>>Hello everyone and welcome back to Las Vegas. I've got my jazz hands because I am very jazzed to be here at AWS Reinvent Live from the show floor all week. My name is Savannah Peterson, joined with the infamous John Farer. John, how you feeling >>After feeling great? Love? What's going on here? The vibe is a cloud, cloud native. Lot of security conversation, data, stuff we love Cloud Native, >>M I >>A L, I mean big news. Security, security, data lake. I mean, who would've thought Amazon have a security data lake? You know, e k s, I mean >>You might have with that tweet you had out >>Inside outside the containers. Reminds me, it feels like coan here. >>It honestly, and there's a lot of overlap and it's interesting that you mention CubeCon because we talked to the next company when we were in Detroit just a couple weeks ago. Teleport E is the CEO and founder F Welcome to the show. How you doing? >>I'm doing well. Thank you for having me today. >>We feel very lucky to have you. We hosted Drew who works on the product marketing side of Teleport. Yeah, we got to talk caddies and golf last time on the show. We'll talk about some of your hobbies a little bit later, but just in case someone's tuning in, unfamiliar with Teleport, you're all about identity. Give us a little bit of a pitch, >>Little bit of our pitch. Teleport is the first identity native infrastructure access platform. It's used by engineers and it's used by machines. So notice that I used very specific choice of words first identity native, what does it mean? Identity native? It consists of three things and we're writing a book about those, but I'll let you know. Stay >>Tuned on that front. >>Exactly, yes, but I can talk about 'em today. So the first component of identity, native access is moving away from secrets towards true identity. The secrets, I mean things like passwords, private keys, browser cookies, session tokens, API keys, all of these things is secrets and they make you vulnerable. The point is, as you scale, it's absolutely impossible to protect all of the seekers because they keep growing and multiplying. So the probability of you getting hacked over time is high. So you need to get rid of secrets altogether that that's the first thing that we do. We use something called True Identity. It's a combination of your biometrics as well as identity of your machines. That's tpms, HSMs, Ubikes and so on, so forth. >>Go >>Ahead. The second component is Zero Trust. Like Teleport is built to not trust the network. So every resource inside of your data center automatically gets configured as if there is no perimeter it, it's as safe as it was on the public network. So that's the second thing. Don't trust the network. And the third one is that we keep access policy in one place. So Kubernetes clusters, databases on stage, rdp, all of these protocols, the access policy will be in one place. That's identity. Okay, >>So I'm, I'm a hacker. Pretend I'm a hacker. >>Easy. That sounds, >>That sounds really good to me. Yeah, I'm supposed to tell 'em you're hacker. Okay. I can go to one place and hack that. >>I get this question a lot. The thing is, you want centralization when it comes to security, think about your house being your AWS account. Okay? Everything inside your furniture, your valuable, like you'll watch collection, like that's your data, that's your servers, paper clusters, so and so forth. Right Now I have a choice and your house is in a really bad neighborhood. Okay, that's the bad internet. Do you wanna have 20 different doors or do you want to have one? But like amazing one, extremely secure, very modern. So it's very easy for you to actually maintain it and enforce policy. So the answer is, oh, you probably need to have >>One. And so you're designing security identity from a perspective of what's best for the security posture. Exactly. Sounds like, okay, so now that's not against the conventional wisdom of the perimeter's dead, the cloud's everywhere. So in a way kind of brings perimeter concepts into the posture because you know, the old model of the firewall, the moat >>It Yeah. Just doesn't scale. >>It doesn't scale. You guys bring the different solution. How do you fit into the new perimeters dead cloud paradigm? >>So the, the way it works that if you are, if you are using Teleport to access your infrastructure, let's just use for example, like a server access perspective. Like that machine that you're accessing doesn't listen on a network if it runs in Teleport. So instead Teleport creates this trusted outbound tunnels to the proxy. So essentially you are managing devices using out going connection. It's kind of like how your phone runs. Yeah. Like your phone is actually ultimate, it's like a teleport like, like I It's >>Like teleporting into your environment. >>Yeah, well play >>Journal. But >>Think about actually like one example of an amazing company that's true Zero trust that we're all familiar with would be Apple. Because every time you get a new iOS on your phone, the how is it different from Apple running massive software deployment into enormous cloud with billions of servers sprinkle all over the world without perimeter. How is it possible That's exactly the kind of technology that Teleports >>Gives you. I'm glad you clarified. I really wanted to get that out on the table. Cuz Savannah, this is, this is the paradigm shift around what an environment is Exactly. Did the Apple example, so, okay, tell 'em about customer traction. Are people like getting it right away? Are their teams ready? Are they go, oh my god this is >>Great. Pretty much you see we kinda lucky like in a, in a, like in this business and I'm walking around looking at all these successful startups, like every single one of them has a story about launching the right thing at just the right like moment. Like in technology, like the window to launch something is extremely short. Like months. I'm literally talking months. So we built Teleport started to work on it in like 2015. It was internal project, I believe it or not, also a famous example. It's really popular like internal project, put it on GitHub and it sat there relatively unnoticed for a while and then it just like took off around 2000 >>Because people start to feel the pain. They needed it. Exactly, >>Exactly. >>Yeah. The timing. Well and And what a great way to figure out when the timing is right? When you do something like that, put it on GitHub. Yeah. >>People >>Tell you what's up >>Yeah's Like a basketball player who can just like be suspended in the air over the hoop for like half the game and then finally his score and wins >>The game. Or video gamer who's lagged, everyone else is lagging and they got the latency thing. Exactly. Thing air. Okay. Talk about the engineering side. Cause I, I like this at co con, you mentioned it at the opening of this segment that you guys are for engineers, not it >>Business people. That's right. >>Explain that. Interesting. This is super important. Explain why and why that's resonating. >>So there is this ongoing shift on more and more responsibilities going to engineers. Like remember back in the day before we even had clouds, we had people actually racking servers, sticking cables into them, cutting their fingers, like trying to get 'em in. So those were not engineers, they were different teams. Yeah. But then you had system administrators who would maintain these machines for you. Now all of these things are done with code. And when these things are done with code and with APIs, that shifts to engineers. That is what Teleport does with policy. So if you want to have a set of rules that govern who or what and when under what circumstances can access what data like on Kubernetes, on databases, on, on servers wouldn't be nice to use code for it. So then you could use like a version control and you can keep track of changes. That's what teleport enables. Traditionally it preferred more kind of clicky graphical things like clicking buttons. And so it's just a different world, different way of doing it. So essentially if you want security as code, that's what Teleport provides and naturally this language resonates with this persona. >>Love that. Security is coding. It's >>A great term. Yeah. Love it. I wanna, I wanna, >>Okay. We coined it, someone else uses it on the show. >>We borrow it >>To use credit. When did you, when did you coin that? Just now? >>No, >>I think I coined it before >>You wanted it to be a scoop. I love that. >>I wish I had this story when I, I was like a, like a poor little 14 year old kid was dreaming about security code but >>Well Dave Ante will testify that I coined data as code before anyone else but it got 10 years ago. You >>Didn't hear it this morning. Jimmy actually brought it back up. Aws, you're about startups and he's >>Whoever came up with lisp programming language that had this concept that data and code are exact same thing, >>Right? We could debate nerd lexicon all day on the cube. In fact, that could even be a segment first >>Of we do. First of all, the fact that Lisp came up on the cube is actually a milestone because Lisp is a very popular language for object-oriented >>Grandfather of everything. >>Yes, yes, grandfather. Good, good. Good catch there. Yeah, well done. >>All right. I'm gonna bring us back. I wanna ask you a question >>Talking about nerd this LIS is really >>No, I think it's great. You know how nerdy we can get here though. I mean we can just hang out in the weeds the whole time. All right. I wanna ask you a question that I asked Drew when we were in Detroit just because I think for some folks and especially the audience, they may not have as distinctive a definition as y'all do. How do you define identity? >>Oh, that's a great question. So identity as a term was, it was always used for security purposes. But most people probably use identity in the context of single signon sso. Meaning that if your company uses identity for access, which instead of having each application have an account for you, like a data entry with your first name, last name emails and your role. Yeah. You instead have a central database, let's say Okta or something like that. Yep. And then you, you use that to access everything that's kind of identity based access because there is a single source of identity. What we say is that we, that needs to be extended because it it no longer enough because that identity can be stolen. So if someone gets access to your Okta account using your credentials, then they can become you. So in order for identity to be attached to you and become your true identity, you have to rely on physical world objects. That's biometrics your facial fingerprint, like your facial print, your fingerprints as well as biometric of your machine. Like your laptops have PPM modules on it. They're absolutely unique. They cannot be cloned stolen. So that is your identity as well. So if you combine whatever is in Octa with the biker chip in this laptop and with your finger that collectively is your true identity, which cannot be stolen. So it's can't be hacked. >>And someone can take my finger like they did in the movies. >>So they would have to do that. And they would also have to They'd >>Steal your match. Exactly, exactly. Yeah. And they'd have to have your eyes >>And they have to, and you have >>Whatever the figure that far, they meant what >>They want. So that is what Drew identity is from telecom and >>Biometric. I mean it's, we're so there right now it's, it's really not an issue. It's only getting faster and better to >>Market. There is one important thing I said earlier that I want to go back to that I said that teleport is not just for engineers, it's also for machines. Cuz machines they also need the identity. So when we talk about access silos and that there are many different doors into your apartment, there are many different ways to access your data. So on the infrastructure side, machines are doing more and more. So we are offloading more and more tasks to them. That's a really good, what do machines use to access each other? Biome? They use API keys, they use private keys, they use basically passwords. Yeah. Like they're secrets and we already know that that's bad, right? Yeah. So how do you extend biometrics to machines? So this is why AWS offers cloud HSM service. HSM is secure hardware security module. That's a unique private key for the machine that is not accessible by anyone. And Teleport uses that to give identities to machines. Does do >>Customers have to enable that themselves or they have that part of a Amazon, the that >>Special. So it's available on aws. It's available actually in good old, like old bare metal machines that have HSMs on them on the motherboard. And it's optional by the way Teleport can work even if you don't have that capability. But the point is that we tried, you >>Have a biometric equivalent for the machines with >>Take advantage of it. Yeah. It's a hardware thing that you have to have and we all have it. Amazon sells it. AWS sells it to us. Yeah. And Teleport allows you to leverage that to enhance security of the infrastructure. >>So that classic hardware software play on that we're always talking about here on the cube. It's all, it's all important. I think this is really fascinating though. So I had an on the way to the show, I just enrolled in Clear and I had used a different email. I enrolled for the second time and my eyes wouldn't let me have two accounts. And this was the first time I had tried to sort of hack my own digital identity. And the girl, I think she was humoring me that was, was kindly helping me, the clear employee. But I think she could tell I was trying to mess with it and I wanted to see what would happen. I wanted to see if I could have two different accounts linked to my biometric data and I couldn't it, it picked it up right away. >>That's your true >>Identity. Yeah, my true identity. So, and forgive me cuz this is kind of just a personal question. It might be a little bit finger finger to the wind, but how, just how much more secure if you could, if you could give us a, a rating or a percentage or a a number. How much more secure is leveraging biometric data for identity than the secrets we've been using historically? >>Look, I could, I played this game with you and I can answer like infinitely more secure, right? Like but you know how security works that it all depends on implementation. So let's say you, you can deploy teleport, you can put us on your infrastructure, but if you're running, let's say like a compromised old copy of WordPress that has vulnerability, you're gonna get a hack through that angle. But >>Happens happens to my personal website all the time. You just touched Yeah, >>But the fact is that we, I I don't see how your credentials will be stolen in this system simply because your TPM on your laptop and your fingerprint, they cannot be downloaded. They like a lot of people actually ask us a slightly different question. It's almost the opposite of it. Like how can I trust you with my biometrics? When I use my fingerprint? That's my information. I don't want the company I work at to get my fingerprint people. I think it's a legit question to ask. >>Yeah. And it's >>What you, the answer to that question is your fingerprint doesn't really leave your laptop teleport doesn't see your fingerprint. What happens is when your fingerprint gets validated, it's it's your laptop is matching what's on the tpm. Basically Apple does it and then Apple simply tells teleport, yep that's F or whoever. And that's what we are really using. So when you are using this form authentication, you're not sharing your biometric with the company you work at. >>It's a machine to human confirmation first and >>Then it's it. It's basically you and the laptop agreeing that my fingerprint matches your TPM and if your laptop agrees, it's basically hardware does validation. So, and teleport simply gets that signal. >>So Ed, my final question for you is here at the show coupon, great conversations there for your company. What's your conversations here like at reinvent? Are you meeting with Amazon people, customers? What are some of the conversations? Because this is a much broader, I mean it's still technical. Yep. But you know, a lot of business kind of discussions, architectural refactoring of organizations. What are some of the things that you're talking about here with Telepo? What are, >>So I will mention maybe two trends I observed. The first one is not even security related. It's basically how like as a cloud becomes more mature, people now actually at different organizations develop their own internal ways of doing cloud properly. And they're not the same. Because when cloud was earlier, like there were this like best practices that everyone was trying to follow and there was like, there was just a maybe lack of expertise in the world and and now finding that different organizations just do things completely different. Like one, like for example, yeah, like some companies love having handful, ideally just one enormous Kubernetes cluster with a bunch of applications on it. And the other companies, they create Kubernetes clusters for different workloads and it's just like all over the map and both of them are believed that they're doing it properly. >>Great example of bringing in, that's Kubernetes with the complexity. And >>That's kind of one trend I'm noticing. And the second one is security related. Is that everyone is struggling with the access silos is that ideally every organization is dreaming about a day, but they have like one place which is which with great user experience that simply spells out this is what policy is to access this particular data. And it gets a automatically enforced by every single cloud provider, but every single application, but every single protocol, but every single resource. But we don't have that unfortunately Teleport is slowly becoming that, of course. Excuse me for plugging >>TelePro. No, no worries. >>But it is this ongoing theme that everyone is can't wait to have that single source of truth for accessing their data. >>The second person to say single source of truth on this stage in the last 24 >>Hours or nerds will love that. I >>Know I feel well, but it's all, it all comes back to that. I keep using this tab analogy, but we all want everything in one place. We don't wanna, we don't wanna have to be going all over the place and to look for >>Both. Because if it's and everything else places, it means that different teams are responsible for it. Yeah. So it becomes this kind of internal information silo as well. So you not even, >>And the risks and liabilities there, depending on who's overseeing everything. That's awesome. Right? So we have a new challenge on the cube specific to this show thing of this as your 30 minute or 30 minute that would be bold. 32nd sizzle reel, Instagram highlight. What is your hot take? Most important thing, biggest theme of the show this year. >>This year. Okay, so here's my thing. Like I want cloud to become something I want it to be. And every time I come here and I'm like, are we closer? Are we closer? So here's what I want. I want all cloud providers collectively to kind of merge. So then when we use them, it feels like we are programming one giant machine. Kind of like in the matrix, right? The movie. So like I want cloud to feel like a computer, like to have this almost intimate experience you have with your laptop. Like you can like, like do this and the laptop like performs the instructions. So, and it feels to me that we are getting closer. So like walking around here and seeing how everything works now, like on the single signon on from a security perspective, there is so that consolidation is finally happening. So it's >>The software mainframe we used to call it back in 2010. >>Yeah, yeah. Just kind of planetary scale thing. Yes. It's not the Zuckerberg that who's building metaverse, it's people here at reinvent. >>Unlimited resource for developers. Just call in. Yeah, yeah. Give me some resource, spin me up some, some compute. >>I would like alter that slightly. I would just basically go and do this and you shouldn't even worry about how it gets done. Just put instructions into this planetary mainframe and mainframe will go and figure this out. Okay. >>We gotta take blue or blue or red pill. I >>Know. I was just gonna say y'all, we are this, this, this, this segment is lit. >>We got made tricks. We got brilliant. We didn't get super cloud in here but we, we can weave that in. We got >>List. We just said it. So >>We got lisp. Oh great con, great conversation. Cloud native. >>Outstanding conversation. And thank you so much for being here. We love having teleport on the show. Obviously we hope to see you back again soon and and Drew as well. And thank all of you for tuning in this afternoon. Live from Las Vegas, Nevada, where we are hanging out at AWS Reinvent with John Furrier. I'm Savannah Peterson. This is the Cube. We are the source for high tech coverage.

