot. So it doesn't need us there all the time to drive. And that's the scale that you talked about. That's the scale we need. So automation is really like the peanut butter and chocolate. Digital is the peanut butter automation is the chocolate. They go well together and they produce amazing tastes. >> Yeah. That's a really interesting insight. And I was just put an exclamation point on that because you mentioned self-driving business you're implying, you said the business is a computer. So if you just think about that mind blowing concept for a second. If it's a computer, what's the operating system and what's the suite of applications that are on top of it? So, okay. Let's go in the old days you had a windows machine and you had office, which was a system software, applications software construct. If you map that to the entire company, you're talking about Red Hat and IBM will kind of come working together. Kind of connects the dots a little bit on what Red Hat could, because they're not breaking system company. So if Hybrid Cloud is the system, edge, hybrid, then you got the application suite is all software for the business. >> That's right. That's right. And if you listen to anything these days about what IBM stands for, it's Hybrid Cloud and think Red Hat as kind of the core element of that with OpenShift and AI. And both of those really matter in terms of automation and maybe I'll come back to the Hybrid Cloud or Red Hat thing in a second, but let's just talk about Watson and AI, which is the application. You mentioned scale, which I'm so glad you did. AI could help scale automation. And the trick is, is that automation sometimes gets stuck. It gets stuck when it's working with data that is noisy or unstructured. So there's a lot of structured data in your organization. With that, we can breeze through automation. But if there is more ambiguous data, unstructured, noisy, you need a human in the loop. And when you get a human in the loop, it slows things down. So what AI can start to do, AI and its subordinates, machine learning, natural language processing, computer vision. We can start to make sense of both unstructured and structured data together and we can make a big deal going forward. So that's the AI part. You mentioned Red Hat and, and Hybrid Cloud Park. Well, think about it this way, when you shop, how many stores do you... You don't just shop in one store, right? You go to specialty stores to pick up that special catsup, I don't know or mustard. (audio cuts) In one store and maybe do shopping in another store. Customers using clouds John, aren't very different. They have their specialty places to go. Maybe they're going to be running workloads in Google, involving search and AI related to search. And they're going to be using other clouds for more specialty things. From that perspective, that's a view of hybrid. Customers today, take that shopping analogy. They're going to be using Salesforce or Servicenow, IBM cloud, they have a private cloud. So when you think about automating that world, it's the real world. It's how we shop, whether it's for groceries or for cloud. The Hybrid Cloud is a reality. And how do you make sense of that? Because when an average customer has five clouds, how do you deal with five things? How do you make it easy, normalize? And that's what Red hat really does. >> It's interesting. I'll just share with you though. When I interviewed Arvin, who is now the CEO of IBM when he was at Red Hat summit 2019 in San Francisco. Before he made the acquisition, I was peppering him with questions. Like, you need to get this cloud and he loves cloud, you know, he loves cloud. So he was smiling. He just wanted to say it, he wanted to just say it. And I think Red hat brings that operating kind of mindset where the clouds are just subsystems in the OS of the middleware, which is now software which is software defined business. And this kind of is the talk of your views. Now you have a podcast called "Art of Automation". We want to get that in there, for the folks watching. Search for the podcast, "Art of Automation". This is the stories that you tell. Tell us some stories, from this phenomena. What's the impact of automation for the holistic picture? >> Well, it starts with a lot of, I guess it starts with customers. The stories start with the customer. So we're hearing from customers that AI and automation is where they're investing in 2021. For all the reasons we briefly mentioned, and IBM has a lot to offer there. So we've made AI powered automation a priority. But John, in the pursuit of making it a priority, I've started talking with many of our subject matter experts and was floored by their knowledge, their energy, their passion, and their stories. And I said, we can't keep this to ourselves. We can't keep this locked away. We have to share it. We have to let it out. So basically this is what started the podcast around that. And since then, we've had many industry luminaries from IBM and outside. Starting with customers. We had Klaus Jensen who is the CIO of Memorial Clones Kettering Hospital to talk about automation in healthcare. And he shared great stories. You need to listen to them, about automation is not going to take the place of doctors. But automation will help better read x-rays and look at those shades of gray on the x-ray and interpret it much better than we can. And be able to ingest all of the up-to-date medical research to provide pointers and make connections that the human may not be able to do in that moment. So the two working together are better than any individual. Carol Polson recently joined me to talk, and she's the CIO for Cooperators, to talk about automation and insurance. And she had some great stories too. So John, with that, a bunch of IBM, great IBM fellows like Rama Akkiraju, who is one of Forbes top 20 women in AI research. Talking about AI ops. And also Ruchir Puri talking, and Ruchir has been working on Watson since jeopardy to tell stories about ultimately now how we're teaching AI to code and all the modern programming languages. And really automating application modernization and the like. Four keyed episodes in, we have those under our belt. About 6,000 downloads so far. So it's coming along pretty well. Thanks for asking, John. >> The key is you're a content creator now, as well as a fellow. And this is the democratization, as we say direct to audience, share those stories. Also here, I think you released an ebook. Tell us a little quickly about that. We've got one minute left, give a quick plug for the ebook. >> The book echoes the podcast. Chapters relate to the episodes of the book. We're dropping the first five chapters plus forward for free on the IBM website. Other chapters will become available and drop as they become available. The book makes the content searchable on the internet. We go into more detail with advice on how to get started. You get to hear the topics and the voice of those subject matter experts. And I really suggest you go out and check it out. >> All right, Jerry Cuomo. IBM fellow VP, CTO, IBM automation. Also a content creator, podcast "Art of Automation." Jerry, we're going to list it out on our Silicon angle and our cube sites, gets you some extra love on that. Love the podcast, love the focus on sharing from experts in the field. Thanks for coming on. >> Yeah. Thank you so much for having me again, John. >> Okay, I'm John Furrier with theCUBE. Here for IBM Think 2021. Thanks for watching.

>>from around the globe. It's the >>cube >>With digital coverage of IBM think 2021 brought to you by IBM. Hello and welcome back to the cubes coverage of IBM Think 2021 virtual. I'm john for a year host of the cube. We're virtual this year in real life. Soon, right around the corner as we come out of code, we've got a great guest cube alumni jerry, cuomo IBM fellow V P C T O for IBM automation jerry, Great to see you uh nonsense got almost since the early days of the cube. Good to see you, >>john thrilled to be back again. Thank you >>what I love about our conversations. One is your super technical, you've got patents under your belt during the cutting edge. You've been involved in web services and web technologies for a long, long time. You constantly riding the wave and also your creator of a great podcast called the art of automation, which is the subject of this discussion as automation becomes central in cloud operations and hybrid cloud, which is the main theme of this event this year and the industry so great to see you. Uh First team is a little background for the folks that may not know you about your history with IBM and who you are. >>Yeah, so thanks john, So I'm I'm jerry Carrillo, I've been with IBM for about three decades and I started my career at IBM research in Yorktown at the dawn of the internet and I've been incredibly fortunate, as you mentioned to be on the forefront of many technology trends over the last three decades. Internet software middleware, including being one of the founding fathers of web sphere software, uh I recently helped launch the IBM Blockchain initiative and now all about aI powered automation, which actually brings me back to my roots of studying AI and graduate school. So it's kind of come full circle for me, you know, really you know, enjoying the topic. >>You know, these funny, you mentioned aI in graduate school, I was really kind of into a I when I was an undergraduate and get a masters degree in computer science, I kind of went the NBA route. But if you think about what was going on in the eighties during those systems times, a lot of the concepts of systems programming and cloud operations kind of gel well together. So you've got this confluence of computer science and engineering A. K A. Now devops sec cops coming together. This is actually a really unique time to bring back the best of the best concepts, whether it's A I and systems and computer science and engineering into the automation. Could you share your view on this because you're in a unique position, you've been there, done that now. You're on the cutting edge with your thoughts. >>Yeah, absolutely, john And just when you think of automation and time, automation is not new, literally, if you go into Wikipedia and you look up automation, you see patents and references to like steam engine regulators at the dawn of the industrial era. Right? So automation has been around and and in the simplest form automation, whether it was back then, um whether it was in the 80s or today, it's about applying technology and that that performs, that uses like technology software to perform tasks that were once exclusively done by us humans. Right? So, but now what we're seeing is a I coming into the picture and and changing the landscape in an interesting way. But I think at its essence, you know, automation is this two step dance of both eliminating repetitive, mundane tasks. That helped reduce errors and free up our time. So we get back the gift of time but also helps. It's not about taking jobs away at that point, as I said, it's a two step dance, that's step one. But if you stop there, you're not getting the full value. Step two is to augment our skills right? And and to use automation to help augment our skills and we get speed, we get quality, we get lower costs, we get improved user experience. So whether it was back in the steam engine times or today with a I automation is evolving with technology >>and it's interesting to its you know, as a student of the history of the computer industry as you are and now a creator with your podcast which we'll get to in a second, you're starting to see the intersection of these concepts are not bespoke as much as they used to be. You got transformation. Digital, transformation and innovation are connected and scale. If you think about those three concepts they don't stand alone anymore. They can stand alone but they work better together transformation. And is the innovation innovation provides cloud scale. So if you think about automation, automation is powering this dynamic of taking all that undifferentiated heavy lifting and moving the creativity and the skill set into higher integrated areas. Can you share >>your john Yeah no right on there when you talk about transformation, jeez look around us, the pandemic has made transformation and specifically digital transformation the default so everything is digital. You know whether it's ordering a pizza, you know visiting a doctor through telemedicine or or this zoom webex based workplace that we live in. But picture of telemedicine environment right? Talking about transformation and going digital With 10 x more users. They can't hire 10 x more support staff and think about it I forgot my password. Um Does does this work on my version of the Apple iphone or all of that kind of stuff? So their support desks or lit up? Right. So uh as they scale digitally automation is the relief that that comes into play which is which is just in time. Right? So the digital transformation needs automation and john I think about it like this um businesses like cars are have become computers right? So they are programmable. So automation software just like in the cars it makes you know the car self driving? I think about the Tesla model three which I recently test drove. Um so with this digital acceleration digital opens the door for automation And now we can use about a self driving business. We can use about uh maybe that's step one, right? That's the um remove repetitive work, but maybe we can actually augment business to have an autopilot so it doesn't eat us there all the time to drive. And that's the scale that you talked about. That's the scale we need. So automation is really like the peanut butter and chocolate Digital is the peanut butter, automation is the chocolate. They go well together and they produce amazing tastes. >>You know, that's a really, that's a really interesting insight and I will just put an exclamation point on that because you mentioned self driving business, you're implying, you said the computer, the business is a computer. So if you just just think about that mind blowing concept for a second, if it's a computer, what's the operating system and what's the suite of applications that are on top of it? So, Okay, let's go in the old days at a Windows machine and you had office, which is a system software, applications, software construct. Okay, If you map that to the entire company, you're talking about Red hat and IBM kind of come working together. Kind of connects the dots a little bit on what Red Hackett because they're not bring system company. So if hybrid cloud is the system mm hybrid, then you got the applications suite is all software for the That's >>right. That's right. And if you, you know, if you listen to anything these days about what IBM stands words, hybrid cloud and and think red hat as as, you know, kind of the core element of that with open shift in a I right. And both of those really matter in terms of automation and maybe I'll come back to the hybrid cloud and red hat thing in a second. Let's just talk about you know Watson and Ai, you know, which is the application and you mentioned scale, which I'm so glad you did. You know a I could help scale automation. And the trick is is that ai automation sometimes gets stuck right? It gets stuck when it's working with data that is noisy or unstructured. Right? So there's a lot of structured data in your organization and it it with that we can breeze through automation. But if there is more ambiguous data unstructured noisy, you need a human in the loop. And when you get a human in the loop, it slows things down. So what a I can start to do a I. And its subordinates, machine learning, natural language processing, computer vision. We can start to make sense of both unstructured and structured data together and we can make a big deal going forward. Right? So that's that's the way I part you mentioned Red hat and and hybrid cloud part. We'll think about it this way. When you shop, how many stores do you don't just shop in one store? Right. You you go to specialty stores to pick up that special uh ketchup, I don't know or must store and maybe do shopping another store, customers using clouds john aren't very different. You know, they have their specialty places to go. Maybe they're going to be running workloads and google involving search and a I related to search, right? And they're going to be using other clouds for more specialty things. Right? So from that perspective, that's a view of hybrid, you know, customers today, you know, take that shopping analogy, they're going to be using sales force or service Now, IBM cloud, they have a private cloud, right? So, when you think about automating that world, All right. It's the real world. It's how we shop, whether it's for groceries or for cloud, right? So the hybrid cloud is a reality. Um and how do you make sense of a high of that? Right, Because when when an average customer has five clouds, How do you deal with five things? Right. How do you make it easy normalize? And that's what red hat really >>does. It's interesting. I just just share with you the and I interviewed Arvin um who is now the ceo of IBM when he was at Red Hat some in 2019 in SAn Francisco before he made the acquisition here that I was, I was peppered with questions like you know, you need to get this cloud and he loves cloud, you know, he loves clouds. So so he was smiling, he just wanted to say it, I wanted to just say it and I think Red Hat brings that operating kind of mindset where the clouds are just subsystems in the Os >>yes of the middle >>where which is now software which is software to find business. And this kind of is the talk of your, your your views. Now you have a podcast called Art of automation. Want to get that in there for the folks watching uh search for the podcast, Art of automation. This is the stories that you tell. Tell us some stories from this phenomenon. What's the impact of automation for the holistic picture? >>Well, it starts with a lot of, I guess it starts with customers. The stories start with the customers. So we're hearing from customers that Ai and automation is where they're investing in 2021. Um for all the reasons we briefly mentioned and and IBM has a lot to offer there. So we've made a I powered automation of priority but john in the pursuit of making it a priority. I've started talking with many of our subject matter experts and was floored by their knowledge, their energy, their passion and their stories. And I said we can't keep this to ourselves, we can't keep this locked away, we have to share it, we have to let it out. So, so basically this is what started the podcast around that. And since then we've had many industry luminaries from IBM and outside, starting with customers, we had claus Jensen who is the ceo of memorial clones Kettering hospital to talk about automation and health care. And he shared great stories. You need to listen to them about. Automation is not going to take the place of doctors, but automation will help better read um x rays and look at those shades of gray on the X ray and interpret it much better than we can and be able to ingest all of the up to date medical research to provide pointers and make connections that the human may not be able to do in that moment. Right. So the two working together or better than any individual carol Poulsen recently joined me to talk and she's the C. I. O. For cooperators to talk about automation and insurance. And she had some great stories to uh so john with that a bunch of IBM great IBM fellows like Rama Agra jew, who is one of Forbes top 20 women in AI research, talking about Ai ops and also Russia near pori talking and Russia has been working on Watson since jeopardy to tell stories about ultimately now how we're teaching ai to code and all the modern programming languages and really automating application modernization and the like uh, 14 episodes in we have those under our belt, About 6000 downloads so far. So it's it's coming along pretty well. Thanks. >>Thanks for being done. Yeah. The key is your your content creator now as well as a fellow and this is the democratization, as we say, direct to audience, share those stories also here. And thank you released an e book. Tell us a little quickly about that. We've got one minute left, give a quick plug. >>The book echoes the podcast chapters relate to the, to the episodes of the book. We're dropping the first five chapters plus forward for free on the IBM website. Other chapters will become available um, and drop as they become available. The book makes the content searchable on the internet. We go into more detail with advice on how to get started. You get to hear the topics and the voice of those subject matter experts and uh I really, you know, suggest you go out and check it out. >>Alright, jerry, cuomo IBM fellow VPC T IBM automation um also a content creator podcast, art of automation, jerry. We're gonna lift it listed on our silicon angle and our cube sites. Get you some extra love on that. Love the podcast. Love the focus on sharing from experts in the field. Thanks for coming on. >>Thank you so much for having me again, john >>Okay. I'm John Fryer with the Cube here for IBM think 2021. Thanks for watching.