(upbeat music) >> Welcome back to New York City everybody. You're watching theCUBE's coverage of MongoDB World 2022. My name is Dave Vellante. Pretty good attendance here. I'd say over 3000 people, great buzz, a lot of really technical sessions. There's an executive session going on. There's a financial analyst session. So a lot of diversity in this attendee base. Vadim Supitskiy is here. He's the CTO of Forbes and Abdul Razack is the vice president of Solution Engineering at Google. Gents, thanks for coming on theCUBE. >> Thanks Dave. >> Happy to be here. >> So, Forbes, very interesting business. I'm interested in what occurred during the pandemic for you guys. Right? Everybody went digital. Obviously you guys have a tremendous brand. We all, in the business world reaped from it, but what happened during the isolation era? What happened to your business? >> Yeah, so we've been innovating and going through digital transformation for years, since we launched our website probably 25 years ago. >> But during the pandemic, because of our coverage, our foresight to create a breaking news team, our audiences and readership really skyrocketed. >> Really? >> Yeah, and at that point, we were very happy and really lucky to be in Google Cloud and MongoDB Atlas. So when the audiences went up, we didn't feel any impact, right? Our environments auto-scaled and our users didn't experience any issues at all. So we were able to focus on innovation, our users loyalty and really building cool products. So we were very lucky and happy to be in Google Cloud and MongoDB Atlas. >> So Abdul, the solution and the title you provided, obviously worked. How did you guys end up getting together? What was that like? >> Yeah, I mean, like Vadim said, maybe there's a little bit of the right place at the right time in this case, but you can see the need for digital transformation and the pandemic really accelerated that. And like Vadim said, primarily Forbes wanted to focus on innovation and customer loyalty and the way that comes to bear, is that you have a technology platform that can serve those needs. Right? Whether it is through unique applications that can be delivered, the ability for developers to build those applications quickly and seamlessly and then remove the intangibles of scalability, performance, latency, and things of that nature. So, you can see this all coming together in this scenario. >> So as consumers, we see the website, we read online, maybe sometimes in the laptop, mostly on mobile. What is it that we don't see? I mean, the apps that Abdul talked about, community. What else is there? Paint a picture of that for us. >> Yeah. There is a lot going on behind the scene. Right? So focusing on audience, building communities, but also what it allowed us to do while everything was working well, we were scaling up. Right? We were able to focus on a lot of innovation. And one of those was first-party data platform that we built. We call it Forbes One. And that's in the center of everything that we do at Forbes right now. Right? So it allows us, one, to connect our partners, advertising partners with the audiences that they're looking to engage and to connect with. And then we are growing our consumer business as well and what that allows us to do is target the right products at the right time, to the right people, on the web website and our domain. So, that's just one of the examples that we've built our full first-party data platform on these technologies and we now know our customers so well that we are able to provide them with what they want. >> So the first-party data platform is what? A self-serve for advertiser, so they can identify? >> Not just advertisers. So it's in the center of everything. So advertiser comes in, we provide the segments and users that they want to reach. Now, we are creating products as well, building cool, innovative products and offering our journalism and everything there to our readers and we are able to connect them to the right audiences at the right time, as well as personalization. Right? You come onto the website, you want to read what you want to read. So we able to create that as well, using machine learning and AI. >> So a product, it might be a data product or it might be a content product? >> It could be a data product. It could be like just personalization or something like that. It could be newsletter. Right? It could be a stand-alone product, like investing product. So, there is a lot going on there, but we want to offer the right ones at the right time, to the right audiences and building that platform has allowed us to do that. >> Okay. Now Google's got great tech. What's the tech behind all this? >> Yeah. So when Vadim talked about segmenting to personalize something that is relevant to you and providing recommendations to you. Right? And all that is based on machine learning, AI technology. The fact that Vadim has all the data curated in a in a first-party data platform gives the ability to create a seamless profile. Right? You could be interested in a couple of products. Right? And then the underlying technology can tailor that to bring what is it that you're looking for at the right place at the right time. Right? So those are recommendations, things of that nature that's all powered by AI and machine learning technology. >> So it's running on Mongo, and then you're bringing in Google AI and machine intelligence tools? Can you double click on that? >> Yeah. It's basically a combination of both, using both platform to the deploy it and we embrace Cloud. Right? So we using all the Cloud native technologies. Right? We didn't want to just lift and shift. We wanted to make sure we do it right. And we focused on automation, even if we had to take a step back, we knew that automating things was a key for us. So yes, it's been really successful, but also really informative for us to use the right tools for the job. >> And you had prior experience with Mongo, or? >> We did. >> What's your journey been like there? >> Yeah, we actually were one of the first clients of Mongo. I think we were number 11 at that time. >> 10gen. >> Yes. It was. >> We remember. >> Many years ago it was MongoDB one, right? >> Yeah. >> And at that time we we introduced contributor network for us and our audiences were scaling as well. And we used Out-of-the-Box WordPress as our publishing platform, which couldn't scale. So we had to rethink and figure out, "Alright, so what do we do?" We compared couple of no SQL databases and Mongo was a winner because they checked all the boxes and developers loved it right away. Right? They're like, "All right, this is so much faster to develop on. It's just a great tool for the job going from SQL to, to no SQL". And we scaled and we never looked back. And then obviously Atlas came, so there are kind of two inflection points here. One switch into no SQL and two going away from managing databases. Like we don't want to be in that business. Right? Updates, patches, all of that, that we had to do manually, over-provision in our environments and kind of wasteful. So being on Atlas, that was a second kind of inflection point for us, which opened it up for us to do even more innovation and move faster. >> Okay. And you're happy about this partnership, despite, I mean, you partner with Mongo obviously, Google has its own databases, that's just the nature of the world we live in, isn't it? >> No and fundamentally like that, we always believe that customer choice is the primary notion. Right? I mean, and Google Cloud platform is more of a platform and the ecosystem is critical to that. Right? It's imperative. So, like Vadim said, the combination of Google and Mongo provides a truly Cloud native platform that can serve the needs for years to come, rather than from looking at it from a legacy perspective. And that's the way we look at it. Right? I mean, there is choices all the time and sometimes it's competition. >> Yeah. Yeah. And you're still selling a lot of compute and storage and machine intelligence, so machine learning. This morning in the keynotes, we heard a lot about a lot of different capabilities. We've certainly watched Mongo evolve its platform over the last half a decade or more really. But you've mentioned the developers loved it. Right? As Mongo evolves its platform, is there trade off from a developer simplicity standpoint? Are they able to preserve that from your perspective? >> I think with Atlas, it actually makes it easier now. So when they need to create an environment, they can do it on the fly. When they need to test something, also things available to them right away. So it actually, in general, as the platform becomes more mature and more stable, which is very important, but at the same time, the flexibility remains for development and for creation of environments and things like that. So we've been pretty happy with how it transitioned, to being a more mature platform. >> Did the move to Google Cloud and Atlas change the way that you're able to deliver high availability versus what you were doing when you were self-managing? Can you talk about that a little bit? >> Yeah, absolutely. We were in a data center, so kind of one location and moving to MongoDB Atlas and Google Cloud, now we're multi region. Right? So we have a full DR strategy and we feel a lot more secure and we feel very confident that anything that happens, we can scale, we can fail over. So absolutely, this helps us a lot. And the feature that was introduced probably a few years ago to auto scale MongoDB environments as well, that has been really key for us, so we can sleep well. >> Meaning you can scale while you sleep. >> Right, exactly. Exactly. >> Yeah. Plus the other part is you don't size for peak. right? You size as you grow, and then you, you have that elasticity built in. Right? That it is the nature. And then Mongo is available on multiple Google Cloud regions. So as you expand, you don't worry about all the plumbing that you need to do and things of that nature. >> They asked us serverless this morning. >> Yeah. >> How does that affect what you guys are doing together and what are your thoughts from Google's perspective and then of course, from Forbes? >> And that's the trend that we see constantly. Right? Serverless really decouples the tie to the VMs. Right? And so it makes it much more easier to provide the elasticity and have function calls across. Right? Function as a service and things of that nature. Right? So we see a lot of promise in that. Right? We do that even within our own products and we see that giving the ability to decompose and recompose applications and would love to hear how you're leveraging that. Right? >> Yeah. We fully embrace serverless. So we use all the tools you provide, I think. If you look at our architectural diagrams, it's like all these pops-up, cloud functions, composer, app engine. So we use the full suite and we love it. >> Yeah, Yeah. Okay. And then you talked this morning about the eliminating, the trade-offs with serverless of having to either when you dial it down You have to restart, but you've solved that problem, or I guess Mongo's helped you solve that problem. Can you explain that a little bit from a technical standpoint? >> Yeah. From a technical standpoint, if you look at, like as a developer, right? If you're building an intelligent app, it has multiple components within it. Right? There is pops-up for messaging, there is cloud functions and things of that nature. So you don't worry about, when it's encompassed in a serverless architecture, you don't worry about a lot of the complexities that go on behind it and so that makes the abstraction much more easier. And it eliminates the friction that a developer goes through. I think they've talked about removing friction and that's the primary source of productivity loss, which is the friction. We used to come from a world where developers were more worried. 80% of the time they would spend on plumbing this thing and then only 20% writing code. Right? And then now this whole paradigm should flip that. Right? That's where we see the promise of it. >> Do you still do stuff on Prem or are you pretty much all in the Cloud? >> Fully in the Cloud. >> How long did that take? What was that like? >> It actually was really fast. We had a real aggressive timeline. It took us six months. >> Really? >> Yeah. Yeah. And it was aggressive, but I was happy that we did it in a short period of time. >> And what was the business impact that you saw moving to the Google Cloud? >> Yeah, so obviously after we moved to the Cloud, we wanted to measure, especially the first year, how it affected us and what were the positives out of it. And yeah, we've seen tremendous results. 58% increase in speed to market. We were releasing four times more often than when we were on Prem. We saw 73% increase in initiatives delivered and while our velocity was scaling up, we also saw 30% decreased in hot fixes and rollbacks. So it became more stable while we scaled up the velocity and obviously very happy with those results. >> Wow. Do you golf? >> I don't actually. >> Do you golf? >> No. I watch golf. >> I used to watch. Okay. Do you know what a mulligan is? >> Yeah. >> Okay. mulligan is like a do-over right. If you had a mulligan, would you do anything differently? >> You know, we learned a lot and one of the keys for me was definitely automate everything, make sure that you automate as much as possible, even if it slows you down because in the future that will help you so much and use the platform and the tools that available to you. So, serverless. Right? Use Cloud the way it's supposed to be, as much as possible and I think that's the advice I would give. >> Are there any cautions with regard to automation, either of you that you see? I mean because sometimes automation brings unintended consequences and "Oops" happens really fast. >> Yeah. It's a little bit of a process. Right? If you take a step back, right, and typically what people tend to do is, there is a standardization process and once it's standardized, the next step is you gain efficiencies by automation. Right? In this whole thing, what is underestimated is change management. And we see a lot of room for improvement around educating on change management, getting ahead of that so that you can see what is coming. So that the organization moves across that. I don't know if you saw that in your case, but we see this predominantly in other other cases. >> Yeah. I mean, for us, we wanted to make sure that all the testing was in place and things like that. So not just automation of deploying or anything like that, but make sure that there is something there to catch if something goes wrong and roll back and things like that. So you want to make sure that you protected in many areas. >> So square this circle for me, because especially with COVID, so many unknowns and one of the benefits of document database is you're not tied into a schema. You got a flexible schema. Okay. So you're changing, you can change things much more easily. So when you talk about standardization, you're talking about standardizing, what at the infrastructure layer, or where does that standardization occur? Where should it occur. >> I mean, you could have it at the business process level. >> Okay. >> You could have it at the infrastructure level. You could also have it on the administration aspect of it. So there are three areas where you could apply automation to. >> So is there an analog to flexible schema at the business process level? Is that kind of how to think about it, whereas I'm not locked into a business process schema? I have to build flexibility into that as I change my? >> No, I mean, you can apply it any which way. I mean, I don't think the schema matters so much. Right? Like, for example, if you take the Forbes US case. Right? There is content curation, for example. Right? >> Yeah, okay. >> You could take content curation. Content curation in the previous world, like in the WordPress world, was not very flexible. Right? Like that it wouldn't scale. And now you are in a world where you have a very flexible schema, but the process of curating the content can be standardized. Right? And then the next step of that is to automate that. Right? And so you could apply it in any manner if you will. >> So have you built a custom CMS? Is that what you've done there? >> Yeah. We built our own custom CMS. It's AI powered. We want to make our journalist lives easier. So we're constantly trying to figure out what can we give them to make their day-to-day job much easier. >> So the machines can curate and find the best content. >> We do recommend things. Yes, absolutely. We curate, we tell them what would be the best headline, for example, what would >> Prior to them publishing? >> Yeah. Yeah. What would be the better keywords to include and things like that, what images. Just recommendations. >> And you can automate the insertion of those WordPress to go every time they do, even though they're writing about the same topic. >> It's a recommendation process obviously, but >> There is a human intelligence to that at the end. Right? I mean, but you can create a much more informed view by curating and recommending content rather than a myopic view. >> And you're eliminating that mundane keystroke task. Wow. Amazing story guys. Thanks so much for sharing. >> Absolutely. >> All right. Keep it right there. We're live from MongoDB World 2022 in New York city. Be right back. (upbeat music)

(upbeat music) >> When Oracle acquired Sun in 2009, it paid $5.6 billion net of Sun's cash and debt. Now I argued at the time that Oracle got one of the best deals in the history of enterprise tech, and I got a lot of grief for saying that because Sun had a declining business, it was losing money, and its revenue was under serious pressure as it tried to hang on for dear life. But Safra Catz understood that Oracle could pay Sun's lower profit and lagging businesses, like its low index 86 product lines, and even if Sun's revenue was cut in half, because Oracle has such a high revenue multiple as a software company, it could almost instantly generate $25 to $30 billion in shareholder value on paper. In addition, it was a catalyst for Oracle to initiate its highly differentiated engineering systems business, and was actually the precursor to Oracle's Cloud. Oracle saw that it could capture high margin dollars that used to go to partners like HP, it's original exit data partner, and get paid for the full stack across infrastructure, middleware, database, and application software, when eventually got really serious about cloud. Now there was also a major technology angle to this story. Remember Sun's tagline, "the network is the computer"? Well, they should have just called it cloud. Through the Sun acquisition. Oracle also got a couple of key technologies, Java, the number one programming language in the world, and MySQL, a key ingredient of the LAMP stack, that's Linux, Apache, MySQL and PHP, Perl or Python, on which the internet is basically built, and is used by many cloud services like Facebook, Twitter, WordPress, Flicker, Amazon, Aurora, and many other examples, including, by the way, Maria DB, which is a fork of MySQL created by MySQL's creator, basically in protest to Oracle's acquisition; the drama is Oscar worthy. It gets even better. In 2020, Oracle began introducing a new version of MySQL called MySQL HeatWave, and since late 2020 it's been in sort of a super cycle rolling, out three new releases in less than a year and a half in an attempt to expand its Tam and compete in new markets. Now we covered the release of MySQL Autopilot, which uses machine learning to automate management functions. And we also covered the bench marketing that Oracle produced against Snowflake, AWS, Azure, and Google. And Oracle's at it again with HeatWave, adding machine learning into its database capabilities, along with previously available integrations of OLAP and OLTP. This, of course, is in line with Oracle's converged database philosophy, which, as we've reported, is different from other cloud database providers, most notably Amazon, which takes the right tool for the right job approach and chooses database specialization over a one size fits all strategy. Now we've asked Oracle to come on theCUBE and explain these moves, and I'm pleased to welcome back Nipun Agarwal, who's the senior vice president for MySQL Database and HeatWave at Oracle. And today, in this video exclusive, we'll discuss machine learning, other new capabilities around elasticity and compression, and then any benchmark data that Nipun wants to share. Nipun's been a leading advocate of the HeatWave program. He's led engineering in that team for over 10 years, and he has over 185 patents in database technologies. Welcome back to the show Nipun. Great to see you again. Thanks for coming on. >> Thank you, Dave. Very happy to be back. >> Yeah, now for those who may not have kept up with the news, maybe to kick things off you could give us an overview of what MySQL HeatWave actually is so that we're all on the same page. >> Sure, Dave, MySQL HeatWave is a fully managed MySQL database service from Oracle, and it has a builtin query accelerator called HeatWave, and that's the part which is unique. So with MySQL HeatWave, customers of MySQL get a single database which they can use for transactional processing, for analytics, and for mixed workloads because traditionally MySQL has been designed and optimized for transaction processing. So in the past, when customers had to run analytics with the MySQL based service, they would need to move the data out of MySQL into some other database for running analytics. So they would end up with two different databases and it would take some time to move the data out of MySQL into this other system. With MySQL HeatWave, we have solved this problem and customers now have a single MySQL database for all their applications, and they can get the good performance of analytics without any changes to their MySQL application. >> Now it's no secret that a lot of times, you know, queries are not, you know, most efficiently written, and critics of MySQL HeatWave will claim that this product is very memory and cluster intensive, it has a heavy footprint that adds to cost. How do you answer that, Nipun? >> Right, so for offering any database service in the cloud there are two dimensions, performance and cost, and we have been very cognizant of both of them. So it is indeed the case that HeatWave is a, in-memory query accelerator, which is why we get very good performance, but it is also the case that we have optimized HeatWave for commodity cloud services. So for instance, we use the least expensive compute. We use the least expensive storage. So what I would suggest is for the customers who kind of would like to know what is the price performance advantage of HeatWave compared to any database we have benchmark against, Redshift, Snowflake, Google BigQuery, Azure Synapse, HeatWave is significantly faster and significantly lower price on a multitude of workloads. So not only is it in-memory database and optimized for that, but we have also optimized it for commodity cloud services, which makes it much lower price than the competition. >> Well, at the end of the day, it's customers that sort of decide what the truth is. So to date, what's been the customer reaction? Are they moving from other clouds from on-prem environments? Both why, you know, what are you seeing? >> Right, so we are definitely a whole bunch of migrations of customers who are running MySQL on-premise to the cloud, to MySQL HeatWave. That's definitely happening. What is also very interesting is we are seeing that a very large percentage of customers, more than half the customers who are coming to MySQL HeatWave, are migrating from other clouds. We have a lot of migrations coming from AWS Aurora, migrations from RedShift, migrations from RDS MySQL, TerriData, SAP HANA, right. So we are seeing migrations from a whole bunch of other databases and other cloud services to MySQL HeatWave. And the main reason we are told why customers are migrating from other databases to MySQL HeatWave are lower cost, better performance, and no change to their application because many of these services, like AWS Aurora are ETL compatible with MySQL. So when customers try MySQL HeatWave, not only do they get better performance at a lower cost, but they find that they can migrate their application without any changes, and that's a big incentive for them. >> Great, thank you, Nipun. So can you give us some names? Are there some real world examples of these customers that have migrated to MySQL HeatWave that you can share? >> Oh, absolutely, I'll give you a few names. Stutor.com, this is an educational SaaS provider raised out of Brazil. They were using Google BigQuery, and when they migrated to MySQL HeatWave, they found a 300X, right, 300 times improvement in performance, and it lowered their cost by 85 (audio cut out). Another example is Neovera. They offer cybersecurity solutions and they were running their application on an on-premise version of MySQL when they migrated to MySQL HeatWave, their application improved in performance by 300 times and their cost reduced by 80%, right. So by going from on-premise to MySQL HeatWave, they reduced the cost by 80%, improved performance by 300 times. We are Glass, another customer based out of Brazil. They were running on AWS EC2, and when they migrated, within hours they found that there was a significant improvement, like, you know, over 5X improvement in database performance, and they were able to accommodate a very large virtual event, which had more than a million visitors. Another example, Genius Senority. They are a game designer in Japan, and when they moved to MySQL HeatWave, they found a 90 times percent improvement in performance. And there many, many more like a lot of migrations, again, from like, you know, Aurora, RedShift and many other databases as well. And consistently what we hear is (audio cut out) getting much better performance at a much lower cost without any change to their application. >> Great, thank you. You know, when I ask that question, a lot of times I get, "Well, I can't name the customer name," but I got to give Oracle credit, a lot of times you guys have at your fingertips. So you're not the only one, but it's somewhat rare in this industry. So, okay, so you got some good feedback from those customers that did migrate to MySQL HeatWave. What else did they tell you that they wanted? Did they, you know, kind of share a wishlist and some of the white space that you guys should be working on? What'd they tell you? >> Right, so as customers are moving more data into MySQL HeatWave, as they're consolidating more data into MySQL HeatWave, customers want to run other kinds of processing with this data. A very popular one is (audio cut out) So we have had multiple customers who told us that they wanted to run machine learning with data which is stored in MySQL HeatWave, and for that they have to extract the data out of MySQL (audio cut out). So that was the first feedback we got. Second thing is MySQL HeatWave is a highly scalable system. What that means is that as you add more nodes to a HeatWave cluster, the performance of the system improves almost linearly. But currently customers need to perform some manual steps to add most to a cluster or to reduce the cluster size. So that was other feedback we got that people wanted this thing to be automated. Third thing is that we have shown in the previous results, that HeatWave is significantly faster and significantly lower price compared to competitive services. So we got feedback from customers that can we trade off some performance to get even lower cost, and that's what we have looked at. And then finally, like we have some results on various data sizes with TPC-H. Customers wanted to see if we can offer some more data points as to how does HeatWave perform on other kinds of workloads. And that's what we've been working on for the several months. >> Okay, Nipun, we're going to get into some of that, but, so how did you go about addressing these requirements? >> Right, so the first thing is we are announcing support for in-database machine learning, meaning that customers who have their data inside MySQL HeatWave can now run training, inference, and prediction all inside the database without the data or the model ever having to leave the database. So that's how we address the first one. Second thing is we are offering support for real time elasticity, meaning that customers can scale up or scale down to any number of nodes. This requires no manual intervention on part of the user, and for the entire duration of the resize operation, the system is fully available. The third, in terms of the costs, we have double the amount of data that can be processed per node. So if you look at a HeatWave cluster, the size of the cluster determines the cost. So by doubling the amount of data that can be processed per node, we have effectively reduced the cluster size which is required for planning a given workload to have, which means it reduces the cost to the customer by half. And finally, we have also run the TPC-DS workload on HeatWave and compared it with other vendors. So now customers can have another data point in terms of the performance and the cost comparison of HeatWave with other services. >> All right, and I promise, I'm going to ask you about the benchmarks, but I want to come back and drill into these a bit. How is HeatWave ML different from competitive offerings? Take for instance, Redshift ML, for example. >> Sure, okay, so this is a good comparison. Let's start with, let's say RedShift ML, like there are some systems like, you know, Snowflake, which don't even offer any, like, processing of machine learning inside the database, and they expect customers to write a whole bunch of code, in say Python or Java, to do machine learning. RedShift ML does have integration with SQL. That's a good start. However, when customers of Redshift need to run machine learning, and they invoke Redshift ML, it makes a call to another service, SageMaker, right, where so the data needs to be exported to a different service. The model is generated, and the model is also outside RedShift. With HeatWave ML, the data resides always inside the MySQL database service. We are able to generate models. We are able to train the models, run inference, run explanations, all inside the MySQL HeatWave service. So the data, or the model, never have to leave the database, which means that both the data and the models can now be secured by the same access control mechanisms as the rest of the data. So that's the first part, that there is no need for any ETL. The second aspect is the automation. Training is a very important part of machine learning, right, and it impacts the quality of the predictions and such. So traditionally, customers would employ data scientists to influence the training process so that it's done right. And even in the case of Redshift ML, the users are expected to provide a lot of parameters to the training process. So the second thing which we have worked on with HeatWave ML is that it is fully automated. There is absolutely