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS and our community partners. All right, you're continuing or we're continuing around the clock coverage and around the world coverage off a W s reinvent 2020 virtual conference This year, I'm guessing hundreds of thousands of folks are tuning in for coverage. And we have we have on the other end of the country a cube alarm. Stephen Towel, co founder and CTO of immunity. Stephen, welcome back to the show. >>Great. Great to be here. Thanks for having me again. I hope to match your enthusiasm. >>You know what is, uh, your co founder? I'm sure you could match the enthusiasm. Plus, we're talking about data governance. You You've been on the cute before, and you kind of laid the foundation for us last year. Talking about challenges around data access and data access control. I want to extend this conversation. I had a conversation with a CEO chief data officer a couple of years ago. He shared how his data analysts his the people that actually take the data and make business decisions or create outcomes to make business decisions spent 80% of their time wrangling the data just doing transformations. >>How's the >>Muda helping solve that problem? >>Yeah, great questions. So it's actually interesting. We're seeing a division of roles in these organizations where we have data engineering teams that are actually managing. Ah, lot of the prep work that goes into exposing data and releasing data analysts. Uh, and as part of their day to day job is to ensure that that data that they're released into the analyst is what they're allowed to see. Um and so we kind of see this, this problem of compliance getting in the way of analysts doing their own transformation. So it would be great if we didn't have to have a limited to just this small data engineering team to release the data. What we believe one of the rial issues behind that is that they are the ones that are trusted. They're the only ones that could see all the data in the clear. So it needs to be a very small subset of humans, so to speak, that can do this transformation work and release it. And that means that the data analyst downstream are hamstrung to a certain extent and bottlenecked by requesting these data engineers do some of this transformation work for them. Eso I think because, as you said, that's so critical to being able to analyze data, that bottleneck could could be a back breaker for organization. So we really think that to you need to tie transformation with compliance in order to streamline your analytics in your organization. >>So that has me curious. What does that actually look like? Because Because when I think of a data analyst, they're not always thinking about Well, who should have this data? They're trying to get the answer to the question Thio provide to the data engineer. What does that functionally looked like when that when you want to see that relationship of collaboration? >>Yeah, So we e think the beauty of a Muda and the beauty of governance solutions done right is that they should be invisible to the downstream analysts to a certain extent. So the data engineering team will takes on some requirements from their legal compliance. Seems such as you need a mask p I I or you need Thio. Hi. These kinds of rose from these kinds of analysts, depending on what the users doing. And we've just seen an explosion of different slices or different ways, you should dice up your data and what who's allowed to see what and not just about who they are, but what they're doing on DSO. You can kind of bake all these policies upfront on your data on a tool like Kamuda, and it will dynamically react based on who the analyst is and what they're doing to ensure that the right policies air being enforced. And we could do that in a way that when the analysts I mean, what we also see is just setting your policies on your data. Once up front, that's not the end of the story. Like a lot of people will tap themselves on the back and say, Look, we've got all our data protected appropriately, job done. But that's not really the case, because the analysts will start creating their own data products and they want to share that with other analysts. And so when you think about this, this becomes a very complex problem of okay. Before someone can share their data with anyone else, we need to understand what they were allowed to see eso being able to control the kind of this downstream flow of of transformations and feature engineering to ensure that Onley the right people, are seeing the things that they're allowed to see. But still, enabling analytics is really the challenges that that we saw that in Muda Thio, you know, help the the data teams create those initial policies at scale but also help the analytical teams build driven data products in a way that doesn't introduce data leaks. >>So as I think about the traditional ways in which we do this, we kind of, you know, take a data sad. Let's say, is the databases and we said, security rules etcetera on those data states. That's what you're painting to ISMM or of Dynamic. Has Muto approaching this problem from just a architectural direction? >>Yeah, great question. So I'm sure you've probably heard the term role based access control on, but it's been around forever where you basically aggregate your users in the roles, and then you build rules around those roles on gritty, much every legacy. Already, BMS manages data access this way. Um, what we're seeing now and I call it the private data era that we're now embarking on or have been embarking on for the past three years or so. Where consumers are more aware of their data, privacy and the needs they had their there's, you know, data regulations coming fast and furious with no end in sight. Um, we believe that this role based access control paradigm is just broken. We've got customers with thousands of roles that they're trying to manage Thio to, you know, slice up the data all the different ways that they need Thio. So instead, we we offer an accurate based access control solution and also policy based access control solution. We're. Instead, it's really about How do you dynamically enforced policy by separating who the user is from the policy that needs to be enforced and and having that execute at runtime? A good analogy to this is role based. Access control is like writing code without being able to use variables. You're writing the same block a code over and over again with slight changes based on the roll where actually based access control is, you're able to use variables and basically the policy gets decided at runtime based on who the user is and what they're doing. So >>that dynamic nature kind of lends itself to the public cloud. Were you seeing this applied in the world off a ws were here Reinvent so our customers using this with a W s >>So it all comes down to scalability so that the same reasons that used to separate storage from compute. You know, you get your storage in one place you could ephemera, lee, spin up, compute like EMR if you want. Um, you can use Athena against your storage in a server lis way that that kind of, um, freedom to choose whatever compute you want. Um, the same kind of concepts of apply with policy enforcement. You wanna separate your policy from your platform on that This private data era has has, you know, created this need just like you have to separate your compute from storage in the big data era. And this allows you to have a single plane of glass to enforce policy consistently, no matter what compute you're using or what a U s resource is you're using, um and so this gives our customers power to not only, um, you know, build the rules that they need to build and not have to do it uniquely her service in the U. S. But also proved to their legal and compliance teams that they're doing it correctly because, um, when when you do it this way, it really simplifies everything. And you have one place to go toe, understand how policies being enforced. And this really gives you the auditing and reporting around, um, be enforcement that you've been doing to put every one of these, that everything is being done correctly and that your data consumers can understand You know how your data is being protected. Their data is being protected. Um, and you could actually answer those questions when they come at you. >>So let's put this idea to the test a little bit. So I have the data engineer who kind of designs the security policy around the data or implements that policy using Kamuda Aziz dictated by the security and chief data officer of the organization. Then I have the analyst, and the analyst is just using the tools at their disposal. Let's say that one analyst wants to use AWS Lambda and another analysts wants to use our type database or analysis tools. You're telling me that Muda allows the flexibility for that analyst to use either tool within a W S. >>That's right, because we enforce policy at the data layer. Eso If you think about a Muda, it's really three layers policy authoring, which you touched on where those requirements get turned into real policies. Policy decision ing. So at query time we see who the user is, what they're doing on what policy has been defined to dynamically build that policy at run time and then enforcement, which is what you're getting at. The enforcement happens at the data layer, for example, we can enforce policies, natively and spark. So no matter how you're connecting to spark, that policy is going to get enforced appropriately. So we don't really care about what the clients Liz, because the enforcement is happening at the data or the compute layer is is a more accurate way todo to say it >>so. A practical reality off collaboration, especially around large data sets, is the ability to share data across organizations. How is immune hoping thio just make that barrier? Ah, little lower but ensuring security so that when I'm sharing data with, uh, analysts with within another firm. They're only seeing the data that they need to see, but we can effectively collaborate on those pieces of content. >>Yeah, I'm glad you asked this. I mean, this is like the, you know, the big finale, right? Like, this is what you get when you have this granularity on your own data ecosystem. It enables you to have that granularity now, when you want to share outside of your internal ecosystem. And so I think an important part about this is that when you think about governance, you can't necessarily have one God users so to speak, that has control over all tables and all policies. You really need segmentation of duty, where different parts of the organ hooking their own data build their own policies in a way where people can't step on each other and then this can expand this out. The third party data sharing where you can set different anonymous ation levels on your data when you're sharing an external the organization verse, if it's internal users and then someone else in your ord could share their data with you and then that also do that Third party. So it really enables and freeze these organizations Thio share with each other in ways that weren't possibly before. Because it happens in the day. The layer, um, these organizations can choose their own compute and still have the same policies being forced again. Going back to that consistency piece, um, it provides. Think of it is almost a authoritative way to share data in your organization. It doesn't have to be ad hoc. Oh, I have to share with this group over here. How should I do it? What policies should enforce. There's a single authoritative way to set policy and share your data. >>So the first thing that comes to my mind, especially when we give more power to the users, is when the auditors come and they say, You know what, Keith? I understand this is the policy, but prove it. How do we provide auditors with the evidence that you know, the we're implementing the policy that we designed and then two were ableto audit that policy? >>Yeah. Good question. So, um, I briefly spoke about this a little bit, but the when you author and define the policies in the Muda there immediately being enforced. So when you write something in our platform, um, it's not a glorified Wikipedia, right? It's actually turning those policies on and enforcing it at the data later. And because of that, any query that's coming through a Muda is going to be audited. But I think even more importantly, to be honest, we keep a history of how policy changes happening over time, too. So you could understand, you know, so and so changed the policy on this table versus other table, you know, got newly added, these people got dropped from it. So you get this rich history of not only who's touching what data and what data is important, but you're also getting a rich history off. Okay, how have we been treating this data from a policy perspective over time? How is it like what were my risk levels over the past year? With B six tables on? You can answer those kinds of questions as well. >>And then we're in the era of cloud. We expect to be able to consume these services via AP I via pay as you go type of thing. How is your relationship with AWS and how in the cutting. Ultimately, the customer. How do I consume a music? >>Yeah, so in Munich can pretty much be deployed anywhere. So obviously we're talking to us here. We have a SAS offering where you can spin up Muda pretrial and just be often running building policies and hooking up hooking our policy enforcement engine into your compute. Um, that runs in our, um you know, infrastructure. There's also a deployment model where you deploy immune it into your VPC s so it can run on your infrastructure. Behind your firewalls on DWI do not require any public Internet access at all for that to run. We don't do any kind of phone homing because, obviously, privacy company, we take this very seriously internally as well. We also have on premise deployments, um, again with zero connectivity air gapped environments. Eso. So we offer that kind of flexibility to our customers wherever they want immediate toe to be deployed. An important thing to remember their two is immediate. Does not actually store any data. We just store metadata and policy information. Um, so it's that also provides the customers some flexibility where if they want to use our SAS, they can simply go policy in there, and then the data still lives in their account. We're just kind of pushing policy down into that. Dynamically. >>So Stephen Towel co founder c t o of immunity. I don't think you have to worry about matching my energy level. I through some pretty tough questions at at you and you were ready there with all the answers. You wanna see more interesting conversations from around the world with founders, builders, AWS reinvent is all about builders and we're talking to the builders throughout this show. Visit us on the web. The Cube. You can engage with us on Twitter. Talk to you next episode off the Cube from AWS reinvent 2020.

>> From around the globe, it's theCUBE, with digital coverage of VMworld 2020, brought to you by the VMworld and its ecosystem partners. >> I'm Stu Miniman, and welcome back to theCUBES coverage of VMworld 2020, our 11th year doing this show, of course, the global virtual event. And what do we love talking about on theCUBE? We love talking to customers. It is a user conference, of course, so really happy to welcome to the program. From the University of Pisa, the Chief Technology Officer Maurizio Davini and joining him is Thierry Pellegrini, one of our theCUBE alumni. He's the vice president of worldwide, I'm sorry, Workload Solutions and HPC with Dell Technologies. Thierry, thank you so much for joining us. >> Thanks too. >> Thanks to you. >> Alright, so let, let's start. The University of Pisa, obviously, you know, everyone knows Pisa, one of the, you know, famous city iconic out there. I know, you know, we all know things in Europe are a little bit longer when you talk about, you know, some of the venerable institutions here in the United States, yeah. It's a, you know, it's a couple of hundred years, you know, how they're using technology and everything. I have to imagine the University of Pisa has a long storied history. So just, if you could start before we dig into all the tech, give us our audience a little bit, you know, if they were looking up on Wikipedia, what's the history of the university? >> So University of Pisa is one of the oldest in the world because there has been founded in 1343 by a pope. We were authorized to do a university teaching by a pope during the latest Middle Ages. So it's really one of the, is not the oldest of course, but the one of the oldest in the world. It has a long history, but as never stopped innovating. So anything in Pisa has always been good for innovating. So either for the teaching or now for the technology applied to a remote teaching or a calculation or scientific computing, So never stop innovating, never try to leverage new technologies and new kind of approach to science and teaching. >> You know, one of your historical teachers Galileo, you know, taught at the university. So, you know, phenomenal history help us understand, you know, you're the CTO there. What does that encompass? How, you know, how many students, you know, are there certain areas of research that are done today before we kind of get into the, you know, the specific use case today? >> So consider that the University of Pisa is a campus in the sense that the university faculties are spread all over the town. Medieval like Pisa poses a lot of problems from the infrastructural point of view. So, we have bought a lot in the past to try to adapt the Medieval town to the latest technologies advancement. Now, we have 50,000 students and consider that Pisa is a general partners university. So, we cover science, like we cover letters in engineering, medicine, and so on. So, during the, the latest 20 years, the university has done a lot of effort to build an infrastructure that was able to develop and deploy the latest technologies for the students. So for example, we have a private fiber network covering all the town, 65 kilometers of a dark fiber that belongs to the university, four data centers, one big and three little center connected today at 200 gigabit ethernet. We have a big data center, big for an Italian University, of course, and not Poland and U.S. university, where is, but also hold infrastructure for the enterprise services and the scientific computing. >> Yep, Maurizio, it's great that you've had that technology foundation. I have to imagine the global pandemic COVID-19 had an impact. What's it been? You know, how's the university dealing with things like work from home and then, you know, Thierry would love your commentary too. >> You know, we, of course we were not ready. So we were eaten by the pandemic and we have to adapt our service software to transform from imperson to remote services. So we did a lot of work, but we are able, thanks to the technology that we have chosen to serve almost a 100% of our curriculum studies program. We did a lot of work in the past to move to virtualization, to enable our users to work for remote, either for a workstation or DC or remote laboratories or remote calculation. So virtualization has designed in the past our services. And of course when we were eaten by the pandemic, we were almost ready to transform our service from in person to remote. >> Yeah, I think it's, it's true, like Maurizio said, nobody really was preparing for this pandemic. And even for, for Dell Technologies, it was an interesting transition. And as you can probably realize a lot of the way that we connect with customers is in person. And we've had to transition over to modes or digitally connecting with customers. We've also spent a lot of our energy trying to help the community HPC and AI community fight the COVID pandemic. We've made some of our own clusters that we use in our HPC and AI innovation center here in Austin available to genomic research or other companies that are fighting the the virus. And it's been an interesting transition. I can't believe that it's already been over six months now, but we've found a new normal. >> Detailed, let's get in specifically to how you're partnering with Dell. You've got a strong background in the HPC space, working with supercomputers. What is it that you're turning to Dell in their ecosystem to help the university with? >> So we are, we have a long history in HPC. Of course, like you can imagine not to the biggest HPC like is done in the U.S. so in the biggest supercomputer center in Europe. We have several system for doing HPC. Traditionally, HPC that are based on a Dell Technologies offer. We typically host all kind of technology's best, but now it's available, of course not in a big scale but in a small, medium scale that we are offering to our researcher, student. We have a strong relationship with Dell Technologies developing together solution to leverage the latest technologies, to the scientific computing, and this has a lot during the research that has been done during this pandemic. >> Yeah, and it's true. I mean, Maurizio is humble, but every time we have new technologies that are to be evaluated, of course we spend time evaluating in our labs, but we make it a point to share that technology with Maurizio and the team at the University of Pisa, That's how we find some of the better usage models for customers, help tuning some configurations, whether it's on the processor side, the GPU side, the storage and the interconnect. And then the topic of today, of course, with our partners at VMware, we've had some really great advancements Maurizio and the team are what we call a center of excellence. We have a few of them across the world where we have a unique relationship sharing technology and collaborating on advancement. And recently Maurizio and the team have even become one of the VMware certified centers. So it's a great marriage for this new world where virtual is becoming the norm. >> But well, Thierry, you and I had a conversation to talk earlier in the year when VMware was really geering their full kind of GPU suite and, you know, big topic in the keynote, you know, Jensen, the CEO of Nvidia was up on stage. VMware was talking a lot about AI solutions and how this is going to help. So help us bring us in you work with a lot of the customers theory. What is it that this enables for them and how to, you know, Dell and VMware bring, bring those solutions to bear? >> Yes, absolutely. It's one statistic I'll start with. Can you believe that only on average, 15 to 20% of GPU are fully utilized? So, when you think about the amount of technology that's are at our fingertips and especially in a world today where we need that technology to advance research and scientistic discoveries. Wouldn't it be fantastic to utilize those GPU's to the best of our ability? And it's not just GPU's , I think the industry has in the IT world, leverage virtualization to get to the maximum recycles for CPU's and storage and networking. Now you're bringing the GPU in the fold and you have a perfect utilization and also flexibility across all those resources. So what we've seen is that convergence between the IT world that was highly virtualized, and then this highly optimized world of HPC and AI because of the resources out there and researchers, but also data scientists and company want to be able to run their day to day activities on that infrastructure. But then when they have a big surge need for research or a data science use that same environment and then seamlessly move things around workload wise. >> Yeah, okay I do believe your stat. You know, the joke we always have is, you know, anybody from a networking background, there's no such thing as eliminating a bottleneck, you just move it. And if you talk about utilization, we've been playing the shell game for my entire career of, let's try to optimize one thing and then, oh, there's something else that we're not doing. So,you know, so important. Retail, I want to hear from your standpoint, you know, virtualization and HPC, you know, AI type of uses there. What value does this bring to you and, you know, and key learnings you've had in your organization? >> So, we as a university are a big users of the VMware technologies starting from the traditional enterprise workload and VPI. We started from there in the sense that we have an installation quite significant. But also almost all the services that the university gives to our internal users, either personnel or staff or students. At a certain point that we decided to try to understand the, if a VMware virtualization would be good also for scientific computing. Why? Because at the end of the day, their request that we have from our internal users is flexibility. Flexibility in the sense of be fast in deploying, be fast to reconfiguring, try to have the latest beats on the software side, especially on the AI research. At the end of the day we designed a VMware solution like you, I can say like a whiteboard. We have a whiteboard, and we are able to design a new solution of this whiteboard and to deploy as fast as possible. Okay, what we face as IT is not a request of the maximum performance. Our researchers ask us for flexibility then, and want to be able to have the maximum possible flexibility in configuring the systems. How can I say I, we can deploy as more test cluster on the visual infrastructure in minutes or we can use GPU inside the infrastructure tests, of test of new algorithm for deep learning. And we can use faster storage inside the virtualization to see how certain algorithm would vary with our internal developer can leverage the latest, the beat in storage like NVME, MVMS or so. And this is why at the certain point, we decided to try visualization as a base for HPC and scientific computing, and we are happy. >> Yeah, I think Maurizio described it it's flexibility. And of course, if you think optimal performance, you're looking at the bare medal, but in this day and age, as I stated at the beginning, there's so much technology, so much infrastructure available that flexibility at times trump the raw performance. So, when you have two different research departments, two different portions, two different parts of the company looking for an environment. No two environments are going to be exactly the same. So you have to be flexible in how you aggregate the different components of the infrastructure. And then think about today it's actually fantastic. Maurizio was sharing with me earlier this year, that at some point, as we all know, there was a lot down. You could really get into a data center and move different cables around or reconfigure servers to have the right ratio of memory, to CPU, to storage, to accelerators, and having been at the forefront of this enablement has really benefited University of Pisa and given them that flexibility that they really need. >> Wonderful, well, Maurizio my understanding, I believe you're giving a presentation as part of the activities this week. Give us a final glimpses to, you know, what you want your peers to be taking away from what you've done? >> What we have done that is something that is very simple in the sense that we adapt some open source software to our infrastructure in order to enable our system managers and users to deploy HPC and AI solution fastly and in an easy way to our VMware infrastructure. We started doing a sort of POC. We designed the test infrastructure early this year and then we go fastly to production because we had about the results. And so this is what we present in the sense that you can have a lot of way to deploy Vitola HPC, Barto. We went for a simple and open source solution. Also, thanks to our friends of Dell Technologies in some parts that enabled us to do the works and now to go in production. And that's theory told before you talked to has a lot during the pandemic due to the effect that stay at home >> Wonderful, Thierry, I'll let you have the final word. What things are you drawing customers to, to really dig in? Obviously there's a cost savings, or are there any other things that this unlocks for them? >> Yeah, I mean, cost savings. We talked about flexibility. We talked about utilization. You don't want to have a lot of infrastructure sitting there and just waiting for a job to come in once every two months. And then there's also the world we live in, and we all live our life here through a video conference, or at times through the interface of our phone and being able to have this web based interaction with a lot of infrastructure. And at times the best infrastructure in the world, makes things simpler, easier, and hopefully bring science at the finger tip of data scientists without having to worry about knowing every single detail on how to build up that infrastructure. And with the help of the University of Pisa, one of our centers of excellence in Europe, we've been innovating and everything that's been accomplished for, you know at Pisa can be accomplished by our customers and our partners around the world. >> Thierry, Maurizio, thank you much for so much for sharing and congratulations on all I know you've done building up that COE. >> Thanks to you. >> Thank you. >> Stay with us, lots more covered from VMworld 2020. I'm Stu Miniman as always. Thank you for watching the theCUBE. 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>> Announcer: From around the globe, it's theCUBE. Covering the UPGRADE 2020, the NTT Research Summit. Presented by NTT research. >> Hey, welcome back everybody. Jeff Frick here with theCUBE. Welcome back to our ongoing coverage of UPGRADE 2020. It's the NTT Research Labs Summit, and it's all about upgrading reality. Heavy duty basic research around a bunch of very smart topics. And we're really excited to have our next guest to kind of dive in. I promise you, it'll be the deepest conversation you have today, unless you watch a few more of these segments. So our first guest we're welcoming back Kazuhiro Gomi He's the president and CEO of NTT research, Kaza great to see you. >> Good to see you. And joining him is Yoshi Yamamoto. He is a fellow for NTT Research and also the director of the Physics and Informatics Lab. Yoshi, great to meet you as well. >> Nice to meet you. >> So I was teasing the crew earlier, Yoshi, when I was doing some background work on you and I pulled up your Wikipedia page and I was like, okay guys, read this thing and tell me what a, what Yoshi does. You that have been knee deep in quantum computing and all of the supporting things around quantum heavy duty kind of next gen computing. I wonder if you can kind of share a little bit, you know, your mission running this labs and really thinking so far in advance of what we, you know, kind of experience and what we work with today and this new kind of basic research. >> NTT started the research on quantum computing back in 1986 87. So it is already more than 30 years. So, the company invested in this field. We have accumulated a lot of sort of our ideas, knowledge, technology in this field. And probably, it is the right time to establish the connection, close connection to US academia. And in this way, we will jointly sort of advance our research capabilities towards the future. The goal is still, I think, a long way to go. But by collaborating with American universities, and students we can accelerate NTT effort in this area. >> So, you've been moving, you've been working on quantum for 30 years. I had no idea that that research has been going on for such a very long time. We hear about it in the news and we hear about it a place like IBM and iSensor has a neat little demo that they have in the new sales force period. What, what is, what makes quantum so exciting and the potential to work so hard for so long? And what is it going to eventually open up for us when we get it to commercial availability? >> The honest answer to that question is we don't know yet. Still, I think after 30 years I think of hard working on quantum Physics and Computing. Still we don't know clean applications are even, I think we feel that the current, all the current efforts, are not necessarily, I think, practical from the engineering viewpoint. So, it is still a long way to go. But the reason why NTT has been continuously working on the subject is basically the very, sort of bottom or fundamental side of the present day communication and the computing technology. There is always a quantum principle and it is very important for us to understand the quantum principles and quantum limit for communication and computing first of all. And if we are lucky, maybe we can make a breakthrough for the next generation communication and computing technology based on quantum principles. >> Right. >> But the second, is really I think just a guess, and hope, researcher's hope and nothing very solid yet. >> Right? Well, Kazu I want to go, go to you cause it really highlights the difference between, you know, kind of basic hardcore fundamental research versus building new applications or building new products or building new, you know, things that are going to be, you know, commercially viable and you can build an ROI and you can figure out what the customers are going to buy. It really reflects that this is very different. This is very, very basic with very, very long lead times and very difficult execution. So when, you know, for NTT to spend that money and invest that time and people for long, long periods of time with not necessarily a clean ROI at the end, that really, it's really an interesting statement in terms of this investment and thinking about something big like upgrading reality. >> Yeah, so that's what this, yeah, exactly that you talked about what the basic research is, and from NTT perspective, yeah, we feel like we, as Dr. Yamamoto, he just mentioned that we've been investing into 30 plus years of a time in this field and, you know, and we, well, I can talk about why this is important. And some of them is that, you know, that the current computer that everybody uses, we are certainly, well, there might be some more areas of improvement, but we will someday in, I don't know, four years, five years, 10 years down the road, there might be some big roadblock in terms of more capacity, more powers and stuff. We may run into some issues. So we need to be prepared for those kinds of things. So, yes we are in a way of fortunate that we are, we have a great team to, and a special and an expertise in this field. And, you know, we have, we can spend some resource towards that. So why not? We should just do that in preparation for that big, big wall so to speak. I guess we are expecting to kind of run into, five, 10 years down the road. So let's just looking into it, invest some resources into it. So that's where we are, we're here. And again, I I'm, from my perspective, we are very fortunate that we have all the resources that we can do. >> It's great. Right, as they give it to you. Dr. Yamamoto, I wonder if you can share what it's like in terms of the industry and academic working together. You look at the presentations that are happening here at the event. All the great academic institutions are very well represented, very deep papers. You at NTT, you spend some time at Stanford, talk about how it is working between this joint development with great academic institutions, as well as the great company. >> Traditionally in the United States, there has been always two complementary opportunities for training next generation scientists and engineers. One opportunity is junior faculty position or possible position in academia, where main emphasis is education. The other opportunity is junior researcher position in industrial lab where apparently the focus emphasis is research. And eventually we need two types of intellectual leaders from two different career paths. When they sort of work together, with a strong educational background and a strong research background, maybe we can make wonderful breakthrough I think. So it is very important to sort of connect between two institutions. However, in the recent past, particularly after Better Lab disappeared, basic research activity in industrial lab decreases substantially. And we hope MTT research can contribute to the building of fundamental science in industry side. And for that purpose cross collaboration with research Universities are very important. So the first task we have been working so far, is to build up this industry academia connection. >> Huge compliment NTT to continue to fund the basic research. Cause as you said, there's a lot of companies that were in it before and are not in it any more. And when you often read the history of, of, of computing and a lot of different things, you know, it goes back to a lot of times, some basic, some basic research. And just for everyone to know what we're talking about, I want to read a couple of, of sessions that you could attend and learn within Dr. Yamamoto space. So it's Coherent nonlinear dynamics combinatorial optimization. That's just one session. I love it. Physics successfully implements Lagrange multiplier optimization. I love it. Photonics accelerators for machine learning. I mean, it's so it's so interesting to read basic research titles because, you know, it's like a micro-focus of a subset. It's not quantum computing, it's all these little smaller pieces of the quantum computing stack. And then obviously very deep and rich. Deep dives into those, those topics. And so, again, Kazu, this is the first one that's going to run after the day, the first physics lab. But then you've got the crypto cryptography and information security lab, as well as the medical and health information lab. You started with physics and informatics. Is that the, is that the history? Is that the favorite child you can lead that day off on day two of the event. >> We did throw a straw and Dr. Yamamoto won it Just kidding (all laugh) >> (indistinct), right? It's always fair. >> But certainly this quantum, Well, all the topics certainly are focuses that the basic research, that's definitely a commonality. But I think the quantum physics is in a way kind of very symbolic to kind of show that the, what the basic research is. And many people has a many ideas associated with the term basic research. But I think that the quantum physics is certainly one of the strong candidates that many people may think of. So well, and I think this is definitely a good place to start for this session, from my perspective. >> Right. >> Well, and it almost feels like that's kind of the foundational even for the other sessions, right? So you talk about medical or you talk about cryptography in information, still at the end of the day, there's going to be compute happening to drive those processes. Whether it's looking at, at, at medical slides or trying to do diagnosis, or trying to run a bunch of analysis against huge data sets, which then goes back to, you know, ultimately algorithms and ultimately compute, and this opening up of this entirely different set of, of horsepower. But Dr. Yamamoto, I'm just curious, how did you get started down this path of, of this crazy 30 year journey on quantum computing. >> The first quantum algorithm was invented by David Deutsch back in 1985. These particular algorithm turned out later the complete failure, not useful at all. And he spent seven years, actually, to fix loophole and invented the first successful algorithm that was 1992. Even though the first algorithm was a complete failure, that paper actually created a lot of excitement among the young scientists at NTT Basic Research Lab, immediately after the paper appeared. And 1987 is actually, I think, one year later. So this paper appeared. And we, sort of agreed that maybe one of the interesting future direction is quantum information processing. And that's how it started. It's it's spontaneous sort of activity, I think among young scientists of late twenties and early thirties at the time. >> And what do you think Dr. Yamamoto that people should think about? If, if, if again, if we're at a, at a cocktail party, not with not with a bunch of, of people that, that intimately know the topic, how do you explain it to them? How, how should they think about this great opportunity around quantum that's kept you engaged for decades and decades and decades. >> The quantum is everywhere. Namely, I think this world I think is fundamentally based on and created from quantum substrate. At the very bottom of our, sort of world, consist of electrons and photons and atoms and those fundamental particles sort of behave according to quantum rule. And which is a very different from classical reality, namely the world where we are living every day. The relevant question which is also interesting is how our classical world or classical reality surfaces from the general or universal quantum substrate where our intuition never works. And that sort of a fundamental question actually opens the possibility I think by utilizing quantum principle or quantum classical sort of crossover principle, we can revolutionize the current limitation in communication and computation. That's basically the start point. We start from quantum substrate. Under classical world the surface is on top of quantum substrate exceptional case. And we build the, sort of communication and computing machine in these exceptional sort of world. But equally dig into quantum substrate, new opportunities is open for us. That's somewhat the fundamental question. >> That's great. >> Well, I'm not, yeah, we can't get too deep cause you'll lose me, you'll lose me long before, before you get to the bottom of the, of the story, but, you know, I really appreciate it. And of course back to you this is your guys' first event. It's a really bold statement, right? Upgrade reality. I just wonder if, when you look at the, at the registrant's and you look at the participation and what do you kind of anticipate, how much of the anticipation is, is kind of people in the business, you know, kind of celebrating and, and kind of catching up to the latest research and how much of it is going to be really inspirational for those next, you know, early 20 somethings who are looking to grab, you know, an exciting field to hitch their wagon to, and to come away after this, to say, wow, this is something that really hooked me and I want to get down and really kind of advance this technology a little bit, further advance this research a little bit further. >> So yeah, for, from my point of view for this event, I'm expecting, there are quite wide range of people. I'm, I'm hoping that are interested in to this event. Like you mentioned that those are the, you know, the business people who wants to know what NTT does, and then what, you know, the wider spectrum of NTT does. And then, and also, especially like today's events and onwards, very specific to each topic. And we go into very deep dive. And, and so to, to this session, especially in a lot of participants from the academia's world, for each, each subject, including students, and then some other, basically students and professors and teachers and all those people as well. So, so that's are my expectations. And then from that program arrangement perspective, that's always something in my mind that how do we address those different kind of segments of the people. And we all welcoming, by the way, for those people. So to me to, so yesterday was the general sessions where I'm kind of expecting more that the business, and then perhaps some other more and more general people who're just curious what NTT is doing. And so instead of going too much details, but just to give you the ideas that the what's that our vision is and also, you know, a little bit of fla flavor is a good word or not, but give you some ideas of what we are trying to do. And then the better from here for the next three days, obviously for the academic people, and then those who are the experts in each field, probably day one is not quite deep enough. Not quite addressing what they want to know. So day two, three, four are the days that designed for that kind of requirements and expectations. >> Right? And, and are most of the presentations built on academic research, that's been submitted to journals and other formal, you know, peer review and peer publication types of activities. So this is all very formal, very professional, and very, probably accessible to people that know where to find this information. >> Mmh. >> Yeah, it's great. >> Yeah. >> Well, I, I have learned a ton about NTT and a ton about this crazy basic research that you guys are doing, and a ton about the fact that I need to go back to school if I ever want to learn any of this stuff, because it's, it's a fascinating tale and it's it's great to know as we've seen these other basic research companies, not necessarily academic but companies kind of go away. We mentioned Xerox PARC and Bell Labs that you guys have really picked up that mantle. Not necessarily picked it up, you're already doing it yourselves. but really continuing to carry that mantle so that we can make these fundamental, basic building block breakthroughs to take us to the next generation. And as you say, upgrade the future. So again, congratulations. Thanks for sharing this story and good luck with all those presentations. >> Thank you very much. >> Thank you. >> Thank you. Alright, Yoshi, Kazu I'm Jeff, NTT UPGRADE 2020. We're going to upgrade the feature. Thanks for watching. See you next time. (soft music)

>> Hello, my name is Daniel Wichs, I'm a senior scientist at NTT research and a professor at Northeastern University. Today I want to tell you about incompressible encodings. This is a recent work from Crypto 2020 and it's a joint work with Tal Moran. So let me start with a question. How much space would it take to store all of Wikipedia? So it turns out that you can download Wikipedia for offline use and some reasonable version of it is about 50 gigabytes in size. So as you'd expect, it's a lot of data, it's quite large. But there's another way to store Wikipedia which is just to store the link www.wikipedia.org that only takes 17 bytes. And for all intents and purposes as long as you have a connection to the internet storing this link is as good as storing the Wikipedia data. You can access a Wikipedia with this link whenever you want. And the point I want to make is that when it comes to public data like Wikipedia, even though the data is huge, it's trivial to compress it down because it is public just by storing a small link to it. And the question for this talk is, can we come up with an incompressible representation of public data like Wikipedia? In other words can we take Wikipedia and represent it in some way such that this representation requires the full 50 gigabytes of storage store, even for someone who has the link to the underlying Wikipedia data and can get the underlying data for free. So let me actually tell you what this means in more detail. So this is the notion of incompressible encodings that we'll focus on in this work. So incompressible encoding consists of an encoding algorithm and a decoding algorithm, these are public algorithms. There's no secret key. Anybody can run these algorithms. The encoding algorithm takes some data m, let's say the Wikipedia data and encodes it in some probabilistic randomized way to derive a codeword c. And the codeword c, you can think of it as just an alternate representation of the Wikipedia data. Anybody can come and decode the codeword to recover the underlying data m. And the correctness property we want here is that no matter what data you start with, if you encode the data m and then decode it, you get back the original data m. This should hold with probably one over the randomness of the encoding procedure. Now for security, we want to consider an adversary that knows the underlying data m, let's say has a link to Wikipedia and can access the Wikipedia data for free does not pay for storing it. The goal of the adversary is to compress this codeword that we created this new randomized representation of the Wikipedia data. So the adversary consists of two procedures a compression procedure and a decompression procedure. The compression procedure takes its input the codeword c and output some smaller compressed value w and the decompression procedure takes w and its goal is to recover the codeword c. And a security property says that no efficient adversary should be able to succeed in this game with better than negligible property. So there are two parameters of interest in this problem. One is the codeword size, which we'll denote by alpha, and ideally we want the codeword size alpha to be as close as possible to the original data size. In other words we don't want the encoding to add too much overhead to the data. The second parameter is the incompressibility parameter beta and that tells us how much space, how much storage and adversary needs to use in order to store the codeword. And ideally, we want this beta to be as close as possible to the codeword size alpha, which should also be as close as possible to the original data size. So I want to mention that there is a trivial construction of incompressible encodings that achieves very poor parameters. So the trivial construction is just take the data m and add some randomness, concatenate some randomness to it and store the original data m plus the concatenated randomness as the codeword. And now even an adversary that knows the underlying data m cannot compress the randomness. So the incompressibility, so we ensure that this construction is incompressible with incompressibility parameter beta that just corresponds to the size of this randomness we added. So essentially the adversary cannot compress the red part of the codeword. So this gets us a scheme where alpha the size of the codeword, is the original data size m plus the incompressible parameter beta. And it turns out that you cannot do better than this information theoretically. So this is not what we want for this we want to focus on what I will call good incompressible encodings. So here, the codeword size should be as close as possible to the data size, should be just one plus little o one of the data size. And the incompressibility should be as essential as large as the entire codeword the adversary cannot compress the codeword almost at all, the incompressible parameter beta is one minus little o one of the data size or the codeword size. And in essence, what this means is that we're somehow want to take the randomness of the encoding procedure and spread it around in some clever way throughout the codeword in such a way that's impossible for the adversary to separate out the randomness and the data, and only store the randomness and rely on the fact that it can get the data for free. We want to make sure it's impossible that adversary accesses essentially this entire code word which contains both the randomness and data and some carefully intertwined way and cannot compress it down using the fact that it knows the data parts. So this notion of incompressible encodings was defined actually in a prior work of Damgard-Ganesh and Orlandi from crypto 2019. They defined a variant of this notion, they had a different name for it. As a tool or a building block for a more complex cryptographic primitive that they called Proofs of Replicated Storage. And I'm not going to talk about what these are. But in this context of constructing these Proofs of Replicated Storage, they also constructed incompressible encodings albeit with some major caveats. So in particular, their construction relied on the random Oracle models, the heuristic construction and it was not known whether you could do this in the standard model, the encoding and decoding time of the construction was quadratic in the data size. And in particular, here we want to apply this, we want to use these types of incompressible encodings on fairly large data like Wikipedia data, 50 gigabytes in size. So quadratic runtime on such huge data is really impractical. And lastly the proof of security for their construction was flawed or someone incompleted, didn't consider general adversaries. And the slope was actually also noticed by concurrent work of Garg-Lu and Waters. And they managed to give a fixed proof for this construction but this required actually quite a lot of effort. It was a highly non-trivial and subtle proof to proof the original construction of Damgard-Ganesh and Orlandi secure. So in our work, we give a new construction of these types of incompressible encodings, our construction already achieved some form of security in the Common Reference String Model come Random String Model without the use of Random Oracles. We have a linear encoding time, linear in the data size. So we get rid of the quadratic and we have a fairly simple proof of security. In fact, I'm hoping to show you a slightly simplified form of it and the stock. We also give some lower bounds and negative results showing that our construction is optimal in some aspects and lastly we give a new application of this notion of incompressible encodings to something called big-key cryptography. And so I want to tell you about this application, hopefully it'll give you some intuition about why incompressible encodings are interesting and useful, and also some intuition about what their real goal is or what it is that they're trying to achieve. So, the application of big-key cryptography is concerned with the problem of system compromise. So, a computer system can become compromised either because the user downloads a malware or remote attacker manages to hack into it. And when this happens, the remote attacker gains control over the system and any cryptographic keys that are stored on the system can easily be exfiltrated or just downloaded out of the system by the attacker and therefore, any security that these cryptographic keys were meant to provide is going to be completely lost. And the idea of big-key cryptography is to mitigate against such attacks by making the secret keys intentionally huge on the order of many gigabytes to even terabytes. And the idea is that by having a very large secret key it would make it harder to exfiltrate such a secret key. Either because the adversary's bandwidth to the compromised system is just not large enough to exfiltrate such a large key or because it might not be cost-effective to have to download so much data of compromised system and store so much data to be able to use the key in the future, especially if the attacker wants to do this on some mass scale or because the system might have some other mechanisms let's say firewall that would detect such large amounts of leakage out of the compromised system and block it in some way. So there's been a lot of work on this idea building big-key crypto systems. So crypto systems where the secret key can be set arbitrarily huge and these crypto systems should testify two goals. So one is security, security should hold even if a large amount of data about the secret key is out, as long as it's not the entire secret key. So when you have an attacker download let's say 90% of the data of the secret key, the security of the system should be preserved. And the second property is that even though the secret key of the system can be huge, many gigabytes or terabytes, we still want the crypto system to remain efficient even though the secret is huge. And particularly this means that the crypto system can even read the entire secret key during each cryptographic operation because that would already be too inefficient. So it can only read some small number of bits of the secret key during each operation, then it performs. And so there's been a lot of work constructing these types of crypto systems but one common problem for all these works is that they require the user to waste a lot of their storage the storage on their computer in storing this huge secret key which is useless for any other purpose, other than providing security. And users might not want to do this. So that's the problem that we address here. And the new idea in our work is let's make the secret key useful instead of just having a secret key with some useless, random data that the cryptographic scheme picks, let's have a secret key that stores let's say the Wikipedia data at which a user might want to store in their system anyway or the user's movie collection or music collection et cetera and the data that the user would want to store on their system. Anyway, we want to convert it. We want to use that as the secret key. Now we think about this for a few seconds. Well, is it a good idea to use Wikipedia as a secret key? No, that sounds like a terrible idea. Wikipedia is not secret, it's public, it's online, Anyone can access it whenever they want. So it's not what we're suggesting. We're suggesting to use an incompressible encoding of Wikipedia as a secret key. Now, even though Wikipedia is public the incompressible encoding is randomized. And therefore the accuracy does not know the value of this incompressible encoding. Moreover, because it's incompressible in order for the adversary to steal, to exfiltrate the entire secret key, it would have to download a very large amount of data out of the compromised system. So there's some hope that this could provide security and we show how to build public encryption schemes and the setting that make use of a secret key which is an incompressible coding of some useful data like Wikipedia. So the secret key is an incompressible encoding of useful data and security ensures that the adversary will need to exfiltrate almost entire key to break the security of this critical system. So in the last few minutes, let me give you a very brief overview of our construction of incompressible encodings. And for this part, we're going to pretend we have something a real beautiful cryptographic object called Lossy Trapdoor Permutations. It turns out we don't quite have an object that's this beautiful and in the full construction, we relax this notion somewhat in order to be able to get our full construction. So Lossy Trapdoor Permutation is a function f we just key by some public key pk and it maps end bits to end bits. And we can sample the public key in one of two indistinguishable modes. In injective mode, this function of fPK is a permutation, and there's in fact, a trapdoor that allows us to invert it efficiently. And in the Lossy mode, if we sample the public in Lossy mode, then if we take some value, random value x and give you fpk of x, then this loses a lot of information about x. And in particular, the image size of the function is very small, much smaller than two to the n and so fpk of x does not contain all the information about x. Okay, so using this type of Lossy Trapdoor Permutation, here's the encoding of a message m using long random CRS come random string. So the encoding just consists of sampling the public key of this Lossy Trapdoor Permutation in injected mode, along with the trapdoor. And the encoding is just going to take the message m, x over it with a common reference string, come random string and invert the trapdoor permutation on this value. And then Coding will just be the public key and the inverse x. So this is something anybody can decode by just taking fpk of x, x over it with the CRS. And that will recover the original message. Now, to add the security, we're going to in the proof, we're going to switch to choosing the value x uniformly at random. So the x component of the codeword is going to be chosen uniformly random and we're going to set the CRS to be fpk of x, x over the message. And if you look at it for a second this distribution is exactly equivalent. It's just a different way of sampling the exact same distribution. And in particular, the relation between the CRS and X is preserved. Now in the second step, we're going to switch the public key to Lossy mode. And now when we do this, then the Codeword part, sorry then the CRS fpk of x, x over m only leaks some small amount of information about the random value x. In other words, even if that resists these, the CRS then the value x and the codeword has a lot of entropy. And because it has a lot of entropy it's incompressible. So what we did here is that we actually start to show that the code word and the CRS are indistinguishable from a different way of sampling them where we placed information about the message and the CRS and the codeword actually is truly random, has a lot of real entropy. And therefore even given the CRS the Codeword is incompressible that's the main idea behind the proof. I just want to make two remarks, our full constructions rely on a relaxed notion of Lossy Trapdoor Permutations which we're able to construct from either the decisional residuoisity or the learning with errors assumption. So in particular, we don't actually know how to construct trapdoor permutations from LWE from any postquantum assumption but the relaxed notion that we need for our actual construction, we can achieve from post quantum assumptions that get post quantum security. I want to mention two caveats of the construction. So one is that in order to make this work, the CRS needs to be long essentially as long as the message size. And also this construction achieves a weak form of selective security where the adversary decides to choose the message before seeing the CRS. And we show that both of these caveats are inherent. We show this by black-box separation and one can overcome them only in the random oracle model. Unless I want to just end with an interesting open question. I think one of the most interesting open questions in this area all of the constructions of incompressible encodings from our work and prior work required the use of some public key crypto assumptions some sort of trapdoor permutations or trapdoor functions. And one of the interesting open question is can you construct and incompressible encodings without relying on public key crypto, using one way functions or just the random oracle model. We conjecture this is not possible, but we don't know. So I want to end with that open questions and thank you very much for listening.