no user intervention required for training. Third is in terms of performance. So one of the things we are very, very sensitive to is performance because performance determines the eventual cost to the customer. So again, in some benchmarks, which we have published, and these are all available on GitHub, we are showing how HeatWave ML is 25 times faster than Redshift ML, and here's the kicker, at 1% of the cost. So four benefits, the data all remain secure inside the database service, it's fully automated, much faster, much lower cost than the competition. >> All right, thank you Nipun. Now, so there's a lot of talk these days about explainability and AI. You know, the system can very accurately tell you that it's a cat, you know, or for you Silicon Valley fans, it's a hot dog or not a hot dog, but they can't tell you how the system got there. So what is explainability, and why should people care about it? >> Right, so when we were talking to customers about what they would like from a machine learning based solution, one of the feedbacks we got is that enterprise is a little slow or averse to uptaking machine learning, because it seems to be, you know, like magic, right? And enterprises have the obligation to be able to explain, or to provide a answer to their customers as to why did the database make a certain choice. With a rule based solution it's simple, it's a rule based thing, and you know what the logic was. So the reason explanations are important is because customers want to know why did the system make a certain prediction? One of the important characteristics of HeatWave ML is that any model which is generated by HeatWave ML can be explained, and we can do both global explanations or model explanations as well as we can also do local explanations. So when the system makes a specific prediction using HeatWave ML, the user can find out why did the system make such a prediction? So for instance, if someone is being denied a loan, the user can figure out what were the attribute, what were the features which led to that decision? So this ensures, like, you know, fairness, and many of the times there is also like a need for regulatory compliance where users have a right to know. So we feel that explanations are very important for enterprise workload, and that's why every model which is generated by HeatWave ML can be explained. >> Now I got to give Snowflakes some props, you know, this whole idea of separating compute from storage, but also bringing the database to the cloud and driving elasticity. So that's been a key enabler and has solved a lot of problems, in particular the snake swallowing the basketball problem, as I often say. But what about elasticity and elasticity in real time? How is your version, and there's a lot of companies chasing this, how is your approach to an elastic cloud database service different from what others are promoting these days? >> Right, so a couple of characteristics. One is that we have now fully automated the process of elasticity, meaning that if a user wants to scale up or scale down, the only thing they need to specify is the eventual size of the cluster and the system completely takes care of it transparently. But then there are a few characteristics which are very unique. So for instance, we can scale up or scale down to any number of nodes. Whereas in the case of Snowflake, the number of nodes someone can scale up or scale down to are the powers of two. So if a user needs 70 CPUs, well, their choice is either 64 or 128. So by providing this flexibly with MySQL HeatWave, customers get a custom fit. So they can get a cluster which is optimized for their specific portal. So that's the first thing, flexibility of scaling up or down to any number of nodes. The second thing is that after the operation is completed, the system is fully balanced, meaning the data across the various nodes is fully balanced. That is not the case with many solutions. So for instance, in the case of Redshift, after the resize operation is done, the user is expected to manually balance the data, which can be very cumbersome. And the third aspect is that while the resize operation is going on, the HeatWave cluster is completely available for queries, for DMLS, for loading more data. That is, again, not the case with Redshift. Redshift, suppose the operation takes 10 to 15 minutes, during that window of time, the system is not available for writes, and for a big part of that chunk of time, the system is not even available for queries, which is very limiting. So the advantages we have are fully flexible, the system is in a balanced state, and the system is completely available for the entire duration operation. >> Yeah, I guess you got that hypergranularity, which, you know, sometimes they say, "Well, t-shirt sizes are good enough," but then I think of myself, some t-shirts fit me better than others, so. Okay, I saw on the announcement that you have this lower price point for customers. How did you actually achieve this? Could you give us some details around that please? >> Sure, so there are two things for announcing this service, which lower the cost for the customers. The first thing is that we have doubled the amount of data that can be processed by a HeatWave node. So if we have doubled the amount of data, which can be a process by a node, the cluster size which is required by customers reduces to half, and that's why the cost drops to half. The way we have managed to do this is by two things. One is support for Bloom filters, which reduces the amount of intermediate memory. And second is we compress the base data. So these are the two techniques we have used to process more data per node. The second way by which we are lowering the cost for the customers is by supporting pause and resume of HeatWave. And many times you find customers of like HeatWave and other services that they want to run some other queries or some other workloads for some duration of time, but then they don't need the cluster for a few hours. Now with the support for pause and resume, customers can pause the cluster and the HeatWave cluster instantaneously stops. And when they resume, not only do we fetch the data, in a very, like, you know, a quick pace from the object store, but we also preserve all the statistics, which are used by Autopilot. So both the data and the metadata are fetched, extremely fast from the object store. So with these two capabilities we feel that it'll drive down the cost to our customers even more. >> Got it, thank you. Okay, I promised I was going to get to the benchmarks. Let's have it. How do you compare with others but specifically cloud databases? I mean, and how do we know these benchmarks are real? My friends at EMC, they were back in the day, they were brilliant at doing benchmarks. They would produce these beautiful PowerPoints charts, but it was kind of opaque, but what do you say to that? >> Right, so there are multiple things I would say. The first thing is that this time we have published two benchmarks, one is for machine learning and other is for SQL analytics. All the benchmarks, including the scripts which we have used are available on GitHub. So we have full transparency, and we invite and encourage customers or other service providers to download the scripts, to download the benchmarks and see if they get any different results, right. So what we are seeing, we have published it for other people to try and validate. That's the first part. Now for machine learning, there hasn't been a precedence for enterprise benchmarks so we talk about aiding open data sets and we have published benchmarks for those, right? So both for classification, as well as for aggression, we have run the training times, and that's where we find that HeatWave MLS is 25 times faster than RedShift ML at one percent of the cost. So fully transparent, available. For SQL analytics, in the past we have shown comparisons with TPC-H. So we would show TPC-H across various databases, across various data sizes. This time we decided to use TPC-DS. the advantage of TPC-DS over TPC-H is that it has more number of queries, the queries are more complex, the schema is more complex, and there is a lot more data skew. So it represents a different class of workloads, and which is very interesting. So these are queries derived from the TPC-DS benchmark. So the numbers we have are published this time are for 10 terabyte TPC-DS, and we are comparing with all the four majors services, Redshift, Snowflake, Google BigQuery, Azure Synapse. And in all the cases, HeatWave is significantly faster and significantly lower priced. Now one of the things I want to point out is that when we are doing the cost comparison with other vendors, we are being overly fair. For instance, the cost of HeatWave includes the cost of both the MySQL node as well as the HeatWave node, and with this setup, customers can run transaction processing analytics as well as machine learning. So the price captures all of it. Whereas with the other vendors, the comparison is only for the analytic queries, right? So if customers wanted to run RDP, you would need to add the cost of that database. Or if customers wanted to run machine learning, you would need to add the cost of that service. Furthermore, with the case of HeatWave, we are quoting pay as you go price, whereas for other vendors like, you know, RedShift, and like, you know, where applicable, we are quoting one year, fully paid upfront cost rate. So it's like, you know, very fair comparison. So in terms of the numbers though, price performance for TPC-DS, we are about 4.8 times better price performance compared to RedShift We are 14.4 times better price performance compared to Snowflake, 13 times better than Google BigQuery, and 15 times better than Synapse. So across the board, we are significantly faster and significantly lower price. And as I said, all of these scripts are available in GitHub for people to drive for themselves. >> Okay, all right, I get it. So I think what you're saying is, you could have said this is what it's going to cost for you to do both analytics and transaction processing on a competitive platform versus what it takes to do that on Oracle MySQL HeatWave, but you're not doing that. You're saying, let's take them head on in their sweet spot of analytics, or OLTP separately and you're saying you still beat them. Okay, so you got this one database service in your cloud that supports transactions and analytics and machine learning. How much do you estimate your saving companies with this integrated approach versus the alternative of kind of what I called upfront, the right tool for the right job, and admittedly having to ETL tools. How can you quantify that? >> Right, so, okay. The numbers I call it, right, at the end of the day in a cloud service price performance is the metric which gives a sense as to how much the customers are going to save. So for instance, for like a TPC-DS workload, if we are 14 times better price performance than Snowflake, it means that our cost is going to be 1/14th for what customers would pay for Snowflake. Now, in addition, in other costs, in terms of migrating the data, having to manage two different databases, having to pay for other service for like, you know, machine learning, that's all extra and that depends upon what tools customers are using or what other services they're using for transaction processing or for machine learning. But these numbers themselves, right, like they're very, very compelling. If we are 1/5th the cost of Redshift, right, or 1/14th of Snowflake, these numbers, like, themselves are very, very compelling. And that's the reason we are seeing so many of these migrations from these databases to MySQL HeatWave. >> Okay, great, thank you. Our last question, in the Q3 earnings call for fiscal 22, Larry Ellison said that "MySQL HeatWave is coming soon on AWS," and that caught a lot of people's attention. That's not like Oracle. I mean, people might say maybe that's an indication that you're not having success moving customers to OCI. So you got to go to other clouds, which by the way I applaud, but any comments on that? >> Yep, this is very much like Oracle. So if you look at one of the big reasons for success of the Oracle database and why Oracle database is the most popular database is because Oracle database runs on all the platforms, and that has been the case from day one. So very akin to that, the idea is that there's a lot of value in MySQL HeatWave, and we want to make sure that we can offer same value to the customers of MySQL running on any cloud, whether it's OCI, whether it's the AWS, or any other cloud. So this shows how confident we are in our offering, and we believe that in other clouds as well, customers will find significant advantage by having a single database, which is much faster and much lower price then what alternatives they currently have. So this shows how confident we are about our products and services. >> Well, that's great, I mean, obviously for you, you're in MySQL group. You love that, right? The more places you can run, the better it is for you, of course, and your customers. Okay, Nipun, we got to leave it there. As always it's great to have you on theCUBE, really appreciate your time. Thanks for coming on and sharing the new innovations. Congratulations on all the progress you're making here. You're doing a great job. >> Thank you, Dave, and thank you for the opportunity. >> All right, and thank you for watching this CUBE conversation with Dave Vellante for theCUBE, your leader in enterprise tech coverage. We'll see you next time. (upbeat music)

>>Hello, My name is John John Sheikh from Iran Tous. Welcome to our session on new extensions for doctors CLI as we all know, containers air everywhere. Kubernetes is coming on strong and the CNC F cloud landscape slide has become a marvel to behold its complexities about to surpass that of the photo. Letha dies used to fabricate the old intel to 86 and future generations of the diagram will be built out and up into multiple dimensions using extreme ultraviolet lithography. Meanwhile, complexity is exploding and uncertainty about tools, platform details, processes and the economic viability of our companies in changing and challenging times is also increasing. Mirant ous, as you've already heard today, believes that achieving speed is critical and that speed results from balancing choice with simplicity and security. You've heard about Dr Enterprise Container Cloud, a new framework built on kubernetes, the less you deploy compliant, secure by default. Cooper nineties clusters on any infrastructure, providing a seamless self service capable cloud experience to developers. Get clusters fast, Justus, you need them, Update them seamlessly. Scale them is needed all while keeping workloads running smoothly. And you've heard how Dr Enterprise Container Cloud also provides all the day one and Day two and observe ability, tools, the integration AP ICE and Top Down Security, Identity and Secrets management to run operations efficiently. You've also heard about Lens, an open source i D for kubernetes. Aimed at speeding up the most banding, tightest inner loop of kubernetes application development. Lens beautifully meets the needs of a new class of developers who need to deal with multiple kubernetes clusters. Multiple absent project sufficiently developers who find themselves getting bogged down and seal I only coop CTL work flows and context switches into and out of them. But what about Dr Developers? They're working with the same core technologies all the time. They're accessing many of the same amenities, including Docker, engine Enterprise, Docker, Trusted registry and so on. Sure, their outer loop might be different. For example, they might be orchestrating on swarm. Many companies are our future of Swarm session talks about the ongoing appeal of swarm and Miranda's commitment to maintaining and extending the capabilities of swarm Going forward. Dr Enterprise Container Cloud can, of course, deployed doctor enterprise clusters with 100% swarm orchestration on computes just Aziza Leah's. It can provide kubernetes orchestration or mixed swarming kubernetes clusters. The problem for Dr Dev's is that nobody's given them an easy way to use kubernetes without a learning curve and without getting familiar with new tools and work flows, many of which involved buoys and are somewhat tedious for people who live on the command line and like it that way until now. In a few moments you'll meet my colleagues Chris Price and Laura Powell, who enact a little skit to introduce and demonstrate our new extended docker CLI plug in for kubernetes. That plug in offers seamless new functionality, enabling easy context management between the doctor Command Line and Dr Enterprise Clusters deployed by Dr Enterprise Container Cloud. We hope it will help Dev's work faster, help them adapt decay. TSA's they and their organizations manage platform coexistence or transition. Here's Chris and Laura, or, as we like to call them, developer A and B. >>Have you seen the new release of Docker Enterprise Container Cloud? I'm already finding it easier to manage my collection of UCP clusters. >>I'm glad it's helping you. It's great we can manage multiple clusters, but the user interface is a little bit cumbersome. >>Why is that? >>Well, if I want to use docker cli with a cluster, I need to download a client bundle from UCP and use it to create a contact. I like that. I can see what's going on, but it takes a lot of steps. >>Let me guess. Are these the steps? First you have to navigate to the web. You i for docker Enterprise Container Cloud. You need to enter your user name and password. And since the cluster you want to access is part of the demo project, you need to change projects. Then you have to choose a cluster. So you choose the first demo cluster here. Now you need to visit the U C p u I for that cluster. You can use the link in the top right corner of the page. Is that about right? >>Uh yep. >>And this takes you to the UCP you. I log in page now you can enter your user name and password again, but since you've already signed in with key cloak, you can use that instead. So that's good. Finally, you've made it to the landing page. Now you want to download a client bundle what you can do by visiting your user profile, you'll generate a new bundle called Demo and download it. Now that you have the bundle on your local machine, you can import it to create a doctor context. First, let's take a look at the context already on your machine. I can see you have the default context here. Let's import the bundle and call it demo. If we look at our context again, you can see that the demo context has been created. Now you can use the context and you'll be able to interact with your UCP cluster. Let's take a look to see if any stacks are running in the cluster. I can see you have a stack called my stack >>in >>the default name space running on Kubernetes. We can verify that by checking the UCP you I and there it iss my stack in the default name space running on Kubernetes. Let's try removing the stack just so we could be sure we're dealing with the right cluster and it disappears. As you can see. It's easy to use the Docker cli once you've created a context, but it takes quite a bit of effort to create one in the first place. Imagine? >>Yes. Imagine if you had 10 or 20 or 50 clusters toe work with. It's a management nightmare. >>Haven't you heard of the doctor Enterprise Container Cloud cli Plug in? >>No, >>I think you're going to like it. Let me show you how it works. It's already integrated with the docker cli You start off by setting it up with your container cloud Instance, all you need to get started is the base. You are all of your container cloud Instance and your user name and password. I'll set up my clothes right now. I have to enter my user name and password this one time only. And now I'm all set up. >>But what does it actually dio? >>Well, we can list all of our clusters. And as you can see, I've got the cluster demo one in the demo project and the cluster demo to in the Demo project Taking a look at the web. You I These were the same clusters we're seeing there. >>Let me check. Looks good to me. >>Now we can select one of these clusters, but let's take a look at our context before and after so we can understand how the plug in manages a context for us. As you can see, I just have my default contact stored right now, but I can easily get a context for one of our clusters. Let's try demo to the plug in says it's created a context called Container Cloud for me and it's pointing at the demo to cluster. Let's see what our context look like now and there's the container cloud context ready to go. >>That's great. But are you saying once you've run the plug in the doctor, cli just works with that cluster? >>Sure. Let me show you. I've got a doctor stack right here and it deploys WordPress. Well, the play it to kubernetes for you. Head over to the U C P u I for the cluster so you can verify for yourself. Are you ready? >>Yes. >>First I need to make sure I'm using the context >>and >>then I can deploy. And now we just have to wait for the deployment to complete. It's as easy as ever. >>You weren't lying. Can you deploy the same stack to swarm on my other clusters? >>Of course. And that should also show you how easy it is to switch between clusters. First, let's just confirm that our stack has reported as running. I've got a stack called WordPress demo in the default name space running on Kubernetes to deploy to the other cluster. First I need to select it that updates the container cloud context so I don't even need to switch contexts, since I'm already using that one. If I check again for running stacks, you can see that our WordPress stack is gone. Bring up the UCP you I on your other cluster so you can verify the deployment. >>I'm ready. >>I'll start the deployment now. It should be appearing any moment. >>I see the services starting up. That's great. It seems a lot easier than managing context manually. But how do I know which cluster I'm currently using? >>Well, you could just list your clusters like So do you see how this one has an asterisk next to its name? That means it's the currently selected cluster >>I'm sold. Where can I get the plug in? >>Just go to get hub dot com slash miran tous slash container dash cloud dash cli and follow the instructions

>> 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)

>> Narrator: From theCUBE Studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Hi, I'm Stu Miniman, the host of Cloud Native Insights. And when we kicked off this program, Cloud Native Insights, we wanted to talk about the innovation and agility that's happening, not just Cloud as a location. We're going to draw down a little bit into one of the very important pieces of a company and that's their websites and their applications, that live in that environment. And of course, that comes from a lot of changes over the years. Any of us that have been in tech for a couple of decades have worked from the early days, to of course today's multimedia globally distributed environment and everyone during the global pandemic, of course, has been (indistinct) straining their use of the internet. So really excited to welcome to the program the two co-founders of Netlify. I have Matt Biilmann, who is the CEO, and his co-founder Christian Bach, who is the president both of Netlify really the company behind Jamstack, which we're going to explain and talk about a bit. Matt and Chris, thank you so much for joining us. >> Thanks for having us. >> Thank you for having us >> All right so, let's start with just some of the basics. I expect that some of our audience is not familiar with Jamstack. You do a quick Google search and it's JavaScript, its APIs, its markup. And you say, okay, I understand what a bunch of that means. But, yeah, if you could give us kind of a compare contrast to what web development was before and how Jamstack's really helping to revolutionize what's happening in this space. >> Yes, so for many years, we built websites and web applications with an application based architecture, where every website or every application would be this monolithic application with typically like a load balancer, a set of web servers, application servers, and that database and every request through a page would go through this whole stack it would pass through the application layer, talk to the database, fetch template, merge data and template, build HTML on the fly and send it back to the user. And basically what we saw happening and what's been happening with the Jamstack is this decoupling of the actual front-end presentation layer of the websites and web applications and then the back-end layer. And the advantages there is that if you can really pre-build the front-end application layer, you can take the actual HTML, or an application shell and distribute it across a globally distributed network, you can get it into the hands of the user's browser very quickly. And then the back end, what we've seen happening there is that it's split up to all these different APIs and services you no longer have your one monolithic back end you have all these different services. Where some of your own but a lot of them are other people's services like Stripe or Twilio or Algolia or Contentful. So we've seen this shift to this architecture, where we're considered in a way that the stack has moved up a little from the old tooling where something like the LAMP stack would be common in really naming the programming language, the specific web server, the Linux server, the operating system, and so on right? And then up to a level where it's really about getting an application into the browser, using JavaScript as the runtime and talking to this whole new economy of APIs and services. >> Yeah, Chris I wonder if you could bring us inside your customers and the companies that you talk to. I think about for the longest time it was, maybe I just outsource my web development, but website is one of those key components that I share my value, I share what's going on, I want to be able to change it pretty often and there's so much more that I can do today than I could have done 10 years ago. We've watched that mark. So, help us understand, what skill sets do people need to have? what type of companies are using Jamstack? And, bring in if you can, Netlify. How is this a business and not just, an open source standards movement, that's helping to revolutionize what's happening? >> Absolutely, I mean, First of all, people using this and companies use this is extremely wide. Wide vertical, right? Its very horizontal. This is anyone with a digital property basically, right? I think what we've seen all the time is that, that we have a lot more channels than we used to have, right? So we started off just maybe having the one dot com, right? With limited functionality. And today, you have a multiple channels, right? You have the landing pages, you have the domains, you have lots of activities online. You have mobile apps and commerce is often a big part of it, and I would say especially the last few months, there's a lot of people that had the digital convergence points as one of many. And now it's the only ones, right? So I think it's become extremely important. I also think that when you look at your web infrastructure in general, it has been very complex, right? And you need a lot of different people, right? And you need to maintain staging environments, production lines, development environments. You need to, have a wide set of skills to maintain these things, right? And if a web developer wanted to do a lot of things, right? They have to go and tap DevOps and so on on the shoulder, right? And I think what the Jamstack is about saying, hey, you can get so much further as a web developer. Now, if you take the modern built tools, you can take the Git workflows, and you wrap around the browser that has become a full-fledged operating system and the API economy as Matt was just talking about. You have these workflows, or you potentially have these workflows, where you can get so much further, right? And that's very much Netlify submission. So Netlify saw this opportunity of decoupling the front end from the back end of the building from the hosting of creating an approach to making websites that would be many times faster, 'cause you have multiple points of origin and you don't feel fredurous. It's many times safer. There's not that huge surface area of attack. It's much more scalable, and so on. It was sort of a win-win-win. But the problem was, there was no viable workflow. If you take a traditional CDN, and you put it in, it doesn't matter really, if it's one or the other. As good as they (indistinct) services, they're all meant to sit in front of an origin, right? They're meant to buffer something. And if you have the gems, there's no origin in that way, right? The network in itself has to be an origin so it has to be architectured quite differently. And then there's a lot of things around CDCI and how you server lists and so on. That all had to be sort of re-merged . And Netlify is that glue, it is that platform that takes you from local development all the way out to edge nodes. But allows you to mix and match any tool. So it's not program independent. So you can say, well, we use a build tool, and that's PHP or Ruby or JavaScript, the react or Next or whatever it might be, right? And we use these APIs for this server, for this property. Over here we have a commerce site. Over here, we have a dotcom, that needs a huge enterprise CMS with tons of stakeholders. But the thing is that all of those now becomes something that plugs into your website. Rather than have to drive the website itself. And that's sort of frees up the silos. So when we see people using Netlify, we have companies using Netlify. Big Fitness Company, for example, that own fitness company that uses us for developer documentation, or their marketing sites, but also for their dotcom. But even if you go to the equipment that people have at home, and you log in, that's actually using some very nifty identity and remote based access control for Netlify and if you watch the video there, it's also going through a Netlify player, all right? We have fast food chains that has their dotcom and their marketing sites, but also the kiosks down in the store like the menus, the screens there. Rather than being an old Windows NT server running some .NET application in a dusty corner, why not have it like that? And so, both the category but also Netlify sort of brings in a solution and because it's decoupled from all those architectural choices, that means that you can now use the solution in a much, much wider setting. And we were sort of first to market doing this. They get serverless approach where you just push your serverless functions to get better Netlify. First Feature Deploy Previews Were invented by us and so on. So the Jamstack is an extremely wide fundamental architectural approach that matches basically anyone that wants to build web properties. Netlify is the segnostic wide platform that just makes it possible. >> Yeah, good Chris actually, I saw the Peloton use case up on the website and you're