>> Announcer: From theCUBE Studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a Cube conversation. >> Hey welcome back everybody, Jeff Frick here with theCUBE. We are in our Palo Alto studios, COVID is still going on, so all of the interviews continue to be remote, but we're excited to have a Cube alumni, he hasn't been on for a long time, and this guy has been in the weeds of the storage industry for a very very long time and we're happy to have him on and get an update because there continues to be a lot of exciting developments. He's Phil Bullinger, he is the SVP and general manager, data center business unit from Western Digital joining us, I think for Colorado, so Phil, great to see you, how's the weather in Colorado today? >> Hi Jeff, it's great to be here. Well, it's a hot, dry summer here, I'm sure like a lot of places. But yeah, enjoying the summer through these unusual times. >> It is unusual times, but fortunately there's great things like the internet and heavy duty compute and store out there so we can get together this way. So let's jump into it. You've been in the he business a long time, you've been at Western Digital, you were at EMC, you worked on Isilon, and you were at storage companies before that. And you've seen kind of this never-ending up and to the right slope that we see kind of ad nauseum in terms of the amount of storage demands. It's not going anywhere but up, and please increase complexity in terms of unstructure data, sources of data, speed of data, you know the kind of classic big V's of big data. So I wonder, before we jump into specifics, if you can kind of share your perspective 'cause you've been kind of sitting in the Catford seat, and Western Digital's a really unique company; you not only have solutions, but you also have media that feeds other people solutions. So you guys are really seeing and ultimately all this compute's got to put this data somewhere, and a whole lot of it's sitting on Western Digital. >> Yeah, it's a great intro there. Yeah, it's been interesting, through my career, I've seen a lot of advances in storage technology. Speeds and feeds like we often say, but the advancement through mechanical innovation, electrical innovation, chemistry, physics, just the relentless growth of data has been driven in many ways by the relentless acceleration and innovation of our ability to store that data, and that's been a very virtuous cycle through what, for me, has been 30 years in enterprise storage. There are some really interesting changes going on though I think. If you think about it, in a relatively short amount of time, data has gone from this artifact of our digital lives to the very engine that's driving the global economy. Our jobs, our relationships, our health, our security, they all kind of depend on data now, and for most companies, kind of irrespective of size, how you use data, how you store it, how you monetize it, how you use it to make better decisions to improve products and services, it becomes not just a matter of whether your company's going to thrive or not, but in many industries, it's almost an existential question; is your company going to be around in the future, and it depends on how well you're using data. So this drive to capitalize on the value of data is pretty significant. >> It's a really interesting topic, we've had a number of conversations around trying to get a book value of data, if you will, and I think there's a lot of conversations, whether it's accounting kind of way, or finance, or kind of good will of how do you value this data? But I think we see it intrinsically in a lot of the big companies that are really data based, like the Facebooks and the Amazons and the Netflixes and the Googles, and those types of companies where it's really easy to see, and if you see the valuation that they have, compared to their book value of assets, it's really baked into there. So it's fundamental to going forward, and then we have this thing called COVID hit, which I'm sure you've seen all the memes on social media. What drove your digital transformation, the CEO, the CMO, the board, or COVID-19? And it became this light switch moment where your opportunities to think about it are no more; you've got to jump in with both feet, and it's really interesting to your point that it's the ability to store this and think about it now differently as an asset driving business value versus a cost that IT has to accommodate to put this stuff somewhere, so it's a really different kind of a mind shift and really changes the investment equation for companies like Western Digital about how people should invest in higher performance and higher capacity and more unified and kind of democratizing the accessibility that data, to a much greater set of people with tools that can now start making much more business line and in-line decisions than just the data scientist kind of on Mahogany Row. >> Yeah, as you mentioned, Jeff, here at Western Digital, we have such a unique kind of perch in the industry to see all the dynamics in the OEM space and the hyperscale space and the channel, really across all the global economies about this growth of data. I have worked at several companies and have been familiar with what I would have called big data projects and fleets in the past. But at Western Digital, you have to move the decimal point quite a few digits to the right to get the perspective that we have on just the volume of data that the world has just relentless insatiably consuming. Just a couple examples, for our drive projects we're working on now, our capacity enterprise drive projects, you know, we used to do business case analysis and look at their lifecycle capacities and we measured them in exabytes, and not anymore, now we're talking about zettabyte, we're actually measuring capacity enterprise drive families in terms of how many zettabyte they're going to ship in their lifecycle. If we look at just the consumption of this data, the last 12 months of industry TAM for capacity enterprise compared to the 12 months prior to that, that annual growth rate was north of 60%. And so it's rare to see industries that are growing at that pace. And so the world is just consuming immense amounts of data, and as you mentioned, the COVID dynamics have been both an accelerant in some areas, as well as headwinds in others, but it's certainly accelerated digital transformation. I think a lot of companies we're talking about, digital transformation and hybrid models and COVID has really accelerated that, and it's certainly driving, continues to drive just this relentless need to store and access and take advantage of data. >> Yeah, well Phil, in advance of this interview, I pulled up the old chart with all the different bytes, kilobytes, megabytes, gigabytes, terabytes, petabytes, exabytes, and zettabytes, and just per the Wikipedia page, what is a zettabyte? It's as much information as there are grains of sand in all the world's beaches. For one zettabyte. You're talking about thinking in terms of those units, I mean, that is just mind boggling to think that that is the scale in which we're operating. >> It's really hard to get your head wrapped around a zettabyte of storage, and I think a lot of the industry thinks when we say zettabyte scale era, that it's just a buzz word, but I'm here to say it's a real thing. We're measuring projects in terms of zettabytes now. >> That's amazing. Well, let's jump into some of the technology. So I've been fortunate enough here at theCUBE to be there at a couple of major announcements along the way. We talked before we turned the cameras on, the helium announcement and having the hard drive sit in the fish bowl to get all types of interesting benefits from this less dense air that is helium versus oxygen. I was down at the Mammer and Hammer announcement, which was pretty interesting; big heavy technology moves there, to again, increase the capacity of the hard drive's base systems. You guys are doing a lot of stuff on RISC-V I know is an Open source project, so you guys have a lot of things happening, but now there's this new thing, this new thing called zonedd storage. So first off, before we get into it, why do we need zoned storage, and really what does it now bring to the table in terms of a capability? >> Yeah, great question, Jeff. So why now, right? Because I mentioned storage, I've been in storage for quite some time. In the last, let's just say in the last decade, we've seen the advent of the hyperscale model and certainly a whole nother explosion level of data and just the veracity with which they hyperscalers can create and consume and process and monetize data. And of course with that, has also come a lot of innovation, frankly, in the compute space around how to process that data and moving from what was just a general purpose CPU model to GPU's and DPU's and so we've seen a lot of innovation on that side, but frankly, in the storage side, we haven't seen much change at all in terms of how operating systems, applications, file systems, how they actually use the storage or communicate with the storage. And sure, we've seen advances in storage capacities; hard drives have gone from two to four, to eight, to 10 to 14, 16, and now our leading 18 and 20 terabyte hard drives. And similarly, on the SSD side, now we're dealing with the capacities of seven, and 15, and 30 terabytes. So things have gotten larger, as you expect. And some interfaces have improved, I think NVME, which we'll talk about, has been a nice advance in the industry; it's really now brought a very modern scalable low latency multi-threaded interface to a NAM flash to take advantage of the inherent performance of transistor based persistent storage. But really when you think about it, it hasn't changed a lot. But what has changed is workloads. One thing that definitely has evolved in the space of the last decade or so is this, the thing that's driving a lot of this explosion of data in the industry is around workloads that I would characterize as sequential in nature, they're serial, you can capture it in written. They also have a very consistent life cycle, so you would write them in a big chunk, you would read them maybe in smaller pieces, but the lifecycle of that data, we can treat more as a chunk of data, but the problem is applications, operating systems, vial systems continue to interface with storage using paradigms that are many decades old. The old 512 byte or even Forte, Sector size constructs were developed in the hard drive industry just as convenient paradigms to structure what is an unstructured sea of magnetic grains into something structured that can be used to store and access data. But the reality is when we talk about SSDs, structure really matters, and so what has changed in the industry is the workloads are driving very very fresh looks at how more intelligence can be applied to that application OS storage device interface to drive much greater efficiency. >> Right, so there's two things going on here that I want to drill down on. On one hand, you talked about kind of the introduction of NAND and flash, and treating it like you did, generically you did a regular hard drive. But you could get away and you could do some things because the interface wasn't taking full advantage of the speed that was capable in the NAND. But NVME has changed that, and now forced kind of getting rid of some of those inefficient processes that you could live with, so it's just kind of classic next level step up and capabilities. One is you get the better media, you just kind of plug it into the old way. Now actually you're starting to put in processes that take full advantage of the speed that that flash has. And I think obviously prices have come down dramatically since the first introduction, where before it was always kind of a clustered off or super high end, super low latency, super high value apps, it just continues to spread and proliferate throughout the data center. So what did NVME force you to think about in terms of maximizing the return on the NAND and flash? >> Yeah, NVME, which we've been involved in the standardization, I think it's been a very successful effort, but we have to remember NVME is about a decade old, or even more when the original work started around defining this interface, but it's been very successful. The NVME standard's body is very productive cross company effort, it's really driven a significant change, and what we see now is the rapid adoption of NVME in all of data center architectures, whether it's very large hyperscale to classic on prem enterprise to even smaller applications, it's just a very efficient interface mechanism for connecting SSDs into a server. So we continue to see evolution at NVME, which is great, and we'll talk about ZNS today as one of those evolutions. We're also very keenly interested in NVME protocol over fabrics, and so one of the things that Western Digital has been talking about a lot lately is incorporating NVME over fabrics as a mechanism for now connecting shared storage into multiple post architectures. We think this is a very attractive way to build shared storage architectures of the future that are scalable, that are composable, that really have a lot more agility with respect to rack level infrastructure and applying that infrastructure to applications. >> Right, now one thing that might strike some people as kind of counterintuitive is within the zoned storage in zoning off parts of the media, to think of the data also kind of in these big chunks, is it feels contrary to kind of atomization that we're seeing in the rest of the data center, right? So smaller units of compute, smaller units of store, so that you can assemble and disassemble them in different quantities as needed. So what was the special attribute that you had to think about and actually come back and provide a benefit in actually kind of re-chunking, if you will, in these zones versus trying to get as atomic as possible? >> Yeah, it's a great question, Jeff, and I think it's maybe not intuitive in terms of why zoned storage actually creates a more efficient storage paradigm when you're storing stuff essentially in larger blocks of data, but this is really where the intersection of structure and workload and sort of the nature of the data all come together. If you turn back the clock maybe four or five years when SMR hard drives host managers SMR hard drives first emerged on the scene. This was really taking advantage of the fact that the right head on a hard disk drive is larger than the read head, or the read head can be much smaller, and so the notion of overlapping or shingling the data on the drive, giving the read head a smaller target to read, but the writer a larger write pad to write the data could actually, what we found was it increases aerial density significantly. And so that was really the emergence of this notion of sequentially written larger blocks of data being actually much more efficiently stored when you think about physically how it's being stored. What's very new now and really gaining a lot of traction is the SSD corollary to SMR on the hard drive, on the SSD side, we had the ZNS specification, which is, very similarly where you'd divide up the name space of an SSD into fixed size zones, and those zones are written sequentially, but now those zones are intimately tied to the underlying physical architecture of the NAND itself; the dyes, the planes, the read pages, the erase pages. So that, in treating data as a block, you're actually eliminating a lot of the complexity and the work that an SSD has to do to emulate a legacy hard drive, and in doing so, you're increasing performance and endurance and the predictable performance of the device. >> I just love the way that you kind of twist the lens on the problem, and on one hand, by rule, just looking at my notes here, the zoned storage device is the ZSD's introduce a number of restrictions and limitations and rules that are outside the full capabilities of what you might do. But in doing so, an aggregate, the efficiency, and the performance of the system in the whole is much much better, even though when you first look at it, you think it's more of a limiter, but it's actually opens up. I wonder if there's any kind of performance stats you can share or any kind of empirical data just to give people kind of a feel for what that comes out as. >> So if you think about the potential of zoned storage in general and again, when I talk about zoned storage, there's two components; there's an HDD component of zoned storage that we refer to as SMR, and there's an SSD version of that that we call ZNS. So we think about SMR, the value proposition there is additional capacity. So effectively in the same drive architecture, with roughly the same bill of material used to build the drive, we can overlap or shingle the data on the drive. And generally for the customer, additional capacity. Today with our 18, 20 terabyte offerings that's on the order of just over 10%, but that delta is going to increase significantly going forward to 20% or more. And when you think about a hyperscale customer that has not hundreds or thousands of racks, but tens of thousands of racks. A 10 or 20% improvement in effective capacity is a tremendous TCO benefit, and the reason we do that is obvious. I mean, the economic paradigm that drives large at-scale data centers is total custom ownership, both acquisition costs and operating costs. And if you can put more storage in a square tile of data center space, you're going to generally use less power, you're going to run it more efficiently, you're actually, from an acquisition cost, you're getting a more efficient purchase of that capacity. And in doing that, our innovation, we benefit from it and our customers benefit from it. So the value proposition for zoned storage in capacity enterprise HDV is very clear, it's additional capacity. The exciting thing is, in the SSD side of things, or ZNS, it actually opens up even more value proposition for the customer. Because SSDs have had to emulate hard drives, there's been a lot of inefficiency and complexity inside an enterprise SSD dealing with things like garbage collection and right amplification reducing the endurance of the device. You have to over-provision, you have to insert as much as 20, 25, even 28% additional man bits inside the device just to allow for that extra space, that working space to deal with delete of data that are smaller than the block erase that the device supports. So you have to do a lot of reading and writing of data and cleaning up. It creates for a very complex environment. ZNS by mapping the zoned size with the physical structure of the SSD essentially eliminates garbage collection, it reduces over-provisioning by as much as 10x. And so if you were over provisioning by 20 or 25% on an enterprise SSD, and a ZNS SSD, that can be one or two percent. The other thing I have to keep in mind is enterprise SSD is typically incorporate D RAM and that D RAM is used to help manage all those dynamics that I just mentioned, but with a much simpler structure where the pointers to the data can be managed without all the D RAM. We can actually reduce the amount of D RAM in an enterprise SSD by as much as eight X. And if you think about the MILA material of an enterprise SSD, D RAM is number two on the list in terms of the most expensive bomb components. So ZNS and SSDs actually have a significant customer total cost of ownership impact. It's an exciting standard, and now that we have the standard ratified through the NVME working group, it can really accelerate the development of the software ecosystem around. >> Right, so let's shift gears and talk a little bit about less about the tech and more about the customers and the implementation of this. So you talked kind of generally, but are there certain types of workloads that you're seeing in the marketplace where this is a better fit or is it just really the big heavy lifts where they just need more and this is better? And then secondly, within these hyperscale companies, as well as just regular enterprises that are also seeing their data demands grow dramatically, are you seeing that this is a solution that they want to bring in for kind of the marginal kind of next data center, extension of their data center, or their next cloud region? Or are they doing lift and shift and ripping stuff out? Or do they enough data growth organically that there's plenty of new stuff that they can put in these new systems? >> Yeah, I love that. The large customers don't rip and shift; they ride their assets for a long lifecycle, 'cause with the relentless growth of data, you're primarily investing to handle what's coming in over the transom. But we're seeing solid adoption. And in SMRS you know we've been working on that for a number of years. We've got significant interest and investment, co-investment, our engineering, and our customer's engineering adapting the application environment's to take advantage of SMR. The great thing is now that we've got the NVME, the ZNS standard gratified now in the NVME working group, we've got a very similar, and all approved now, situation where we've got SMR standards that have been approved for some time, and the SATA and SCSI standards. Now we've got the same thing in the NVME standard, and the great thing is once a company goes through the lift, so to speak, to adapt an application, file system, operating system, ecosystem, to zoned storage, it pretty much works seamlessly between HDD and SSD, and so it's not an incremental investment when you're switching technologies. Obviously the early adopters of these technologies are going to be the large companies who design their own infrastructure, who have mega fleets of racks of infrastructure where these efficiencies really really make a difference in terms of how they can monetize that data, how they compete against the landscape of competitors they have. For companies that are totally reliant on kind of off the shelf standard applications, that adoption curve is going to be longer, of course, because there are some software changes that you need to adapt to enable zoned storage. One of the things Western Digital has done and taken the lead on is creating a landing page for the industry with zoned storage.io. It's a webpage that's actually an area where many companies can contribute Open source tools, code, validation environments, technical documentation. It's not a marketeering website, it's really a website built to land actual Open source content that companies can use and leverage and contribute to to accelerate the engineering work to adapt software stacks to zoned storage devices, and to share those things. >> Let me just follow up on that 'cause, again, you've been around for a while, and get your perspective on the power of Open source. And it used to be the best secrets, the best IP were closely guarded and held inside, and now really we're in an age where it's not necessarily. And the brilliant minds and use cases and people out there, just by definition, it's more groups of engineers, more engineers outside your building than inside your building, and how that's really changed kind of a strategy in terms of development when you can leverage Open source. >> Yeah, Open source clearly has accelerated innovation across the industry in so many ways, and it's the paradigm around which companies have built business models and innovated on top of it, I think it's always important as a company to understand what value ad you're bringing, and what value ad the customers want to pay for. What unmet needs in your customers are you trying to solve for, and what's the best mechanism to do that? And do you want to spend your RND recreating things, or leveraging what's available and innovating on top of it? It's all about ecosystem. I mean, the days where a single company could vertically integrate top to bottom a complete end solution, you know, those are fewer and far between. I think it's about collaboration and building ecosystems and operating within those. >> Yeah, it's such an interesting change, and one more thing, again, to get your perspective, you run the data center group, but there's this little thing happening out there that we see growing, IOT, in the industrial internet of things, and edge computing as we try to move more compute and store and power kind of outside the pristine world of the data center and out towards where this data is being collected and processed when you've got latency issues and all kinds of reasons to start to shift the balance of where the compute is and where the store and relies on the network. So when you look back from the storage perspective in your history in this industry and you start to see basically everything is now going to be connected, generating data, and a lot of it is even Opensource. I talked to somebody the other day doing kind of Opensource computer vision on