right, a very different experience rather than I bring my device, is it synced? Does it work with it? Really integrates those solutions. And you just brought up serverless, which is actually how I got connected to talk in Netlify. So, Matt, sorry, I think you wanted to jump in there but I was wondering if you could help us. I've looked at serverless and what the promise of serverless of course, is that I don't need to think about that underlying infrastructure. I just like developers build our applications. Well, feels like that's really the same mission that you have. And they're serverless is a piece of your story. So, maybe explain (indistinct) that out a little for us. >> Absolutely, I think it ties in, right? Basically, what we saw just from a architectural perspective was this approach of really decoupling front end and back end and so on and working in a new way that gave a lot of benefits to the inducers in performance and security and so on right? But on the other hand, early on, what we saw was that to adopt that approach, like developers had to deal with lot of different moving pieces like CICD, CDN. What to do about the API endpoints that didn't need to be dynamic, and so on. And as Netlify, what we saw was that we could give one intro and workflow for all of this and make it extremely easy for developers to work with this thing. And serverless plays a really important piece there, right? Because when Amazon pioneered AWS Lambda and took it to the world, right? I think the promise also for the front-end web developers of being able to simply write code and then not have to worry at all about where is it actually running? How are we scaling it? How are we operating it and so on, right? That's a really powerful promise, right? But at the same time, in the same way, what we saw earlier on was that for a front-end team to actually adopt serverless functions as part of the Jamstack, it introduced another level of complexity of now having to manage your serverless functions independent from your front end figuring out API Gateway endpoints for every one of them. And how about deployment pipeline for your functions layer versus deployment pipelines for the actual front end layer that's supposed to talk to those front ends. How about staging environments versus to production environments? How do you manage all that, right? So we saw that there was this inherent incredible potential, but also a lot of complexity, right? And as Netlify we saw that if we could give front end developers a web developers in general, an ene-to-end workflow, where they can work both with the front-end framework, write the code that will get deployed into the browser, but also just have a folder where they can write this serverless functions and then know that Netlify will take care of all of the wiring, right? When you open a pull request and get with new function we'll give you a URL on our globally distributed CDN where you can view both the whole front end, but also the function and sidestep sort of all of the complexities of linking together API Gateways, to functions of managing CICD pipelines and test environments and so on. And in the end, the serverless functions starts becoming a really important part of this Jamstack approach, right? Because you have this world where you have a front end that's often talking to many different APIs and services where again, some of your own and some other people's services. But really often you need some place to glue those together or to build your own custom API endpoint that talks to a couple of them and it has access to server site secrets and so on, right? And this idea of not having to suddenly operate and manage a whole set of servers and infrastructure just for that part of it, but simply just writing the code and then knowing, that you don't have to worry about the operation scalability or anything around that code. That's a really powerful paradigm. >> Yeah, that's one of the real challenges of the Cloud as you talk about the Paradox of Choice. There's so many ways to do things. Not necessarily... It's simple anybody... I was a blogger for many years and it was like, well, I'll just use the self-hosted WordPress, because I don't want to have to worry about that piece of it. Matt, I watched it you did a presentation talking about if I wanted to do WordPress hosted in a AWS that absolutely is not simple. I heard a podcast from one of your board members, Tom Preston Werner, talking about we need to be more opinionated. We need to be able to give more guidance to developers, maybe Chris if you could, how are we when the proliferation of choice, keeps increasing, making sure that people can... How do I make that decision tree? And how do we try to keep it simple? >> Absolutely, I mean, and I actually think that, that's a super relevant question, because you have a lot of choice as a web developer today. Front-end developers used to cut out Photoshop files and turn them into HTML, right? Now with the new advanced markup, and they have all these frameworks and flavors of JavaScript to choose between and there's these powerful build tools, And all those workflows and the browser can do everything you can imagine, right? And so yeah, there is a lot of choice out there, right? And I think, for Netlify what's extremely important is that we are opinionated in the right places. And so when it comes to, for example, a front-end tool and built tools and these things that web developers often face with having to choose between. Our role is to make it as simple as possible to use any of them. But also give you the opportunity of saying, well, this new paradigm allows you to actually mix and match, right? It allows you to use this tool for this property and this tool for this property and gives you a ton of flexibility. But still, come under one roof of a platform like Netlify. And I think that is very powerful. And so we also don't want to choose for you, we want to inform your choices and we want to make it as easy as possible to go and say, hey, these are my needs, what direction should I be going? And of course, we work with enterprise clients, so on migration services, and so on, right? And where we help them a lot with that. But if we locked down on a single flavor, or a single bill tool or a single front end framework, then we also limit the application of what we bring to market and we want to remain a little more open-ended there. But I think there's a lot complexity, a platform like Netlify is all about simplification. So all that wiring that Matt just mentioned, that at least goes, right? You don't spend hours configuring bondage caching and trying to find those edge cases, it just works. And that's a huge game changer for a lot of people, right? But there's definitely parts of the ecosystem that has a lot of choice. And we do our best to inform. And I think, under hand holding part, adjacent to that is the story of, well, do we then start using content management systems? Is this a whole new? Is it out with the old and in with the new? And I would say, you still have a lot of those needs, right? You still have non-technical people, for example, that needs to be able to update and create moves and content, and so on, right? And create content. And so you very often will need and an E-commerce solution or content management systems and so on. But what we're seeing there, is that we're speaking basically with every single major CMS out there. That are saying we're working on a headless system, or we already have a headless version, or we just gone full headless, that means that we work decoupled. So we don't no longer need to build the site. But we just provide like an independent source of content. And then it plugs into a platform like Netlify. So that can bring a lot of simplicity. And now you just have to maintain your content, but you don't have to worry about all the different environments and what is up to date and how does some of the infrastructure look like you press a button that commits to get a default preview, and it looks the same everywhere. >> I'm curious, what impact the current global pandemic has had on Netlify, and your customers. I saw you've got a COVID tracking project that you've done. But also now just there's different considerations when I think about what services I need to access from the web and what kind of connectivity the ultimate end user would have. So, what learnings have you had? What's involved there? >> In, obviously we, it depends a lot on, as Chris mentioned, right? The game circus is adopted horizontally across all kinds of areas and businesses and so on, right? So, we've of course seen businesses in sectors that are having a hard time and on the other hand, we've seen businesses and sectors that are exploding, right? We did immediately when the lockdown started happening and the pandemic started happening we set aside like a free plan for projects working in the space of tackling the information sharing around COVID and finding solutions and so on. And that was really interesting to see you mention the COVID tracking project, right? Which was a project like built a short time by small group of distributed incredibly talented front end developers and scientists and so on, right? And I think it was interesting to see that, how the Jamstack and our tooling and so on also really made it possible for them to build as a small distributed team the set of data information and tooling to a global audience, right? Seeing huge traffic peaks at time and just knowing that their architecture and our infrastructure could handle it for them. >> All right. Chris, I've got one, a little bit off to the side here. When I look at what Netlify is doing, you talk about having an open and independent web. And while we are fully supportive of that, we're a little concerned sometimes. If you look at what's happening across the globe, there's a lot of discussions. Will the internet actually fragment? Will certain countries wall off certain environments? Any concerns there? What do you look at? What are you hearing from your customers when you talk about that mission? >> It's one of the big challenges of all time, right? I think we all maybe took for given the Internet as the standard it became right? The way that you can publish without permission is pretty magnificent, right? And it would be indescribably painful for civilization if we lost that, right? And I think fragmentation is something that we all have to sort of worry around. From the way we see it, is that the web, the traditional monolithic approach, right? To which led to as a web that wasn't secure enough and wasn't scalable enough and wasn't performing enough and that's, for example, what opened the door for mobile applications, right? Where it just didn't make sense to pull in the UI every time you turn the page. So we ended up with a form that's it. We prebuilt the application, you download it, and then you speak to service for anything then atmosphere come up with it, right. And that makes perfect sense. That's basically the same architecture that we're bringing to the web a very large scale. Of course, the problem is that now there are gatekeepers there, right? There people, you have to ask for permission to publish and so on. And, and there are other attempts to say, "Hey, we need a performing web." And there's a very big players out there that say, "Let's come over and just..." Do we even need to call it the Internet? Can we just call it our company website? I'm not going to name any names here, right? But leading down, it's what we've called walled gardens, that are great for absolutely no one except for the company. And what we believe is that if you have a web that is secure and is scalable, and it's performant enough to justify at least the architecture maintaining and not having to run into any walled gardens and still say no, you don't need to use a handful of commercial platforms if you want to be heard rather than have your own web properties on your own custom domains, right? I think that's the part of the open independent viable web that we're fighting for. Basically, one that adopts and keeps adopting an architecture that is something that levels the playing field. And then they would also say, why Netlify? I mean, a few years before we started, like, try configuring your own CDN. And like that was reserved for the very, very large tech players. Now you can comment, you can literally click a button on Netlify, you get custom domain and ACS post process site that's globally distributed, automatically integrated into get. And that's on the premium plan. And so as a startup, you can level set together with everyone else and be available widely across the globe without performance issues, immediately. And so in that way, I'm also seeing that's a democrat sensation of performance, right? That means that, that's great. And for places where you see developing economies, where you often have brownouts, where you often can't depend on having viable services and is locally and so on, this idea of having he cover that and having something that's just automatically, you know what, don't even worry about it, because it's already ready to go in all these packets all around the world. That's a huge game changer. That's actually what we see a lot of adoption of the gems they can never find in those places as well. Guess that's just such a promise to the architecture. So, I hear what you're saying and I'm also very concerned about a fragmented web for political reasons as well across the globe. And from our angle, the way we fight for this is to make sure that it retains using an architecture that makes it accessible for me. >> Yeah, I heard many years ago, a friend of mine said, if you're a technologist it means that in general you are a technology optimist, which I definitely try to be. So, I love Chris how you've just brought in some of the potential opportunity Matt, I want to give you just... People out there they hear like oh, 5G is coming, it's going to completely change the world. Anything that you're seeing on your side as to real opportunities that we will see, just a step function in what your company is using. Jamstack, partnering with Netlify in your ecosystem. What are some of the early things that you see that are exciting you down the line for this? >> Part of it is simply like the whole ecosystem around the gem stalk growing up and the tooling, the APIs, the frameworks available around it, and the level of innovation that's triggered. And especially how it's triggering in... Especially how we're seeing like the potential for small, distributed teams to work together and build things with a global impact in a short time. And I remember a couple of years ago, we did a hackathon with together with freeCodeCamp. And of course, like since it was with freeCodeCamp, it was mostly like teams were mostly fairly new to programming and so on, right? It was pretty amazing to see what over a weekend with this architecture and with this tooling, with the vendors that were present there and helping out and so on, what the small teams could actually get done in a weekend, right? Like I remember the winning team had an app where the whole room would see an image on the main stage screen and then on their phone, try to place that image on the map and you would real time see how people ranked, how close they got and get a winner and so on, right? And that was all just from combining APIs and tooling, like history, like Netlify, like Honor Bee, like Google Maps, and so on, right? And I think, in some way we shouldn't forget just how much this kind of ecosystem of readily available APIs and services around this front end stake. It's allowing people to build things that years ago would have taken a very big team probably like a year to build, and suddenly you can have a relatively small group of relatively new programmers built something really impressive, right? So I think that's a trend we'll see continue accelerating And me and Chris are personally involved in advising and helping out a lot of these new startups in the space that are trying to bring new tooling to the world that makes more and more of these things possible and accessible. >> Well, Chris and Matt, I really appreciate you both joining such an exciting space. Talk about the cloud, agility and innovation, such a robust ecosystem. Thank you so much for joining. >> Thanks for having us. >> Thanks for having us. >> And I'm Stu Miniman. Thank you for joining and look forward to hearing more about your CUBE insight. (soft 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 Barcelona, Spain, it's theCUBE. Covering KubeCon CloudNativeCon Euope 2019. Brought to you by Red Hat, the Cloud Native Computing foundation and eco-system partners. >> Welcome back to the Fira here in Barcelona, Spain. This is theCUBE's coverage of KubeCon CloudNativeCon 2019. I'm Stu Miniman, my co-host for two days of coverage is Corey Quinn, and we're excited to have on the program a first time guest, but a company that we've known for quite a while, Daniel Lopez Ridruejo, who's the CEO and co-founder of Bitnami. Just announced recently that Bitnami is being acquired by VMware. Daniel, thanks so much for joining us and congratulations to you and the team on the 'exit' as it were. >> Thank you very much, gracias. It's an honor to be here. >> Yeah so we had Erica Brescia who's the co-founder of yours on theCUBE seven years ago. Back then I was trying to figure out exactly what Bitnami was and where it fit in this whole world. Maybe you can just bring us up to speed for those that maybe don't know, and there's all these people in the enterprise space that might not know your community that the dev space knows real well, as to bring us back the who and the why of Bitnami >> Yeah Erica is my co-founder and we have been building this together over the years. It has been quite a fair ride and, we started Bitnami as an offshoot of our previous company called Bedrock in which we made software easy to install. And then we realized that a lot of what people wanted to make easy to install on Linux was Open Source software, so we started working with companies like MySQL and SugarCRM, Splunk really early on when they were only four or five people, and over time we decided to do the same thing as an Open Source project for all those other tools and projects that didn't have a way to make them easy to install. We started as Bitnami.org, we wanted to emphasize that it was an Open Source project, was never going to be a company, and it didn't turn out that way. >> All right so, we got a lot of things to cover, but help us connect the dots as to those early you know, dot org, it wasn't a company, to a company having the dev space to, we're starting down the path towards the enterprise, which seemed to be a natural fit as to what happened today. >> Yeah so going back to your original question of why we wanted to make, was always being driven. There is all this marvelous Open Source software out there that is super difficult to use for a great majority of people, and we just wanted to lower the barrier to make it easy to use, and that's what got it started. We never expected the success. It turns out we went from a hundred, to a thousand, to ten thousand to hundreds of thousands of downloads, and you know, we're super popular with developers. We have literally millions of developers using Bitnami, and as part of that evolution, we started working with the cloud providers. We drive a significant percentage of usage for Amazon, for Google, for Microsoft, that's what makes it valuable to those cloud vendors, and as the next stage of the company, we wanted to go directly to the enterprises in which we already have a lot of developers in those same enterprises, but when you go move to production, you know that it's a lot of red tape, a lot of gates that you have to go about compliance and security, and that's where we're taking the company to. >> Nine, ten years ago I stumbled over you, over your company or I guess project at that time, and it was the second best way I ever found to run WordPress. The first of course is, don't run WordPress. I'm very serious. Don't run WordPress. And I'm curious now, with the acquisition of Bitnami, what is the longer-term vision for how this fits into a more cloud-native landscape. Is it continuing to just be the, well not just but, is it continuing to be the application you get from a catalog and it's up and running, is their a containerized story, is there something else I'm not seeing? >> No, that's the core of Bitnami, and that will continue to do that. What has evolved over time is that initially you could download an installer and run it on your Mac. And then we were one of the first early adapters of AWS, so we created all these AMIs and when, you know, people were thinking that we were crazy, that Amazon was a company that sold books, but you know, what were we doing? We kind of saw where it was going early on. And then as Kubernetes came along, we were really, really early there as well, and we were one of the early partners of these around Helm. We provided a lot of the Helm charts. Right now we may have dabbled a little bit on Serverless, So whatever comes next, we will be there and our goal continues to be the same thing, which is to make awesome software available to everyone. So independently of the underlying platform, that's where we're focusing, so, the core mission is not changing, we're just omitting that, and going after the enterprise, more red hat enterprise Linux, you know, more OpenShift, more multi tier, high availabilty, more production features. >> All right so, you talk about all those pieces, and you talk about linux and everything there. I want you help connect, how does that tie into VMware and what you see them doing today because, sure Linux has been something that could live on a hypervisor for a long time, but in many ways there's been struggles in competition between VMware and them and the Linux community in the past, but, you know, we're starting to see some of that change and maybe this helps accelerate some of that change. >> Yeah I think there is a couple of companies, Microsoft and VMware, that were completely different companies than five years ago and probably the decision would have been different for us like five years ago versus what the company is today and where they're going. For us VMware is, the holy grail of acquisition is 2 plus 2 equals five, and that's hardly the, you know, there's a lot of acquisitions that don't go that way. For us it was a very thought out decision and it was, I think it was clear for us in the sense that we have a very big footprint with developers, they own enterprise IT, we wanted to go enterprise, they wanted to go into developers, they understand Open Source, they understand distributed teams, yeah. >> Maybe, I'd love to hear your insight as to that developer community, because when I walk around the show floor, you know, there was that struggle between the enterprise and the developers, and now, the storage world, we need to get CI/CD and all these things and they're like "uh, we don't know how to get there" . And over the last few years, it seems there's been a blurring of the lines, and more enterprise is embracing it, Open Source is a big piece of that, so is it, as you said, five years ago this wouldn't have happened, but now it feels like we're ready for that next step of the curve. >> Correct. And all of that is because of this standardization, that Kubernetes is allowing, you can standardize business practices, and your seeing a consolidation, the CI/CD wall. And it's like, things that used to be very exotic now is business as usual. And it's a parallel, you know, I started using Linux in '93, when there was not even a concept of a Linux distribution, you have to do all these things just to get a prompt, but over time people have standardized, you know I remember there were like, 50 or 60 Linux distributions; StagWare, SLS. And eventually, everybody converged on Red Hat enterprise Linux. I think something similar is going to happen, we're just midway there, in which you will not have KubeCon because Kubernetes will be something transparent that is boring. So, we're not there yet, but at some point Kubernetes will be boring and there will be layers on top of that where all the action is. Or will be. >> From my perspective, coming from a small startup background, it seemed to me that VMware was always one of those stodgy, boring companies I didn't have much time for and lately there've been a series of high profile acquisitions, Heptio, Wavefront, CloudFront and now Bitnami, and it's really changing, almost without me noticing, my entire perception of their place in the modern evolving cloud ecosystem. >> I think so, and that's one of the things that attracted us and I talked to Victoria about it, get to spend a bit of time with the CEO, with the people at the high level. For us it was very important. But again, one thing we haven't mentioned is that, for the most part we have been bootstrapped. We have been profitable, we only took a little money from Ycombinator when we were already profitable. So we have choices. Sometimes our BC funded peers don't have that choice, so it was a very meditated decision, and for me for these kind of acquisitions, when a much bigger company joins forces with a smaller company, the strategies need to be aligned. And to me, VMware realized that the world, a few years ago, that the world is going to be moved to cloud, the world is going to go towards Kubernetes and containers. And the acquisition of Heptio, the acquisition of CloudHealth, told us that they're serious about that and that we can fit right in and take advantage of that transformation they are going. And so far it's working really, really, really well and that's part of what made us decide to go in this direction. >> Yeah Daniel, what can you tell us about things, once this actually does close, what will that mean for the brand? What about relationships with, you mentioned Heptio? But not only Heptio, Pivotal obvously is a big player in this space. How does all of that line up? >> With Heptio and other units like the marketplace's other groups, we were already working with them before the acquisition, with Heptio, with ksonnet and a bunch of other initiatives. We're just going to double down on that, and they want to keep Bitnami, they want to keep the brand, they want to keep the team. If anything we're going to get more resources, and again, that was the fact that they didn't want to touch something that is working. We have been partners for, I think, seven or eight years. We have gotten to know each other over that time and built that trust that is needed. In a way nothing is going to change. We're going to have the same team doing the same things, we're just going to have more access to their userbase. Which is what we're going to do. We started down this path because we were raising money to build an enterprise sales force, and at some point we decided, okay, this doesn't make sense. We're going to give away all this chunk of the company to get access to the enterprise, or to build a sales force to get access to the enterprise, when we can be part of VMware and get that for free. >> You've mentioned a fair bit about what's going to change as far as you getting exposure to new customers, effectively broadening into additional markets. What does this mean for your existing customers who are, in some cases, whenever you're a customer of a small-ish company, and there's an acquisition, it sometimes is natural to be a little concerned of, do I need to find a new vendor? Do I need to find a new provider? And frankly, there's nothing else like you that I've ever seen on the market. >> No, that's a really good question. For us, what is a little bit unique is we have millions of users, but we only have a handful of customers. So our customers are AWS, Google, Microsoft, Oracle. So it was very important; VMware is already a vendor to all of these; and so far everybody is going to stay and we're just going to continue and deepen the relationship. And that's one of the things that made this attractive. So for customers, nothing is going to change. And we're just going to continue to deepen those relationships. And again, that was important. Had we gone through some of the other options there would have been a lot of very outward conversations to have and that is not the case. >> Yeah Daniel, how about the developer community itself. It's just had millions of downloads out there. We understand how some of the reaction can be. >> Yeah, everybody is like, is VMware going to be the evil company that's going to touch that? And I think so far the feedback has been extremely positive, including even Hacker News, right, which is shocking. >> And those people don't like anything. >> I've been high Hacker News since the very beginning and it can be harsh. So it was something I was monitoring how people. And so far it has been very positive and that's only not a testimony how much people like Bitnami but also again, VMware acquire Heptio and everything's great. We talk to a lot of the people at Heptio, you know, hey how are things going? How has it been? And everybody loved it there, so for us it was something that gave us a lot of reassurance that all these other companies with a lot of Open Source DNA were being successful there and gave us reassurance. Time will tell. We'll see one year from now where we are, but so far everybody that we have talked to, all the conversations have been great. >> So Daniel you have a very interesting viewpoint on this whole ecosystem, we work with all the cloud providers. Any commentary you'd give of, you talk about that midway point of maturity? Where do you see things today, where do you see them going? What do we need to fix as an industry? >> Well it's very difficult to predict where things are going I just think that at this point it's very safe to say that it's going to be a multi-cloud war. That was not like three, four years ago. It seemed like it could be a repeat of the '90s in which Microsoft own ninety-something percent of the market share. And there was a lot of things that didn't make sense. Right now at least Amazon, plus a bunch of other clouds, are viable, and if anything they are growing. So a lot of companies like HashiCorp, like VMware. Companies that support this multi-cloud environment, not all of them, but all of them are very well positioned to thrive because it's not going to change any time soon. The other thing I think that is safe to assume is, we are going to have more artifacts than ever, so companies like Artifactory, I think they will do well. As any companies have to do to do with security. We're going to have more security issues, not less. But in the long term that's as much as I can predict. >> All right, well, Daniel, thank you so much. Congratulations again, and we look forward to seeing you at VMworld. Where we'll have theCUBE there. It'll actually be our tenth year being at Vmworld. >> Awesome >> So we're excited and always happy to talk to, especially the startups some great news here. For Corey Quinn, I'm Stu Miniman, thanks as always for watching theCUBE.