surveillance video. So the amount of stuff coming off of these machines is growing in crazy ways. At the same time, it can't all be processed at the data center, it can't all be kind of shipped back and then have a decision and then ship that information back out to. So when you sit back and look at Edge from your kind of historical perspective, what goes through your mind, what gets you excited, what are some opportunities that you see that maybe the laymen is not paying close enough attention to? >> Yeah, it's really an exciting time in storage. I get asked that question from time to time, having been in storage for more than 30 years, you know, what was the most interesting time? And there's been a lot of them, but I wouldn't trade today's environment for any other in terms of just the velocity with which data is evolving and how it's being used and where it's being used. A TCO equation may describe what a data center looks like, but data locality will determine where it's located, and we're excited about the Edge opportunity. We see that as a pretty significant, meaningful part of the TAM as we look three to five years. Certainly 5G is driving much of that, I think just any time you speed up the speed of the connected fabric, you're going to increase storage and increase the processing the data. So the Edge opportunity is very interesting to us. We think a lot of it is driven by low latency work loads, so the concept of NVME is very appropriate for that, we think, in general SSDs deployed and Edge data centers defined as anywhere from a meter to a few kilometers from the source of the data. We think that's going to be a very strong paradigm. The workloads you mentioned, especially IOT, just machine-generated data in general, now I believe, has eclipsed human generated data, in terms of just the amount of data stored, and so we think that curve is just going to keep going in terms of machine generated data. Much of that data is so well suited for zoned storage because it's sequential, it's sequentially written, it's captured, and it has a very consistent and homogenous lifecycle associated with it. So we think what's going on with zoned storage in general and ZNS and SMR specifically are well suited for where a lot of the data growth is happening. And certainly we're going to see a lot of that at the Edge. >> Well, Phil, it's always great to talk to somebody who's been in the same industry for 30 years and is excited about today and the future. And as excited as they have been throughout their whole careers. So that really bodes well for you, bodes well for Western Digital, and we'll just keep hoping the smart people that you guys have over there, keep working on the software and the physics, and the mechanical engineering and keep moving this stuff along. It's really just amazing and just relentless. >> Yeah, it is relentless. What's exciting to me in particular, Jeff, is we've driven storage advancements largely through, as I said, a number of engineering disciplines, and those are still going to be important going forward, the chemistry, the physics, the electrical, the hardware capabilities. But I think as widely recognized in the industry, it's a diminishing curve. I mean, the amount of energy, the amount of engineering effort, investment, that cost and complexity of these products to get to that next capacity step is getting more difficult, not less. And so things like zoned storage, where we now bring intelligent data placement to this paradigm, is what I think makes this current juncture that we're at very exciting. >> Right, right, well, it's applied AI, right? Ultimately you're going to have more and more compute power driving the storage process and how that stuff is managed. As more cycles become available and they're cheaper, and ultimately compute gets cheaper and cheaper, as you said, you guys just keep finding new ways to move the curve in. And we didn't even get into the totally new material science, which is also coming down the pike at some point in time. >> Yeah, very exciting times. >> It's been great to catch up with you, I really enjoy the Western Digital story; I've been fortunate to sit in on a couple chapters, so again, congrats to you and we'll continue to watch and look forward to our next update. Hopefully it won't be another four years. >> Okay, thanks Jeff, I really appreciate the time. >> All right, thanks a lot. All right, he's Phil, I'm Jeff, you're watching theCUBE. Thanks for watching, we'll see you next time.

>> Presenter: From theCUBE studios in Palo Alto in Boston, connecting with thought leaders around the globe, these are cloud native insights. >> Hi, I'm Stu Miniman, the host of Cloud Native Insights. When we launched this program, we talked about, how do we take advantage of the innovation and agility that's in the cloud? And of course, one of the big components that we've talked about for many years on theCUBE is, how do we empower developers? and developers are helping change things, and I'm really happy to welcome to the program first time guests that helped build many of the tools that developers are very well familiar. So Tom Preston Werner, he is the co-founder of Chatterbug, he is the creator of redwoodjs, we had an early episode, the JAMstack Netlify team, he's also on the board for that, and we'll talk about those pieces. People might know him, if you check him out on Wikipedia, you know, GitHub, he was one of the co-founders as well as held both CTO and CEO roles there. I could go on but Tom, thank you so much for joining us. >> Thank you for having me. >> All right, so let's start there, Tom, you know, when I live in the enterprise space, how do you take advantage of new things? One of the biggest challenges out there is, let's go to something new, but let's do it the old way. And we know that that really doesn't take advantage of it you know, I think back to the oldest, some of the older technologies, it's like, well, you know, if I talk to people that are riding horses, what do they want? You know, well, I want faster horses, not the, you know, let's completely change things. I was hearing a stat that, you know, back in the early days of cars, we had like, 30% of them were electric cars, and now it's one. So what's old is new again, but I digress. One, as I mentioned, you know, GitHub, of course, is, you know, such a fundamental piece when we look at in the technology space over the last decade, you know, get in general, GitHub, specifically, of course, has created so much value engaged, you know, just millions and millions of developers and transform businesses. Take us back a little bit and you know, like to get your philosophy on, you know, building tools, how do you do it? How do you think about it? And what's inspired you? >> Yeah, I think it goes a long way back to just wanting to build things for the community. One of the first big projects I worked on was called Gravatar, and I remember laying in bed staring at the ceiling, just trying to think up some idea that that would contribute to what we then called The Blogosphere, and I came up with an idea for avatars that would follow you around and I coded it up and I got it out to a few bloggers and they started using it, and it caught on and it was really, it really introduced me to this idea that no matter who you are, where you come from, or what your background is, you know, I grew up in Iowa, things are very different there. And with with the Internet, and the ability to code, you can impact the world in really significant ways. And so it follows on from there, and I think GitHub is an extension of that desire to really put things into the world that will be useful for people, and knowing that, if you have the ability to code and especially with the advent of web applications as a common tool, there's such power in that you have global reach, you just need a computer and the ability to code and you can create these things, and GitHub kind of became that. It was just, it started out really as a side project, and I hoped that someday it would be able to support me to work on it full time. But I, we started building it just because we wanted it to exist. And that's most of what I work on is, is just ideas that I want to exist in the world. >> Yeah, it's been one of those great trends to watch at, you know, there were certain technologies that used to have to be a nation state, or, you know, one of the one of the global 50 companies to take advantage of it. Now, tools like GitHub, making it so that, you know, the smallest company or even the individuals can participate in communities, can create and build you know, the building is such an important theme. So Maybe, let's fast forward a little bit if we would, I mentioned Netlify and JAMstack, you talked about the blogosphere, that team is helping to really reinvent how we think about the web, you know, it's real time, It's high performance, and you know, we need to be able to get that to where everybody is. So, you know, back in the early days, web pages, you know, relatively static and, you know, had certain criteria, and now, of course, you know, edge devices and the global population change things. So, you know, you, you've been engaged in a, you know, huge supporter of that project, and that'll lead us towards the redwoods discussion, but maybe bring us as to how you got involved there, and what got you excited? >> Well, like you said, Everything old is new again and I think that's true in fashion. It's also true in technology, in a lot of ways, and the JAMstack really is taking these old ideas where the web started, taking files and just serving them as static files and it's super fast, and it's extremely secure. This is how the internet started, and now we've sort of come full circle. But we've added a lot of really nice things and workflows on top of that. And so my journey into the JAMstack, I suppose, started more than a decade ago, when I started working on a project called Jekyll, that's a, I called it at the time, A Blog Aware Static Site Generator. So you would write your blog articles, and you would run it through Jekyll, and that would take your markdown, you'd write your articles in markdown, and it would combine them with a, some kind of a theme that you would have, and that would output static pages that represented your blog, and then you could serve those from any kind of static blog serving system. GitHub had has one built in called GitHub Pages, and so we ended up adopting Jekyll for GitHub Pages. So everything that you put up on GitHub Pages. would be run through Jekyll, and so it was a really natural place to put your blog. And so I had a blog post, one of my blog posts using Jekyll was called Blogging Like A Hacker. And it was this idea that you don't need WordPress, you don't need to have a database somewhere that's, that's hackable, that's going to cause you security problems, all the WordPress admin stuff that constantly is being attacked. You don't need all that, like you can just write articles in flat files, and then turn them into a blog statically and then put those up to serve them somewhere, right? And so when I say it like that, it sounds a little bit like the JAMstack, right? That's not how we thought about it at the time, because it was really hard to do dynamic things. So if you wanted to have comments on your blog for instance, then you needed to have some third party service that you would embed a component onto your blog, so you could receive comments. And so you had to start gluing things together, but even then, again, that sounds a little bit like the JAMstack. So it's all of these ideas that have been, evolving over the last decade to 15 years, that now we finally have an entire tool chain and adding Git on top of that and Git based workflows, and being able to push to GitHub and someone like Netlify can pick those up and publish them, and you have all these third party services that you can glue together without having to build them yourself. All of the billing things, like there's just the ecosystem is so much more advanced now, so many more bits are available for you to piece together that in a very short amount of time, you can have an extremely performant site capable of taking payments, and doing all of the dynamic things that we want to do. Well, many, I should say many of the dynamic things that we want to do, and it's fast and secure. So it's like the web used to be when the web started, but, now you can do all the modern things that you want to do. >> You're giving me flashbacks remembering how I glued discus into my Tumblr instance when that was rolling out. (laughing) >> That's what I was referring to, discuss. >> Yeah, so absolutely, you talk about there's just such a robust ecosystem out there, and one of the real challenges we have out there is, people will come in and they say, "Oh my gosh, where do I start?" And it's like, well, where do you want to go? There's the Paradox of Choice, and that I believe is one of the things that led you to create Redwoods. So help explain to our audience you know, you created this project Redwood, it related to JAMstack, but, but I'll let you explain you know, what it is in life needed? >> Yeah, Redwood is a response to a couple of things. One of those things, is the JavaScript world has, as everything has evolved in tremendous way, in all kinds of ways and almost entirely positive I think. The language itself has been improved so much from when I was a teenager using view source and copy pasting stuff into you know, some random X Files fan site. To now it's a first class language I can compete with with everything, from a ergonomics perspective. I really enjoy programming in it and I come from a Ruby, Ruby on Rails background and now I'm very happy in JavaScript that was not true even five, seven years ago, right? So JavaScript itself has changed a lot. Along with that comes NPM in the whole packaging universe, of availability of modules, right? So most of the things that you want to do, you can go and you can search and find code that's going to do those things for you, and so being able to, to just pull those into your projects so easily. That is amazing, right? The power that that gives you is tremendous. The problem comes in when, like you said, you have the Paradox of Choice. Now you have, not just one way to do something, but you have 100 ways to do something, right? And now as a as a developer, and especially as a new developer, someone who's just learning how to build web applications, you come into this and you say, all you see is the complexity, just overwhelming complexity, and every language goes through this. They go through a phase of sort of this Cambrian explosion of possibilities as people get excited, and you see that the web is embracing these technologies, and you see what's possible. Everyone gets excited and involved and starts creating solution after solution after solution, often times to the same problems. And that's a good thing, right, like exploring the territory is a good and necessary part of the evolution of programming languages and programming ecosystems. But there's comes a time where that becomes overwhelming and starts to trend towards being a negative. And so at Chatterbug, which is a foreign language learning service, if you want to learn how to speak French or Spanish or German, we'll help you do that, as part of that work, we started using react on the front end, because I really love what react brings you from a JavaScript and interactivity perspective. But along with react, you have to make about 50 other choices of technologies to use to actually create a fully capable website, something for state management, you got to choose a way to do JavaScript or sorry, CSS. There's 100 things that you have to choose, and it's, it seems very arbitrary and you go through a lot of churn, you choose one, and then the next day an article comes out and then people raving about another one, and then you choose, you're like, Oh, that one looks really nice. You know, grass is always greener, and so Redwood is a bit of a, an answer to that, or a response to that, which is to say, we've learned a lot of things now about what works in building with react, especially on the front end. And what I really want to do is have a tool that's more like Ruby on Rails, where I come from, having done years and years of Ruby on Rails, what GitHub was built with. And Ruby on Rails presents to you a fully capable web application framework that has made all the choices or most of the choices, many of the important choices. And the same is kind of missing in the JavaScript TypeScript world and so, when I saw Netlify come out with their feature where you could commit the code for a lambda function to your repository, and if you push that up to GitHub, Netlify will grab it, and they will orchestrate deploying that code to an AWS lambda so that you can run business logic in a lambda but without having to touch AWS, because touching AWS is another gigantic piece of complexity, and their user interfaces are sometimes challenging, I'll say. That, that then made me think that, here finally is the ability to combine everything that's awesome about the JAMstack and static files, and security, and this workflow, with the ability to do business logic, and that sounded to me like the makings of a full stack web application framework, and I kept waiting for someone to come out and be like, hey, tada, like we glued this all together, and here's your thing, that's rails, but for the JAMstack, JavaScript, TypeScript world and nobody was doing it. And so I started working on it myself, and that has become Redwoodjs. >> It's one of the things that excited me the early days when I looked into Serverless was that, that low bar to entry, you know, I didn't have to have, you know, a CS degree or five years of understanding a certain code base to be able to take advantage of it. Feels like you're hoping to extend that, it believe it's one of your passions, you know, helping with with Chatterbug and like, you know, helping people with that learning. What do you feel is the state out there? What's your thoughts about kind of the future of jobs, when it when it comes to this space? >> I think the future of jobs in technology and especially software development is, I mean, there is no, there is no better outlook for any profession than that. I mean, this is the, this is where the world is going, more and more of what we want to accomplish, we do in software and it happens across every industry. I mean, just look at Tesla's for instance, right? You think about automobiles and the car that you owned, you know, 10 years ago, and you're like, I don't know, I know there's a computer in here somewhere, but like, I don't really, you know, either the software for it is terrible, and you're like, who, when was the last time you actually use the navigation system in your car, right? You just like get like just turn that off because it's, it's so horrible. And then Tesla comes along and says, hey, what if we actually made all this stuff useful, and had a thoughtful interface and essentially built a car that where everything was controlled with software, and so now cars are are basically software wrapped in hardware, and the experience is amazing. And the same is true of everything, look at your, look at how many things that your phone has replaced that used to be physical devices. Look at manufacturing processes, look at any any element of bureaucracy, all of this stuff is mediated by computers, and oftentimes it's done badly. But this just shows how much opportunity there is speaking of like governmental websites, right, you go to the DMV, and you try to schedule an appointment, and you just have no confidence that that's going to work out because the interfaces feel like they were written 15 years ago, and sometimes I think they were, written that long ago. But there's so much, there's still so much improvement to be had and all of that is going to take developers to do it. Unless, you know, we figure out how to get AI to do it for us, and there's been some very interesting things lately around that angle, but to me, it's, humans will always be involved. And so, at some level, humans are telling machines what to do, whether you're doing it more or less directly, and having the ability to tell machines what to do gives you tremendous leverage. >> Yeah, we're big fans, if you know Erik Bryjolfsson and Andy McAfee from MIT, they've, you know, are very adamant that it's the combination of people plus machines that always will win against either people alone or machines alone. Tom, what, you know, right now we're in the middle of a global pandemic, there're financially, there's a lot of bad news around the globe right now. I've talked to many entrepreneurs that said, well, a downturn market is actually a great time to start something new. You're an investor, you've helped build lots of things. We talked a lot about lowering the bar for people to create and build new things. What do you see are some of the opportunities out there, if you know, you had to recommend for the entrepreneurs out there? Where should they be looking? >> I'd say look at all of the things in your life that have become challenging, because where there's challenge, where there's pain, there's opportunity for solutions. And especially when there's a big environmental change, which we see right now, with COVID-19, obviously has changed a lot of our behaviors and made some of the things that used to be easy. It's made those a lot harder, and so you see, certain segments of the economy are doing extremely well, namely technology and things that allow us to do interviews like this instead of in person, and so those industries are doing extremely well. So you look at the you look at the stock market in the United States, and it's it's very interesting, because while much of the country is suffering, the people that are already wealthy are doing very well, and technology companies are doing very well. And so the question for me is, what are the opportunities that we have, leveraging technology in the internet, to where we can create more opportunities for more people, to get people back to work, right? I think there's so much opportunity there. Just look at education, like the entire concept of educating kids right now and I have three. So we feel this very much, it has been turned on its head. And so we so you see many people looking for solutions in that space, and that's, I think that's as it should be. When things get, when things get challenged when our, our normal daily experience is so radically changed, there's opportunity there, because people are willing to change more quickly in a crisis, right? Because you need, you need something like any solution. And so some choice is going to be made, and where that's happening, then you can find early adopters more easily, than you can under other circumstances, and so in economic downturns, you often see that kind of behavior where these are crisis moments for people, you have an opportunity to come in and if you have something that could solve a problem for them, then you can get a user where that may have not been a problem for a person before. So where there is, where there is a crisis, there is always opportunity to help people solve their problems in different and better ways to address that crisis. So again, it goes back to pain, you know, and it doesn't have to be the pain from a crisis. It could be a pain from from anything. Just like with GitHub, it was, it was hard to share code as developers like it was, there was too much pain, and this was, we started it in 2008, right after the housing crisis. It was unrelated to that, but it turns out that when you start a company, when the economy is depressed in a certain way, then at least you can look forward to the economy getting better as you are building your company. >> Oh, Tom, Preston Werner, thank you so much for joining pleasure talking with you. I appreciate all of your input. >> Absolutely, thanks for having me. >> I'm Stu Miniman, thank you for joining this Episode of cloud native insights. Thank you for watching the theCUBE. (light music)

>>from the Cube Studios in >>Palo Alto and Boston connecting with thought >>leaders all around the world. This is a cube conversation. >>Hey, welcome back, everybody. Jeff Frick here with the Cube. We're in our Palo Alto Studios Cove. It is still going on. So, uh, all of our all of the interviews continue to be remote, but we're excited to have Ah, Cube alumni hasn't been on for a long time, but this guy has been in the weeds of the storage industry for a very, very long time, and we're happy to, uh, I have a mon and get an update because there continues to be a lot of exciting developments. He's Phill Bollinger. Ah, he is the SVP and general manager Data center business unit from Western Digital. Joining us, I think from Colorado. So, Phil, great to see you. How is the weather in Colorado today? >>Hi, Jeff. It's great to be here. Well, it's It's a hot, dry summer here. I'm sure like a lot of places. Yeah, enjoying enjoying this summer through these unusual times it >>is. It is unusual times, but fortunately, there's great things like the Internet and heavy duty. Ah, compute and store out there so we can we can get together this way. So let's jump into it. You've been in the business a long time. You've been a Western digital, your DMC you worked on I salon and you were at storage companies before that. And you've seen kind of this never ending up into the right slope that we see, you know, kind of ad nauseam. In terms of the amount of storage demands. It's not going anywhere but up in police. Increased complexity in terms of unstructured data, sources of data, speed of data, you know, the kind of classic big V's of big data. So I wonder before we jump into specifics if you can kind of share your perspective because you've been kind of sitting in the catbird seat. And Western Digital's a really unique company. You not only have solutions, but you also have media that feeds other people solutions. So you guys are really, you know, seeing. And ultimately all this computes gotta put this data somewhere, and a whole lot of it's in our western digital. >>Yeah, it's It's a great a great intro there. Yeah, it's been interesting, you know, through my career. I've seen a lot of advances in storage technology. Uh, you know, speeds and feeds like we often say, But you know, the advancement through mechanical innovation, electrical innovation, chemistry, physics, you know, just the relentless growth of data has been, has been driven in many ways by the relentless acceleration and innovation of our ability to store that data. And that's that's been a very virtuous cycle through you know what for me has been more than 30 years and in enterprise storage there are some really interesting changes going on that I think if you think about it in a relatively short amount of time, data has gone from, you know, just kind of this artifact of our digital lives, um, to the very engine that's driving the global economy, um, our jobs, our relationships, our health, our security. They all depend on data on for most companies, kind of irrespective of size. How you use data, how you how you store it, how you monetize it, how you use it to make better decisions to improve products and services. You know, it becomes not just a matter of whether your company's going to thrive and I bet in many industries it's it's almost an existential question. Is, is your company going to be around in the future? And it and it depends on how well you're using data. So this this drive toe capitalize on the value of data is is pretty significant. >>It's Ah, it's a really interesting topic. We've had a number of conversations around trying to get, like a book value of data, if you will. And I think there's a lot of conversations, whether it's accounting, kind of way or finance or kind of of good will of how do you value this data? But I think we see it intrinsically in a lot of the big companies that are really database, like the Facebooks and the Amazons and the Netflix and the Googles and those >>types >>of companies where it's really easy to see. And if you see you know the valuation that they have compared to their book value of assets, right, it's really baked into there. So it's it's it's fundamental to going forward. And then we have this thing called Covet Hit, which, you know, >>you've >>seen on the media on social media, right? What drove your digital transformation. The CEO CIO, the CMO, the board Rick over 19. And it became this light switch moment where your opportunities to think about it or no more, you've got to jump in with both feet. And it's really interesting to your point that it's the ability to store this and think about it differently as an asset driving business value versus a cost that I t has >>to >>accommodate to put this stuff somewhere. So it's a really different kind of a mind shift and really changes the investment equation for companies like Western Digital about how people should invest in higher performance and higher capacity and more unified it in kind of democratizing the accessibility that data to a much greater set of people with tools that can now start making much more business line and in line decisions than just the data scientists you know, kind of on mahogany row. >>Yeah, like as you mentioned Jeff Inherit Western Digital. We have such a unique kind of perch in the industry to see all the dynamics in the ODM space and the hyper scale space and the channel really across all the global economy's about this this growth of data. I have worked at several companies and have been familiar with what I would have called big data projects and and, ah, fleets in the past. But the Western digital you have to move the decimal point, you know, quite a few digits to the right to get to get the perspective that that we have on just the volume of data, that the world is just relentlessly, insatiably consuming. Just a couple examples for for our Dr Projects we're working on now, our capacity enterprise Dr. Projects. You know, we used to do business case analyses and look at their life cycle. Pass it ease and we measure them and exabytes and not anymore. Now we're talking about Zeta Bytes were actually measuring capacity Enterprise drive families in terms of how many's petabytes they're gonna ship in their life cycle. And if we look at just the consumption of this data the last 12 months of Industry tam for capacity enterprise, compared to the 12 months prior to that, that annual growth rate was north of 60%. So it's it's rare to see industries that are that are growing at that pace. And so the world is just consuming immense amounts of data. And as you mentioned, the dynamics have been both an accelerant in some areas as well as headwinds and others. But it's certainly accelerated digital transformation. I think a lot of companies were talking about digital transformation and and, um, hybrid models. And Covert has really accelerated that. And it's certainly driving continues to drive just this relentless need toe to store and access and take advantage of data. Yeah, >>well, filling In advance of this interview, I pulled up the old chart right with with the all the different bytes, right, kilobytes, megabytes, gigabytes, terabytes, petabytes, exabytes and petabytes. And just just for the Wikipedia page. What is is that a byte, a zoo? Much information as there are grains of sand in all the world's beaches. For one fight, you're talking about thinking in terms of those units. I mean, that is just mind boggling to think that that is the scale in which we're operating. >>It's really hard to get your head wrapped around a set amount of storage. And, you know, I think a lot of the industry thinks when we say that a byte scale era that It's just a buzzword. But I'm here to say it's a real thing where we're measuring projects and in terms of petabytes, that's >>amazing. Let's jump into some of the technology. So I've been fortunate enough here at the Cube toe to be there at a couple of major announcements along the way. We talked before we turned the cameras on the helium announcement and having the hard drive sit in the in the fish bowl, um, to get off types of interesting benefits from this less dense air that is helium versus oxygen. I was down at the mammary and hammer announcement, which was pretty interesting. Big, big, heavy technology moves there to again increase the capacity of the hard drive based systems. You guys are doing a lot of stuff on. This five I know is an open source projects. You guys have a lot of things happening, but now there's this new thing, this new thing called zoned storage. So first off before we get into, why do we need zone storage? And really, what does it now bring to the table in terms of ah, capability? >>Yeah, Great question, Jeff. So why now, right. I as I mentioned, you know, storage. I've been in storage for quite some time in the last. Let's just say, in the last decade we've seen the advent of the hyper scale model and certainly the, you know, a whole another explosion, level of, of data and just the veracity with which the hyper scaler is can create and consume and process and monetize data. And, of course, with that has also come a lot of innovation, frankly, in the compute space around had a process that data and moving from, you know, what was just a general purpose CPU model to GP use and DP use. And so we've seen a lot of innovation on that. But you know, frankly, in the storage side, we haven't seen much change at all in terms of how operating systems applications, final systems, how they actually use the storage or communicate with the storage. And sure we've seen, you know, advances in storage capacities. Hard drives have gone from 2 to 4 to 8 to 10 to 14 16 and now are leading 18 and 20 terabyte hard drives and similarly on the SSD side, you know, now we're dealing with the complexities of seven and 15 and 30 terabytes. So things have gotten larger, as you would expect, but and and some interfaces have improved, I think Envy Me, which we'll talk about, has been nice advance in the industry. It's really now brought a very modern, scalable, low latency, multi threaded interface to a NAND flash to take advantage of the inherent performance of transistor based, persistent storage. But really, when you think about it hasn't changed a lot and so but what has changed his workloads? One thing that definitely has evolved in the space of the last decade or so is this. The thing that's driving a lot of this explosion of data and industry is around workloads that I would characterize as a sequential in nature there, see, really captured and written. They also have a very consistent lifecycle, so you would write them in a big chunk. You would read them, uh, maybe in smaller pieces, but the lifecycle of that data we can treat more as a chunk of data, but the problem is applications. Operating systems. File systems continue to interface with storage, using paradigms that are, you know, many decades old, they'll find 12 bite or even four K sectors. Size constructs were developed in, you know, in the hard drive industry, just as convenient paradigms to structure what is unstructured sea of magnetic grains into something structured that can be used to store and access data. But the reality is, you know, when we talk about SSD is structured really matters. And so these what has changed in the industry as the workloads are driving very, very fresh looks at how more intelligence could be applied to that application OS storage device interface to drive much greater officials. >>Right? So there's there's two things going on here that I want to drill down on one hand. You know, you talked about kind of the introduction of NAND flash Ah, and treating it like you did generically. You did a regular hard drive, but but you could get away and you could do some things because the interface wasn't taking full advantage of the speed that was capable in the nan. But envy me has changed that and forced kind of getting getting rid of some of those inefficient processes that you could live with. So it's just kind of classic. Next next level step up and capabilities. One is you got the better media. You just kind of plug it into the old way. Now, actually, you're starting to put in processes that take full advantage of the speed that that flash has. And I think you know, obviously, prices have come down dramatically since the first introduction. And for before, we always kind of clustered offer super high end, super low latency, super high value APS. You know, it just continues to Teoh to spread and proliferate throughout the data center. So, you know what did envy me force you to think about in terms of maximizing, you know, kind of the return on the NAND and flash? >>Yeah, yeah, in envy me, which, you know, we've been involved in the standardization after I think it's been a very successful effort, but we have to remember Envy me is is about a decade old, you know, or even more When the original work started around defining this this interface and but it's been very successful, you know, the envy, any standards, bodies, very productive, you know, across company effort, it's really driven a significant change. And what we see now is the rapid adoption of Envy Me in all data center architectures. Whether it's a very large hyper scale to, you know, classic on prim enterprise to even, you know, smaller applications. It's just a very efficient interface mechanism for connecting SSD, ease and Teoh into a server, you know, So the we continue to see evolution and envy me, which is great, and we'll talk about Z and s. Today is one of those evolutions. We're also very keenly interested in VM e protocol over fabrics. And so one of the things that Western Digital has been talking about a lot lately is incorporating Envy me over fabrics as a mechanism for now connecting shared storage into multiple post architectures. We think this is a very attractive way to build shared storage architectures in the future that are scalable, that air compose herbal that really are more have a lot more agility with respect two rack level infrastructure and applying that infrastructure to applications. Right >>now, one thing that might strike some people it's kind of counterintuitive is is within the zone, um, storage and zoning off parts of the media to think of the data also kind of in these big chunks, is it? It feels contrary to kind of optimization that we're seeing in the rest of the data center. Right? So smaller units of compute smaller units of store so that you can assemble and disassemble them in different quantities as needed. So what was the special attributes that you had to think about and and actually come back and provide a benefit in actually kind of re chunking, if you will in the zones versus trying to get as atomic as possible? >>Yeah, It's a great question, Jeff, and I think it's maybe not intuitive in terms of why zone storage actually creates a more efficient storage paradigm when you're storing stuff essentially in larger blocks of data. But if this is really where the intersection of structure and workload and sort of the nature of the data all come together, uh, if you turn back the clock, maybe 45 years when SMR hard drives host managers from our hard drives first emerged on the scene, this was really taking advantage of the fact that the right head on a hard describe is larger than the reader can't reach. It could be much smaller, and so then the notion of overlapping or singling the data on the drive giving the read had a smaller target to read. But the writer a larger right pad to write the data I could. Actually, what we found was it increases areal density significantly, Um, and so that was really the emergence of this notion of sequentially written larger blocks of data being actually much more efficiently stored. When you think about physically how it's being stored, what is very new now and really gaining a lot of traction is is the the SSD corollary to tomorrow in the hard drive. On the SSD side, we have the CNS specification, which is very similarly where you divide up a name space of an SSD and two fixed size zones, and those zones are written sequentially. But now those zones are are intimately tied to the underlying physical architecture of the NAND itself. The dies, the planes, the the three pages, the the race pages so that in treating data as a black, you're actually eliminating a lot of the complexity and the work that an SSD has to do to emulate a legacy hard drive. And in doing so, you're increasing performance and endurance and and the predictable performance of the device. >>I just love the way that that, you know, you kind of twist the lens on the problem and and on one hand, you know, by rule just looking at my notes of his own storage devices, the CS DS introduced a number of restrictions and limitations and and rules that are outside the full capabilities of what you might do. But in doing so in aggregate, the efficiency and the performance of the system in the hole is much, much better, even though when you first look at you think it's more of a limiter, but it's actually opens up. I wonder if there's any kind of performance stats you can share or any kind of empirical data, just to >>get people kind >>of a feel for what? That what that comes out as >>so if you think about the potential of zone storage in general, when again, When I talk about zone storage, there's two components. There's an HDD component of zone storage that we that we refer to as S. Some are, and there's an SSD version of that that we call Z and s So you think about SMR. The value proposition. There is additional capacity so effectively in the same Dr architecture with with, you know, roughly the same bill of material used to build the drive. We can overlap or single the data on the drive and generate for the customer additional capacity. Today with our 18 20 terabyte offerings, that's on the order of just over 10% but that Delta is going to increase significantly, going forward 20% or more. And when you think about ah, hyper scale customer that has not hundreds or thousands of racks but tens of thousands of racks, a 10 or 20% improvement and effective capacity is a tremendous TCO benefit, and the reason we do that is obvious. I mean, the the the the economic paradigm that drives large scale data centers is total cost of ownership, the acquisition costs and operating costs. And if you can put more storage in a square, you know, style of data center space, you're going to generally use less power. You're gonna run it more efficiently. You're actually from an acquisition cost. You're getting a more efficient purchase of that capacity. And in doing that, our innovation, you know, we benefit from it and our customers benefit from it so that the value proposition pours. Don't storage in in capacity. Enterprise HDD is very clear. It's it's additional capacity. The exciting thing is in the SSD side of things for Z and as it actually opens up even more value proposition for the customer. Um, because SSD is have had to emulate hard drives. There's been a lot of inefficiency in complexity inside an enterprise. SSD dealing with things like garbage collection and write amplification, reducing the endurance of the device. You have to over provision. You have to insert as much as 2025 28% additional NAND bits inside the device just too allow for that extra space, that working space to deal with with delete of the you know that that are smaller than the the a block of race that that device supports. And so you have to do a lot of reading and writing of data and cleaning up it creates for a very complex environment. Z and S by mapping the zone size with the physical structure of the SSD, essentially eliminates garbage collection. It reduces over provisioning by as much as 10% are 10 x And so if you were over provisioning by 20 or 25% in an enterprise SSD and Xeon SSD, that could be, you know, one or 2%. The other thing we have to keep in mind is enterprise. SSD is typically incorporate D RAM and that D RAM is used to help manage all those dynamics that I that I just mentioned, but with a very much simpler structure where the pointers to the data can be managed without all that d ram, we can actually reduce the amount of D ram in an enterprise SSD by as much as eight X. And if you think about the bill of material of an enterprise, SSD d ram is number two on the list in terms of the most expensive bomb components. So Z and S and SSD is actually have a significant customer. Total cost of ownership impact. Um, it's it's an exciting it's an exciting standard. And now that we have the standard ratified through the Envy me working group, um, you can really accelerate the development of the software ecosystem around >>right. So let's shift gears and talk a little bit about less about the tech and more about the customers and the implementation of this. So, you know, are there you talked to kind of generally, but are there certain certain types of workloads that you're seeing in the marketplace where this is, you know, a better fit? Or is it just really the big heavy lifts? Um, where they just need more and this is better. And then secondly, within you know, these both hyper scale companies, um, as well as just regular enterprises that are also seeing their data demands grow dramatically. Are you seeing you know, that this is a solution that they want to bring in for kind of the marginal kind of next data center extension data center or their next ah, cloud region? Or are they doing you know, lift and shift and ripping stuff out? Or do they have enough? Do they have enough data growth organically? >>Then >>there's plenty of new stuff that they can. They can put in these new systems. >>Yeah, well, the large customers don't don't rip and shift. They they write their assets for a long life cycle because with the relentless growth of data. You're primarily investing to handle what's what's coming in over the transom, but we're seeing we're seeing solid adoption in SMR. As you know, we've been working on that for a number of years. We've we've got, you know, significant interest in investment co investment, our engineering and our customers engineering, adapting the the application environments. Let's take advantage of SMR. The great thing is, now that we've got the envy me, the Xeon s standard ratified now, in the envy of the working group, um, we've got a very similar and all approved now situation where we've got SMR standards that have been approved for some time in the sand and scuzzy standards. Now we've got the same thing in the envy, any standard. And that's the great thing is once a company goes through the lifts, so it's B to adapt an application file system, operating system, ecosystem to zone storage. It pretty much works seamlessly between HDD and SSD. And so it's not. It's not an incremental investment when you're switching technologies and for obviously the early adopters of these technologies are going to be the large companies who designed their own infrastructure. You have you know, mega fleets of racks of infrastructure where these efficiencies really, really make a difference in terms of how they can monetize that data, how they compete against, you know, the landscape of competitors They have, um, for companies that are totally reliant on kind of off the shelf standard applications. That adoption curve is gonna be longer, of course, because there are there are some software changes that you need to adapt to to enable zone storage. One of the things Western Digital is has done, and taking the lead on is creating a landing page for the industry with zone storage. Not Iot. It's a Web page that's actually an area where, where many companies can contribute open source tools, code validation environments, technical documentation it's not. It's not a marketeering website. It's really a website bill toe land, actual open source content that companies can and use and leverage and contribute to. To accelerate the engineering work to adapt software stacks his own storage devices on to share those things. >>Let me just follow up on that, because again you've been around for a while and get your perspective on the power of open source and you know, it used to be, you know, the the best secrets, the best I p were closely guarded and held inside. And now really, we're in an age where it's not necessarily and you know, the the brilliant minds and use cases and people out there. You know, just by definition, it's a It's a more groups of engineers, more engineers outside your building than inside your building and how that's really changed. You know, kind of the strategy in terms of development when you can leverage open source. >>Yeah, Open source clearly has has accelerated innovation across the industry in so many ways. Um, and it's ah, you know, it's the paradigm around which, you know companies have built business models and innovated on top of it. I think it's always important as a company to understand what value add, you're bringing on what value add that customers want to pay for what unmet needs and your customers are you trying to solve for and what's the best mechanism to do that? And do you want to spend your R and D recreating things or leveraging what's available and and innovating on top of it? It's all about ecosystems in the days where the single company can vertically integrate. I talked about him a complete end solution. You know those air few and far between. I think it's It's about collaboration and building ecosystems and operating within those. >>Yeah, it's it's It's such an interesting change. And one more thing again, to get your perspective, you run the data center group. But there's this little thing happening out there that we see growing in I o T Internet of things and the industrial Internet of things and edge computing. As we, you know, try to move more, compute and store and power, you know, kind of outside the pristine world of the data center and out towards where this data is being collected and processed when you've got latency issues and and in all kinds of reasons to start to shift the balance of where the computers aware that store Ah, and the reliance on the network. So when you look back from a storage perspective in your history in this industry and you start to see that basically everything is now going to be connected, generating data and and and a lot of it is even open source. I talked to somebody the other day doing, you know, kind of open source, computer vision on surveillance, you know, video. So, you know, the amount of stuff coming off of these machines is growing like crazy ways at the same time, you know, it can't all be processed at the data center. It can all be kind of shift back and then have you have a decision and then ship that information back out to. So when you sit back and look at the edge from your kind of historical perspective, what goes through your mind? What gets you excited? You know, what are some of the opportunities that you see that maybe the Lehman is not paying close enough attention to? >>Yeah, it's It's really an exciting time in storage. I get asked that question from time to time, having been in storage for more than 30 years, you know what was the most interesting time, and there's been a lot of them, but I wouldn't trade today's environment for any other in terms of just the velocity with which data is is evolving and how it's being used and where it's being used. You know that the TCO equation made describe what a data center looks like. But data locality will determine where it's located and we're excited about the edge opportunity. We see that as a pretty significant, meaningful part of the TAM. As we look out 3 to 5 years, certainly five G is driving much of that. I think just anytime you speed up the speed of the connected fabric, you're going to increase storage and increase the processing of the data. So the edge opportunity is very interesting to us. We think a lot of it is driven by low latency workloads. So the concept of envy any, um is very appropriate for that. We think in general SSD is deployed in in edge data centers defined as anywhere from a meter to a few kilometres from the source of the data. We think that's going to be a very strong paradigm. Um, the workloads you mentioned especially I O. T just machine generated data in general now I believe, has eclipse human generated data in terms of just the amount of data stored, and so we think that curve is just going to keep going in terms of machine generated data, much of that data is so well suited for zone story because it's sequential, it's sequentially written, it's captured, it's it has a very consistent and homogeneous lifecycle associated with it. So we think what's going on with with Zone storage in general and and Z and S and SMR specifically are well suited for where a lot of the data growth is happening. And certainly we're going to see a lot of that at the edge. >>Well, Phil, it's always great to talk to somebody who's been in the same industry for 30 years and is excited about today and the future on as excited as they have been throughout the whole careers. That really bodes well for you both. Well, for for Western Digital. And we'll just keep hoping the smart people that you guys have over there keep working on the software and the physics, Um, and then in the mechanical engineering to keep moving this stuff along. It's really ah, it's just amazing and just relentless. >>Yeah, it is. It is relentless. What's what's exciting to me in particular, Jeff is we've we've we've driven storage advancements, you know, largely through. As I said, a you know a number of engineering disciplines, and those are still going to be important going forward the chemistry of the physics, the electrical, the hardware capabilities. But I think, as you know, is widely recognized in the industry that it's a diminishing curve. I mean, the amount of energy, the amount of engineering, effort, investment, the cost and complexity of these products to get to that next capacity step, um, is getting more difficult, not less. And so things like zone storage where we now bring intelligent data placement to this paradigm is what I think makes this current juncture that we're at a very exciting >>right, Right. Well, it is applied ai, right. Ultimately, you're gonna have, you know, more more compute, you know, compute power. You know, driving the storage process and how that stuff is managed. And, you know, as more cycles become available and they're cheaper and ultimately compute, um gets cheaper and cheaper. You know, as you said, you guys just keep finding new ways to ah, to move the curve. And we didn't even get into the totally new material science, which is also, you know, come down the pike at some point in time. Well, >>very exciting. >>It's been great to catch up with you. I really enjoy the Western Digital story. I've been fortunate to to sit in on a couple chapters. So again, congrats to you. And, uh, we'll continue to watch and look forward to our next update. Hopefully, it won't be another four years. >>Okay. Thanks, Jeff. I really appreciate the time. All >>right. Thanks a lot. Alright. He's Phill. I'm Jeff. You're watching the Cube. Thanks for watching. We'll see you next time. Yeah, Yeah, yeah, yeah.

>> From theCube studios in Paloalto and Boston, connecting with thought leaders all around the world, this is a Cube conversation. >> Hi, I'm Stu Miniman, and welcome to this special Cube conversation. I'm coming to you from our Boston area studio, and theCube is really mostly about people, about network, and so we're going to have a focus in, we're going to talk about some technology, we're also going to talk a little bit about careers. I want to welcome to the program, I've got two first-time guests on the program. First, Mark Zbikowski. Probably butchered that badly, Mark, sorry, technical advisor, and Blue Gaston. Uh, Gaston. Boy, I'm doing horrible with names here. Software engineer, you're both with Polyverse. But, you know, my last name's Miniman, it has been butchered a million times. But Mark, and Blue, thank you so much for joining us. >> You're welcome. Our pleasure. >> Yes. >> All right. So one of you I've read a lot about online and the other one is Mark, go to the Wikipedia page, stuff like that. So we'll get to that too. So, Blue, maybe start with you, give us a little bit about your background. >> Yeah, so I work at Polyverse now, a cybersecurity startup. But actually I got my undergraduate degree in Philosophy, and from there, kind of just like, what am I going to do with a philosophy degree? And it just weirdly was like a natural transition. I was like, oh, computer science. And kind of the logical, like the technical version of philosophy. So got my master's in philosophy and now, or not philosophy, in computer science, and now have been working at Polyverse. I started as an intern and they hired me on, I think after a month, they were like, no, we want you full-time. So that was cool and I've loved it. So I'm starting off my story, that's kind of where my kick-off point is. >> Awesome. So, and Mark, first of all, you have to give us the connection between yourself and Blue, and a little bit surprising that she waited so long to go into the computer business. >> Uh, okay, I'm her stepfather. It's not surprising that she, you know, wanted to go into computer science. She's got lots of aptitude for it. She was just on a career path and an education path that was primarily logic, analysis, which is basically what we do in computer science. >> All right. So Mark, if you could just give our audience a little bit of a thumbnail sketch as to your background in the tech industry, and it's a storied one. >> Uh, okay. I was, I think, employee number 55 at Microsoft, when I started back in 1981. The first task that they gave me was to work on something that ended up becoming MS-DOS. I worked on MS-DOS for a long time, about five and a half years, worked on a number of other operating systems at Microsoft, ending up with being one of the initial development managers and architects for Windows. I was responsible for all file storage. And I was there for about 26 years. >> Yeah, you know, interesting, you know, when you look on the Wikipedia page, you were the third employee that reached the 25-year milestone. Some guy, Bill Gates, and Steve Balmer, were the first two to reach that milestone. So, you know, quite impressive. I think back, back when I learned computers, it was programming, and you know, today it's coding, and things are quite different there. But, Mark, you were also, you're noted as one of the early hackers there, so what does that mean to you, how have you seen that's been changing? Polyverse is in the cybersecurity realm, so would love your kind of viewpoint on just hacking in general. >> Oh, the early days, well my hacking started pretty much when I was in eighth or ninth grade back in Detroit. We had access to an academic operating system called MTS by way of Wayne State University. I grew up in, just in the suburbs of Detroit. And we had access to it, and for me Excuse me. Hacking at the time was all about trying to understand and learn stuff that was arcane and hidden and mysterious. Figuring out how, for example, password encryption algorithms worked, figuring out how operating systems worked, because at the time, there were very few organized textbooks about how to construct operating systems. Even though operating systems had been around for 20 years. So my early, earliest stuff was in basically, finding holes in security at MTS, and that's how I started, in what they would say "hacking", but it was very innocent, it was very, let's see what we can do! As opposed to, let's extract information, let's go and ransom people's data for bitcoin, which is, you know, I think, a wrong direction to go. >> Yeah. I'm curious your thoughts as the decades have progressed, you know, hacking today, what's your take on, you know, there's the white hats and the black hats, and everything in between. >> Uh, it's kind of an arms race. (laughs) Everything that the white hats will throw up, the black hats will eventually attack to some degree. Social engineering is sort of the ultimate way that people have been getting around, you know, software protections. I think it's unfortunate that there is such a financial reward to the black hat side of things, as counter to one's ethics. I think there's a lot of slippery slopes involved, in terms of, you know, boy, these companies shouldn't be making money, so I deserve my bit. I think that it's much better that, you know, people should come at this from an intellectual, you know, exploration standpoint, rather than an exploitative. But that's the nature of the world. >> Yeah, well, Blue, maybe we can help connect the dots towards what you both do at Polyverse. You mentioned you started as an intern, and I loved the article that talked about this. Well, you know, you're going to be an intern. Can you fix the internet for us? And you did some things to help, you know, help stop some of that malicious hacking. >> Yeah, I, that was crazy. I was very intimidated when I heard that, you're going to be fixing the internet. What I've been working at the company, which is different from our flagship product, but kind of in the same vein, is to stop malicious php javascript code execution. So that's what they came in, that's how they prefaced that problem to me. It was, you're going to go fix the internet. Um, and it was crazy. It was really cool and surprisingly, a lot of philosophy that goes into the way we look at our problem-solving at Polyverse, and how we tackle problems, but of course, I have my Jedi master Mark over here, and I was constantly, "What do you think about this? Isn't this crazy? "Like, look at how Polyverse is attacking this." And I think finally I broke him down, and I was like, come join. Come jump in, and you be the foresight, and you tell us what we're going to do in a year or two. And I convinced him, and now, he's, he's with us too. >> Excellent. So, Mark, tell us a little bit about, you know, more about Polyverse, your role there. In the industry there's a lot of talk about, you know, lots of money obviously gets spent on cybersecurity, but it's still a major challenge in the industry. So what's your role there and how's Polyverse helping to attack that? >> Well, my title is Technology Advisor, and I'm one of a small collection of people who have pretty wide-ranging expertise across operating systems, networks, compilers, languages, development tools, all of that. And our goal is, you know, my role, as well the other Jedi masters, is to take a look at what Polyverse is doing at present, try to figure out where we need to go, try to figure out what the next set of challenges are, use our broad experience and knowledge of the computing milieu, and try to figure out what are the tough issues we need to face? We make some progress on those tough issues, and then turn everything over for the mainline Polyverse development staff to bring it to reality. We're not like researchers, we're much more into the product planning side of things, but product planning in, I hate to use this word, but in a visionary sense. (Blue laughs) >> Yeah, no, it's-- >> We look for the vision. We're not visionaries. We look for the vision. >> You're a visionary, Mark. Admit it. >> Excellent. Well, I do love the, you know, Jedi analogy there. When you look at, I'm curious to your thoughts, both of you, you know, some of the real challenges and opportunities facing the cybersecurity industry. It's a large financial industry company, they'll spend a billion dollars and, you know, does that make them secure? Well, at least they've done what they can and they're pushing enough pieces. But, you know, fundamentally, we understand that this is such a huge issue. >> I think-- >> Blue? >> Well, (laughs) I can try to answer. I think Polyverse recognizes that as well. So we're trying to create new solutions, that instead of just being compliant and checking the boxes, we're actually trying to create systems and products that will stop attacks from actually working. Rather than being reactive and being responsive, we're trying to build these systems out where the attacks just don't work as they're currently designed. And I think we, you know, and to do so in an easy-to-deploy, time-saving kind of way is definitely our goal. Rather than the status quo and, you know, we're fighting inertia, we're trying to, to change that narrative in a really meaningful way. >> Thanks, Blue. Mark, do you have some comments you can add to that? >> Once we started taking individual computers and hooking them up to the internet, where they can communicate fairly freely with each other, and by intent communicate fairly freely with each other, by design, by intent, all of a sudden that opened us to just a wide range of malicious behavior, from being DoS'd, to leaking passwords, et cetera. There are, there's layers and layers that one can do to mitigate these problems. From IT operational manuals to buzz-testing your API, to best practices, it's a, there's a long list. And every bit, every piece of it is important. You need to secure your passwords before you can do anything else. You need to make sure that there's a firewall in your system be fore you go and start, before you even start thinking about doing things like, like what's goin on with what we're doing at Polyverse. It's a, like I said, there's a wide range of tools that people need, that people use, that people spend money on today. Polyverse has got a very unique perspective on how to go and extend this. We, it's a, it's very pragmatic, you know, the realization is that these attackers are going to keep attacking, and they're going to exploit certain features that, despite everyone's best intentions, aren't covered, and we have found a rather unique and novel way to prevent people from doing it. Is it going to solve everything? No. There's still, there's all these other early layers that need to be taken care of first, before the more sophisticated tools that, for example, that Polyverse has or that other companies have. >> Great. Well, Blue, you talked a little bit about it, but, you know, love your, what you've found, you know, working together as a family dynamic here. You know, specifically. >> Um, (laughs) I think it's really cool. What's the best, I'll say this, is when, I always like asking Mark his opinion, because why wouldn't I? The brain that guy has, and just the experience, he can add so much. Every once in a while, I'll go, and I'll say, you know, oh, this is what I'm working on, and here's what I'm kind of thinking, and he'll say, oh, yeah, well what about this? And I'll actually get to explain something to him. And I got to tell you, that feels really good. Is when I get to say, oh, well, actually it looks like this, and this was my plan, and he's like, oh yeah, definitely. And I get that validation, which is really cool. And I can, you know, drive to his house and bug him whenever I want to. I know where he lives, so if I'm really stuck, or just want to bounce ideas off of him, it's really cool. It's really cool, and I, you know, strong-armed, not strong-armed, I enticed him to come and join Polyverse just by the cool things that we're doing, and I think that's cool too. To now be able to work on something together. >> Yeah, and Mark, sounds like you're learning some things from Blue. Give us your side of that relationship. >> Well, it's a great relationship. Blue, um, Blue never hesitates to challenge. (Blue laughs) >> Blue: Okay. And that, I'm saying that in a very positive sense. Um, you know, she'll come up, every so often I'll get a text from her that says, "Help!" >> Oh my god! (laughing) >> Yes. Sorry. At least I'm not showing it. (laughs) But it's great. And we get together and we talk about stuff, and she says, you know, here's the problem I'm facing, and I'll ask her about it and she gets to go and teach me about what her problem is. I'm a big fan of teaching. I think one of the frustrations that Blue has is I almost never give her the answer when she asks a question. (laughs) >> Not even when I was in school, >> Yeah, not even when you were in school. I was always asking the questions and leading her to the answer rather than just giving it to her. >> Or saying, well why don't we sit down and I'll teach you how to implement knowledge. Just like, oh my god. What are you doing? >> Yeah. So, yeah, I'm a big fan of teaching and learning by way of teaching. One of the things I do is I'm an affiliate with the University of Washington, and I teach every year one quarter of their Operating Systems class. And I love teaching, I love seeing the light go on. But every year, when I'm teaching a class that I know pretty well, I learn something new. By a question the student asks, or by reading a paper that I'm asking the students to read, I learn something new just about every year. And so having Blue teach me is a way that I get to learn, but I think in the process Blue also gets to learn as well. You know, in the process of teaching me. >> Yeah, well, that's such a great point. All right, want to give you both the final word on what's exciting you, what draws you to working in the cybersecurity industry. >> Um, I'll start. (laughs) So when I started at Polyverse, I actually got to, as an intern, own my own product. And in, I think, less than a month now, we're actually officially releasing that product, polyscripting. Officially, like Marketing is coming up with materials for it, and that was right out of school is when I started on this project, so it's kind of like a big deal for me. You know, I've owned the project, I'd say like 90% of it, over the last year or two, and now I get to see it come into fruition. So that's really exciting to me. Um, you know, that's exciting. So I'm excited about that, I'm excited about what Polyverse is doing in general. So, yeah. >> And Mark? >> Yeah. It's great working in a startup, it's great working with a bunch of very, very bright, energetic people. For me, contributing to that environment is extremely valuable. Helping Polyverse out, they're, you know, cybersecurity is problem. Trying to come up with good, effective solutions that are really pragmatic in terms of, you know, we're not going to solve every problem, but here's a great little space that we're going to solve all the problems in. That's, there's a huge appeal to that for me. >> Well, Mark and Blue, thank you so much for joining. Appreciate you sharing some of the personal as well as the professional journeys that you've both been on. Thanks so much. >> Yeah, thank you >> Yeah, you're welcome. >> All right. Thank you for watching theCube. I'm Stu Miniman. Thanks for watching. (soothing music)

from around the globe it's the cube with digital coverage of Red Hat summit 2020 brought to you by Red Hat hi I'm Stu min a man and this is the cubes coverage of Red Hat summit 2020 of course this year the event is happening all online and that gives us an opportunity to meet with red hat executives customers partners and practitioners where they are around the globe in this segment one of our favorites ever years we're talking to the women in open source and joining me for this segment first of all we have Elissa and Alexander who is the executive vice president and chief people officer of Red Hat this award fit thunder her domain dallisa it is great to see you again thanks so much for joining us thank you so much for having us all right and we have two of the Award winners so first if you see right next bit Elissa we have an epic Sain who's a doctor and PhD candidate in clinical neuroscience at the University of Gothenburg coming to us from Sweden method great to see you thank you very much all right we also have Megan Burge Sinicki who is a manager of research and operations at the open source program office at Google Megan thank you so much for joining us off though thanks for having me all right so dallisa let me hand it off to you is give our audience a little bit if they're not familiar with whipping an open source what the initiative is the community and you know what might have changed from previous years when we've talked about this sure so we realized that the tech industry is a great industry for diverse populations but a lot of diverse populations don't realize that and so as the open source leader we wanted to shine a light on the contributions that some of our underrepresented populations are making an open source that trying to inspire more people to join communities to participate to contribute we know that more diverse populations help us to innovate more rapidly they help us to solve more problems and so it's really important especially today with what's happening in the world lots of important problems to solve that we really invite more of our other upper sort of populations to join in the communities awesome so absolutely there there are lots of people that volunteer there are lots of people that do it as their day job Megan why don't we fuck you have a roll open source first Google as a strong legacy and open source in general so tell us a little bit about you know what you were working on and what you're being recognized for here yeah well a lot of the recognition comes from my work with the Drupal Association I had been with Drupal for 8 years hoping to build that foundation in supporting that community and lots of different ways from fundraising to community events running sprints and helping with their developer tools and so that was a lot what the award was based on and now I'm at Google and I've been here for about a year and a half and I run their research and operations and so Google is an expression of open source and we have thousands of people using thousands of projects and we want to make sure they do it well they feel supported that we are good citizens in the projects that we participate in and so my group provides the operational support to make sure that happens you know you know what one of the things that's always fascinating when I go to Red Hat there's so many projects there's so many participants from various walks of life last year at the show there was a lot of discussion of you know it was a survey really and said that you know the majority of people that tribute now it's actually part of their job as opposed to when I think back you know you go back a couple of decades ago and it was like oh well in my spare time or down in my basement I'm contributing here so maybe talk a little bit about the communities and you know what what Megan is embodying CSUN she worked on project now she's working for obviously a good partner of Red Hat's that does a lot of open source yeah I love the way she described what her role is at Google and that it's fascinating and Google has been really a huge contributor in the community for in communities for years and years so I think that what we're seeing with the communities and people saying yeah now it's part of my day job is that you know 20 years ago the idea that open-source development would be kind of on par with proprietary development and on par in terms of being used in the enterprise and the data center was something that I think many people questioned proprietary software was the way that most people felt comfortable making sure that their intellectual property is protected and that users could feel comfortable using it within the parameters required so that was the way it was 20 years ago and then now you think about you know most companies there is some form of open source that is part of their infrastructure so now open source is no longer you know that disrupter but it's really a viable alternative and organizations really want to use both they want to have some propriety or they want to have some open sources so that means like every company is going to need to have some need to understand how to participate in communities how to influence communities and Red Hat's a great partner in helping enterprise customers to be able to understand what those red Nets might look like and then helping to kind of harden it make sure things that they need to have application city to have certified or certified and make it really usable in a way they're comfortable with in the enterprise that's kind of special Red Hat place but it's just a tribute to where we come in a world in terms of open source being really accepted and thriving and it helps us to innovate much more rapidly yeah and there's there's no better way to look at not only where we are but where we're going then talk about what's happening in the academic world so that gives it brings us Aneta so you are the academic award winner you're a PhD candidate so tell us a little bit about your participation and open source what it means to be part of this community my PhD project involves using virtual reality to measure the arm movements of people with stroke so we have participants coming in into our lab so they we're these 3d glasses and then they start seeing virtual objects in the 3d space and they use their hands to touch at these targets and make them disappear and we have all these movements data specially interpreters and then we write code and analyze the data and find out how much they have recovered within one year after stroke this is my PhD project but my involvement with open source happens they before like in starting from 2010 I have been editing Wikipedia and I have been writing several articles related to medicine and healthcare so that is where I started with open open knowledge and then I moved on words and after my medical studies I moved to research and worked on this awesome project and so there are multiple ways by which I have engaged with open source that's far that's awesome my understanding is also some of the roots that you had and some of the medical things that you're doing have an impact on what's happening today so obviously we're all dealing with the global pandemic in Koba 19 so I'd like to hear you know what your involvement there you know your data obviously is politically important that we have the right data getting to the right people as fast as possible definitely yes right now I'm working on writing creating content for Wikipedia writing on articles related to Kobe 19 so I mostly work on writing about its socio-economic impact writing about Kobe 19 testing and also about the disease in general mental health issues surrounding that social stigma associated began with it and so forth so I use all these high-quality references from the World Health Organization the United Nations and also from several journals and synthesize them and write articles on Wikipedia so we have a very cool project called wiki project code 19 on Wikipedia where people who are interested in writing articles creating data uploading images related to poet 19 come together and create some good content out of it so I am a very active participant there alright and making my understanding is you you also have some initiatives related to kovat 19 maybe you can tell us a little bit about those yeah well one I'm loosely affiliated with this kovat act now and that is a combination of developers data scientists epidemiologists and US state government officials and it's looking at how was the curve look like and how does that curve get flattened if governor's made decisions faster or differently than what they're making today and how does it impact the availability of ICU beds and ventilators and so that is a tool that's being used today by many decision-makers here in the US and my contribution to that was they needed some resources I reached into Google and found some smart generous volunteers that are contributing to the dataset and actually I just connected with Neda do this award program and now she's connected and is gonna start working on this as well yes oh that's fantastic yeah I mean dallisa you know we've known for a long time you want to move fast if you want to connect you know lots of diverse groups you know open sources is an important driver there what what else are you seeing in your group you know with your hat is the the people officer you know obviously this is a big impact not only on all of your customers partners but on fun Red Hatters themselves well it is a huge impact we're so fortunate that we have some experience working remotely we have about 25 percent of our population that historically works remotely so we have that as a foundation but certainly the quick move the rapid move to really thinking about our people first and having them work from home across the globe that is unprecedented and at this point we have some individuals who have been working from home for many many many week and others that are really in entering their fourth week so we're starting to have this huge appreciation for what it's like to work remotely and what we can learn about more effective inclusion so I think you know back to the idea of women and open source and diversity inclusion one of the things you may always prided ourself in is we focus on inclusion and we think about things like okay if the person is not in the room with their remote let's make sure for including them let's make sure they get to speak first etcetera well now we're learning what it's really like to be remote and for everyone to be remote and so we're creating this muscle as an organization I think most organizations are doing this right getting a muscle you didn't have before we really really having to think about inclusion in a different way and you're building a capability as an organization that you didn't have to appreciate those that are not in the room and to make sure they are included because no one's in the room you know we're really important pieces and dallisa you know one of the things that that's always great about Red Hat summit is you you bring together all these people as we just heard you know that your two Award winners here you know got connected through the awards so maybe give us a little bit of a peek as to what sort of things the community can still look forward to how they can continue to connect even though we're all going to be remote for this event yeah this event is is it going to be great event and I hope everyone joins us along our journey we are fortunate that Red Hat you know as the open source leader really wants to take a leadership position in thinking about how we can shine a light on opportunities for us to highlight the value of diversity and inclusion and so we've got a number of events not throughout the summit that we'd love people to join in and we're going to be celebrating our women and open-source again at our women's leadership community lunch is now not a lunch it is now a discussion unless you're having your lunch that you can check your desk but we're having a great conversation at that event I mean by people to join in and have a deeper conversation and also another look at our women in open source Award winners but these Award winners are just so amazing every year that applications that are submitted are just more and more inspiring and all the finalists were people that are so impressive so I love the fact that our community continues to grow and that they're more and more impressive people that are joining the community and that they're making those connections so that together we can you know really shine a light on the value that women bring to the communities and continue to inspire other underrepresented groups to join in and participate then a you know research obviously is an area where open-source is pretty well used but just give us a little bit of viewpoint from your standpoint yourself and your peers you know I would think from the outside that you know open sourced is just kind of part of the fabric of the tools that you're using is it something that people think specifically about a course or does it just come naturally that people are you know leveraging using and even contributing what what's available the tool I'm using is called cuteness it's an open source tool written in Python and so that gives me the possibility to have a look in deeper into the code and see what's actually inside for example I would like to know how what is the size of the target that is shown in the virtual space and I can fit know that correctly to the millimeters because it's available to me in open source so I think these are the advantages which researchers see when they have tools open-source tools and at the same time there's also a movement in Sweden and in most of Europe where they want the researchers are asking for publishing their articles in open access journals so they want most of their research be published as transparent as possible and there is also this movement where people want researchers want to have their data put in some open data city so that everybody can have a look at it and do analysis on the data and build up on that data if other people want to so there's a lot going from the open access side and knowledge side and also the open source side in the research community and I'm looking forward to what probably 19 will do to this movement in future and I am sure people will start using more more and more open-source tools because after the Manderly yeah making I'm curious from your standpoint when I think about a lot of these communities you know meetups are just kind of some of the regular fabric of how I get things done as well as you know just lots of events tie into things so when you're talking to your colleagues when you're talking to your peers out there how much is kind of the state of reality today having an impact in any any learnings that you can share with gaudí yeah that is definitely a challenge that we're going to figure out together and I am part of a group called Foss responders we are reaching out to projects and listening to their needs and amplifying their needs and helping to get them connected with resources and one of the top three areas of need include how do I run an online community event how do I replace these meetups and what is wonderful is that groups have been moving in this direction already and so who would release a guide of how they run online events and they provide some tooling as well but so has WordPress put out a guide and other projects that have gone down this path and so in the spirit of open source everyone is sharing their knowledge and Foss responders is trying to aggregate that so that you can go to their site find it and take advantage of it yeah definitely something I've seen one of the silver linings is you know these communities typically have been a lot of sharing but even more so everybody's responding everybody's kind of rallying to the cause don't want to give you the final word obviously you know this is a nice segment piece that we usually expect to see at Red Hat summit so what else do you want to help share where the community is final closing thoughts well I think that you know we're not done yet we have been so fortunate to be able to highlight you know the contributions that women make to open source and that is a honor that we get to take that role but we need to continue to go down this path we are not we're not done we have not made the improvement in terms of the the representative in our communities that will actually foster all of the improvements and all the solutions that need to happen in the world though we're going to keep down this pathway and really encourage everyone to think through how you can have a more inclusive team how you can make someone feel included if you're participating in a community or in an organization so that we really continue to bring in more diversity and have more innovation well excellent thank you so much Alisa for sharing it thank you too - both of you Award winners and really look forward to reading more online definitely checking out some of the initiatives that you've shared valuable pieces that hopefully everybody can leverage all right lots more coverage from Red Hat summit 2020 I'm Stu minimun and as always thank you for watching the cube [Music]

>>Live from San Diego, California It's the Q Covering Koopa and Cloud Native Cot Brought to you by Red Cloud. Native Computing Pounding and its ecosystem >>Welcome back, toe Gorgeous. SAN Diego, California This is Q Khan Cloud Native Khan. 29 years. I'm still minimum. My co host is John Troyer, and this is the end of three days water wall coverage over 12,000 and 10 d Having a welcome to the program. Cassie, Who's the executive director at the national headquarters for v. American Red Cross. Matt. Thank you, American Red Cross. And thank you so much for joining us. >>Yeah. Thanks for having me. Uh, it's been a great conference so far. Uh, you know, we're here to share our story where as an end user on our journey with Cloud native with kubernetes Andi how that helps Red Cross do what we do, which is help people in need a cz best we can every day. >>Eso no matter what industry I talked to, everybody's dealing with change. There's always more things happening. American Red Cross. I mean, you know, it feels like I hear American Red Cross mentioned Maur a cz Time goes on because you know everything from, you know, things related to climate through, you know, global events and the like. So maybe before we get into some of the tech, just give us, you know, you know your role there and how kind of the changing world impact your organization. >>Sure. So my role is to support a few different business units. One is biomedical marketing. We try to recruit blood donors, too. Give blood at Red Cross Blood Dot or GE and other channels. That's obviously a significant part of what we d'oh were major player in the blood supply market in the US we provide service is to the armed forces, you know, in that regard as well. So that's part of it. Part of it is I work with humanitarian service is group as well to recruit financial donors on recruit volunteers. That's primarily through Red Cross. That or GE a T least as faras my group goes on, then corporate brand marketing and chapter related marketing and communications. So all that happens through Red Cross that Oregon Red Cross blood dot organ some related platforms on those our flagship brand products. >>Okay, And what led to the American Red Cross being part of this cloud. Native computing, Yeah, system. >>Our journey is a lot like, you know, a lot of other folks. We had a very, you know, monolithic type of architecture. We had all of these different business units with the different priorities, different timelines, different needs wrapped up into one big monster of a platform that, you know, kind of bundled up risk for everybody in this one platform. And, uh, you know, we'd always have collisions of priorities, mostly not to mention the resource issues of who's gonna work, you know, on what? At what time. And so a few years ago, we started talking about breaking that down. And, um, we've been lucky to have some technical leaders that are very aware of and welcoming to new cloud native technologies. We decided at that time to pursue, you know, a cloud native architecture. And what we have today in a few years later, is two years worth of being in production with a platform that runs on Amazon. We take advantage of a lot of the native orchestration tools there for running our clusters. And we've been able to service, you know, those different needs in a much more nimble way. We can release something for a Red Cross blood dot or without risking much on the financial donation side or on the volunteer recruitment side. And likewise, you know, for those other groups, we can kind of separate out the risks for each of those groups. And that's that's been a great, great benefit. >>You've been on the the vendor side. The for profit side is I t very different at the non profit. If you're looking, people are looking down, you get >>higher. Yeah, You know, I have been doing it a long time, a lot of different perspectives. But I think you know what I tried to do. And I would. I think I've seen work best is when I t is not the ticket taker, you know, integrated with the business. I'm very fortunate to have some business partners at Red Cross that collaborate. You know, every day we're having conversations every day. We have some people on our team that feel as though they're accountable for business outcomes, not just, you know, doing cool technology things, you know, For example, you know, multiyear evolution of process related to being more agile. We've got so much more integration and communication with business teams have gone from, you know, something like one release every five months now due to a weak, you know, and I think we could do more. It's just we don't have the need to do more. Um, and that's a huge, huge, big lift. You know, there's a lot of conversations that need to happen. Should make that work. >>Yeah, it's all a journey, right? We're all we're all improved. Continuous improvement, but so follow up there. So as a 90 leader for a very large organization, you know, they're one of the things people are saying this year. Wow. The conference is big. So many new technologies. So many new company somebody open source projects. You know, you're in the middle of this journey. You can't screw it up, right? That would be disastrous. So how do you How do you How did you and your organization look at new technologies and pick out which do technologies to try and incorporate them into your stack and your portfolio? >>Right. So we wanted to be a cloud native. We wanted a do, um, you know, focus on projects that where we knew there were skills in the marketplace, uh, that we could acquire at our price point. You know, we try to be good stewards of donor dollars at the end of the day, you know, all the money we have comes from folks like you and you guys who support Red Cross, you know, and thank you very much for all that generous support. And so we try to spend that money, uh, you know, very carefully. Way have some people who are, uh, you know, employees on our team made about 25 or so. But one of the great things we've been able to do with some of these technologies now is we have a program called Code for Good. It's a volunteer work force where we're here recruiting volunteers with the skill set that you know, they have a day job, but they have an interest in supporting Red Cross. Uh, maybe not financially. Maybe not with their blood, but they can give us some time on their skills, and we run it like an open source project. We set out a road map of features for six months or so. We have planning sessions, we say. Listen, you know if you can sign up for a feature that because you have two hours this week to work. Great. You have six hours. Great. You just had a baby, and you're not available for three months. Fine. You know, we we wanna have a, you know, a bench of people that can self select based on their time commitment, what to work on. And somehow it's been been working Great. You know, we started this in June. We have about 30 volunteers now on. We've already delivered an app for slack. That is kind of a workplace app where you can, you know, if your organization works with us, you can donate right from slack. You can give a schedule of blood donation, appointment, do things like that. >>I love that model. It's something that, you know we've looked at years ago. That kind of micro participation, if you will. You know, You think it's like, you know, Wikipedia wouldn't have been built if it wasn't for everybody. Just spending a little bit of time on it. Uh, I'm curious. Does something like participating with you know, this ecosystem I have generalized tools that people know and can plug in with, as opposed to, you know, having to know your direct stack Is that helpful To kind of be able to recruit people into that environment? What? What are the kind of most needed skills on dhe usages that you're recruiting? >>It is. You are learning. Curve at this point is much smaller than it was on our previous platform Because of the fact that we're using technologies people are familiar with, um, you know, things like Docker we use a lot. We just started evaluating Prometheus, another C N C F project for monitoring some non proud systems. Hopefully that'll graduate into production systems. So from a technology standpoint, yes, yes, we find that, you know, the people we talked with can walk in and be productive sooner. You know, there's still the Red Cross specific things they need to know about how we do business. But, um, you know, at least at this point, is that and not some proprietary system that they also have to learn >>any learnings that you've had participating in the c m. D. F. With the rollout of the technologies that you share with your peers, >>you know, I love the sea. NCF is very maintainer driven, You know, uh, and and user driven. I heard today at one of the analyst panels. I did. I think maybe 30% of people here are end users. >>That's a pretty >>large number. Um, you know, the fact that we can come here and learn about technologies meet people, meat vendors meet some of the people contributing code. Um, it's a lot different than you know, maybe some some summit sponsored by a for profit vendor that wants to, uh, you know, generate leads and sell you things. It feels much more community driven here and open to lots of different perspectives. >>So now what you looking forward to in the next few years? Both in terms of your stack and maybe coming back >>to Cuba? Yeah, way. It's funny. We've started to see other parts of Red Cross come to us toe, learn about kubernetes because the vendors they work with are mentioning these things. And and we have been early adopters, as far as you know, where across goes our group. Um and I think it's great if we can expand usage of, um, cloud native technologies to other parts of the organization on really get some economies of scale. So that's part of what we're trying to do is kind of internal, uh, consulting knowledge sharing collaboration on then, as far as what we're doing on our team way. Just really want to focus on. We're on a stable point in the platform, and then we want to do some things around monitoring and alerting that. Reduce those incident outages, too. Nothing. Hopefully, um, and work on that. >>You're working on a few projects that are that are being worked on here for That >>s So you have this Prometheus project. Like I said, we're piloting that, uh, you know, I would say in four or five months time, we'll know if that's going to be something we can, you know, put some more investment into >>All right, that want to give you the final word. Red cross dot org's code for good. I believe. The web. >>Yes, yes. >>What else? >>Your code for the number four. Good on. You know, if you're interested in volunteering, we need technical skills. We need team leadership skills, product owner skills, eh? So it's not just about you know, developing features and ops engineers as well. So thanks for your time. I want to say hi to my daughter, Peyton. It's late on the East Coast, so go to bed now, but thanks, folks. >>All right. What? Well, Matt, and actually, that is the final word for our day one of coverage for John Troyer. I'm stupid. And be sure to join us tomorrow. We've got two more days water wall coverage here. Lots of great speakers. Really appreciate. We've got the end users on. And, Matt, thank you so much. And, you know, great mission. The code for good. We definitely hope that the community here, you know, reaches out in connection Participates s Oh, that's it for today. Fixes all for watching.