>> Live from Bahrain it's theCUBE. Covering AWS Summit Bahrain. Brought to you be Amazon Web Services. >> Okay welcome back everyone, we are here in Bahrain for the special exclusive coverage of AWS Summit in the region and the big announcement of the AWS region being deployed here, and that's scheduled for Q1 roughly in 2019 as Teresa Carlson told us today on theCube. This is exciting opportunities for the Middle East and the region and around the world, a big spot where Amazon needed a region, of course we get all that coverage. Our next guest here is Lana Al Attar, founder of Gud Juju, and amazing logo on their card, empowering good, welcome to theCube, good to see you. >> Thank you for having me. >> Great to meet you. Yesterday, we were chatting, take a minute to explain what Gud Juju is, and we'll get to the meaning of the logo, if you can see it, I'm not sure if you can see it. Go ahead, what's Gud Juju? >> Gud Juju means good magic. We're a digital agency with a conscience, and how we do that is, a lot of our clients are people that have any kind of social good to them, so we're talking autism schools, or even normal schools, for example, they might charge money, but they're still in the industry of education, so it's fantastic. So, we help the little guys, we give them the technology they need, we empower them with technology, and that's what Gud Juju's about. On the other side of the spectrum, we also hire mostly women to do the development, so we hire and train. We hire refugees as well, and most recently we've signed a contract with a company and they provide us differently abled people, but they're fantastic designers and developers, so we've expanded into that as well. So, we pay our staff to be heroes, they are heroes day in and day out, and that's why I told you to wait (laughter) I can see you're impressed. >> Oh my god, I'm impressed. Well, no, I mean, there's a little bit of capitalism in there too that makes me go "Wow, that's a brilliant idea" (mumbles) In the labor market, but they're also helping people, and you're giving, tapping into passion. Taking a passion project, turning it into a business, help people, bring it all together, magic. >> Exactly. >> We help people >> That's good magic! >> Exactly, that's where the name Gud Juju comes from. >> Great mission, I love your mission, tell me about some of the things you're working on. >> Oh, okay, so most recently we're working on a "diversity on board" project, and initially it will be to, so we handle the tech part of this. A lot of people, they come to us and they try to partner with us, or even get us to complete their project. And, this particular project, if you go to any part of the world, usually in the Middle East, it'll be Middle Eastern men on the boards, and if you go to the States, it'll be middle aged white men on the boards, so there's no diversity. And that can be a problem for certain companies because if you want a 360 view, >> That's a big problem. It's a problem, period. I think, pretty much, people have figured out that diversity gives you more. >> Of course. >> Than not having it. >> You see things from different angles, because if you have the same kind of age group, and the same kind of gender, and the same everything, then you will not foresee the different challenges or opportunities that come by. So, this is a matchmaking portal that we're working on and it's soon to be launched, we're already testing internally in beta, and then then we're going to expand it into a private beta group, so that's one of the things that I'm working on, I recently, within the company, did one for the Women Power Summit, it was the largest summit in Bahrain for women, we had 500 woman attendees, and about 50 speakers, some of them were international as well, all women, men were welcome but a majority of them were women. And, just to really up the game in the gender equality. So we did the technology for that as well, so we did an online ticketing system, a credit card debit card payment and reservation, so it was a lot of fun. >> So who's coding all these apps? >> Oh, it's us, internally in the team. We have about nine in Bahrain, and most of them are women except for one, the digital marketer. >> People are getting down and dirty and coding away. >> Yes, though it does take a lot of training. So, in Bahrain, there's been a shift, so I also run a local development community, so the Google developer group in Manama, the Manama chapter, and we have like 651 members, so it's picking up. >> Wow. TensorFlow is like candy for developers. People love TensorFlow. >> We did do a few TensorFlow, yeah, definitely, AI, machine learning, deep learning, it's on right now. >> It really is. So how do you find all these people? Do they come to you, you got an open policy hiring refugees, >> Mm-hmm. >> So is there like a network, are you tapping your own network? >> So what we usually try to do is find people that are on the ground, and then they tell us "we have this kind of facility setup, so can you find us a job?" and I will never say no to people wanting jobs and to be very honest, in my line of work, especially when you're catering to charities and NGOs, you have an abundance of clients, it's overflowing. Because, they don't have the capital that they need in order to accomplish their projects. >> And even big firms are like conglomerates, now. We want to tap into these big agencies, you need to write a big check, >> Yeah, exactly. and you might not get the service you need anyway. And if you're an NGO, you don't get the love that the big commercial clients would get. >> Exactly. So, we cater to those kinds of clients. And also, we have, so our slogan is "empowering good". So we empower those who empower other people. We would never sell our clients something that they don't need, for example, we focus primarily on like, sometimes they want a website and an app and the whole nine yards, and they just need one page to accept payments and donations online, and that's all they really need, And some good branding. And so we make sure they focus that capital that they already have, and then, and really capitalize on that capital that they have, and give them the maximum bang for their buck, basically. >> That's awesome. Now, who do you work with? Do you have any partners, are you doing it solo, what are your... >> It's just me leading a staff of nine, and then we tap into a global market of ten, so we have associates that we bring on. I believe in the gig market, I don't think that having full-time staff long term is something that is sustainable. So I recently came back from China, from the annual meeting of champions, and the common theme over there, whether it was the future of work, the future of education, or even the future of... Wait, there was work, there was education, and there was one more. >> There was something. >> Yeah, oh, E-commerce, there we go. Digital, like online sales. And we noticed, there was a very big trend towards everything being digitalized. And with the exception of having community clubhouses, to have that human factor over there. So people love to still have that, you know, that engagement, that bonding, that I belong to that company, or I belong to that mission or vision, but everything is going online. So, I really believe that the gig market's going to, it's already started taking off. So, it's going to be a thing. >> And then being a part of community really brings the shared experiences together. And when you have that kind of co-creation or interaction, it makes people feel part of something bigger. And that's bonding. >> Exactly. So even if you look at e-commerce stores, a lot of them have their flagship stores, but they're not there for sales, because most of their sales get done online. They're there for giving that experience, so when you walk in a store, you have that experience. >> Yeah. >> That's super. What's the biggest thing that you've learned doing this? Is there anything that surprised you, things that jumped out at you, things you didn't expect, things that were successful, you go "wow, that was amazing", things you tripped over >> Oh, I tripped over a lot. (both laugh) It's constant tripping. >> Of course. Well that's how you learn! >> Yeah. So when I first started, I mean, people think like, okay, so you're going to help charities and you're going to help people and you're going to make money, how does that work? I learned very earlier on that people value what they pay for, so don't give things for free. You charge people, but charge them reasonably. So you say, okay I'll charge you this much for this kind of service, and this bit you can do by yourself. We'll give you the guidelines, we've done it over and over, and we'll tell you what your branding is, and then you go out, find the pictures, or write up the copywriting or whatever. So there was that, there was a lot of, so for example, "oh you can't hire refugees", and "why can't I hire refugees", there's a lot of trial and error. Obviously, you'd have people that will sell their (laughs) >> You got to give it a shot, give it a try. >> yeah, of course. And, the experience has been unbelievable. Some of them start out with wordpress development, and then end up with full-stack Javascript. Right? And you give people a chance, it's just like that example of professor Muhammed Yunus in Grameen bank, where they said "oh you can't loan to beggars" and he's like "why not?", and the default rate is so much more lower than the conventional banks. And they don't have any credit ratings! Right, so, I learned to not, I try not to follow the status quo, my mission is purely to empower people, whether I'm employing them, or I'm doing a project for them, so there's always that angle. And it's very different when you know what your values are. And you go in, so... >> And then you got having alignment. Well, Lana, thanks for coming on theCube this year, great to hear your story. >> Thank you. I think Bahrain is going to be a great tech scene. Obviously, I'm very encouraged by the entrepreneurs that I met. You can kind of spot an entrepreneur a mile away, they chirp a lot, they want to go faster, where's the cash, where's the collaboration? Not in a mean way, I don't mean that. And the other thing is that surprised me was Teresa's Women's Breakfast yesterday, that was blowing me away. How dynamic the group was, thought that was phenomenal. Just great to see this startup community here in Bahrain doing, just so much energy and support. >> We have a very tight ecosystem, I'm sure some of the other people probably have already mentioned this. Everybody talks to everybody, so you either, you're always one step away from whatever it is that you need. So, either you ask a person, they know exactly what you need, or they refer you to that person, and they give you like, name and number, right then and there, so, we're all intertwined. I think you've seen us all, how the ecosystem is amazing. >> It's important, and it's super important as communities with open source, you can see this when Amazon comes here, it's going to be a goodness of open source. >> Oh, it is! We're going to have navscale, we're going to have pull, new opportunities going to be coming in, >> Fantastic. it's going to be really, it should be a fun ride, I'm looking forward to covering you guys. >> I've been telling everyone that it is an amazing time to be a woman tech entrepreneur in this region. Amazing. >> Congratulations, and our doors are always open in Silicon Valley if you need anything. >> Awesome. >> Okay, there's more Cube coverage, stay with us! All day, wall to wall, stay with us. We got a couple more interviews, be right back after this short break. 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>> Commentator: Live from Austin, Texas, it's the Cube. Covering KubeCon and CloudNativeCon 2017. Brought to you by Red Hat, the Linux foundation, and the Cube's ecosystem partners. >> Hey welcome back everyone. Live here in Austin, Texas the Cube's exclusive coverage of KubeCon and CloudNativeCon, its third year, not even third year I think it's second year and not even three years old as a community, growing like crazy. Over 4500 people here. Combined the bulk of the shows it's double than it was before. I'm John Ferrier, co-founder of SiliconANGLE. Stu Miniman, analysts here. Next is Gabe Monroy who was lead p.m. product manager for containers for Microsoft Azure, Gabe welcome to the Cube. >> Thanks, glad to be here. Big fan of the show. >> Great to have you on. I mean obviously container madness we've gotten past that now it's Kubernetes madness which really means that the evolution of the industry is really starting to get some clear lines of sight as a straight and narrow if you will people starting to see a path towards scale, developer acceleration, more developers coming in than ever before, this cloud native world. Microsoft's doing pretty well with the cloud right now. Numbers are great, hiring a bunch of people, give us a quick update big news what's going on? >> Yeah so you know a lot of things going on. I'm just excited to be here, I think for me, I'm new to Microsoft right. I came here about seven months ago by way of a Dais acquisition and I like to think of myself as kind of representing part of this new Microsoft trend. My career was built on open source. I started a company called Dais and we were focused on really Kubernetes based solutions and here at Microsoft I'm really doing a lot of the same thing but with Microsoft's Cloud as sort of the vehicle that we're trying to attract developers to. >> What news do you guys have here, some services? >> Yeah so we got a bunch of things, we're talking about so the first is something I'm especially excited about. So this is the virtual kubelet. Now, tell a little bit of story here, I think it's actually kind of fascinating, so back in July we launched this thing called Azure Container Instances and what ACI was first of its kind service containers in the cloud. Just run a container, runs in the cloud. It's micro build and it is invisible infrastructure, so part of the definition of serverless there. As part of that we want to make it clear that if you were going to do complex things with these containers you really need an orchestrator so we released this thing called the ACI Connector for Kubernetes along with it. And we were excited to see people just were so drawn its idea of serverless Kubernetes, Kubernetes that you know didn't have any VMs associated with it and folks at hyper.sh, who have a similar service container offering, they took our code base and forked it and did a version of theirs and you know Brent and I were thinking together when we were like "oh man there's something here, we should explore this" and so we got some engineers together, we put a lot of work together and we announced now, this in conjunction with hyper and others, this virtual kubelet that bridges the world of Kubernetes with the world of these new serverless container runtimes like ACI. >> Okay, can you explain that a little bit. >> Sure. >> People have been coming in saying wait does serverless replace, how does it work, is Kubernetes underneath still? >> Yeah so I think the best place to start is the definition of serverless and I think serverless is really the conflation of three things: it's invisible infrastructure, it is micro billing, and it is an event based programming model. It's sort of the classical definition right. Now what we did with ACI and serverless containers is we took that last one, the event based programming model, and we said look you don't need to do that. If you want to write a container, anything that runs in that container can work, not just functions and so that is I think a really important distinction that I believe it's really the best of serverless is you know that micro billing and invisible infrastructure. >> Well that's built in isn't it? >> Correct yeah. >> What are the biggest challenges of serverless because first of all its [Inaudible 00:03:58] in the mind of a developer who doesn't want to deal with plumbing. >> Yes. >> Meaning networking plumbing, storage, and a lot of the details around configurating, just program away, be creative, spend their time building. >> Yes. >> What is the big differences between that? What are the issues and challenges that service has for people adopting it or is it frictionless at this point? >> Well you know as far I mean it depends on what you're talking about right. So I think you know for functions you know it's very simple to you know get a function service and add your functions and deploy functions and start chaining those together and people are seeing rapid adoption and that's progressing nicely but there's also a contingent of folks who are represented here at the show who are really interested in containers as the primitive and not functions right. Containers are inclusive of lots of things, functions being one of them, betting on containers as like the compute artifact is actually a lot more flexible and solves a lot more use cases. So we're making sure that we can streamline ease of use for that while also bringing the benefits of serverless, really the way I think of this is marrying our AKS, our Managed Kubernetes Service with ACI, our you know serverless containers so you can get to a place where you can have a Kubernetes environment that has no VMs associated with it like literally zero VMs, you'd scale the thing down to zero and when you want to run a pod or container you just pay for a few seconds of time and then you kill it and you stop paying for it right. >> Alright so talk about customers. >> Yep. >> What's the customer experience you guys are going after, did you have any beta customers, who's adopting your approach, and can highlight some examples of some really cool and you don't have to name names or you can, anecdotal data will be good. >> Yeah well you know I think on the blog post announcement blog post page we have a really great video of Siemens Health and Years, I believe is the name, but basically a health care company that is looking, that is using Kubernetes on Azure, AKS specifically, to disrupt the health care market and to benefit real people and you know to me I think it's important that we remember that we're deep in this technology right but at the end of the day this is about helping developers who are in turn helping real world people and I think that video is a good example of that. >> An what was there impact, speed? Speed of developers? >> Yeah, I mean I think it's really the main thing is agility right, people want to move faster right and so that's the main benefit that we hear. I think cost is obviously a concern for folks but I think in practice the people cost of operating some of these systems is tends to be a lot higher than the infrastructure costs when you stack them up, so people are willing to pay a little bit of a premium to make it easier on people and we see that over and over again. >> Yeah Gabe, want you to speak to kind of the speed of company the size of Microsoft. So you know the Dais acquisition of course was already focused on Kubernetes before inside of Microsoft, see I mean big cloud companies moving really fast on Kubernetes. I've heard complaints from customers like "I can't get a good roadmap because it's moving so fast". >> You know I would say that was one of the biggest surprises for me joining Microsoft, is just how fast things move inside of Azure in particular. And I think it's terrific you know. I think that there's a really good focus of making sure that we're meeting customers where they are and building solutions that meet the market but also just executing and delivering and doing that with speed. One of the things that is most interesting to me is like the geographic spread. Microsoft is in so many different regions more than any other cloud. Compliance certification, we take to all that stuff really seriously and being able to do all those things, be the enterprise friendly cloud while also moving at this breakneck pace in terms of innovation, it's really spectacular to watch from the inside. >> A lot of people don't know that. When they think about Azure they think "oh they're copying Amazon" but Microsoft has tons of data centers. They've had browsers, they're all over the world, so it's not like they're foreign to region areas I mean they're everywhere. >> Microsoft is ever and not only is it not foreign but I mean you got to remember Microsoft is an enterprise software company at its core. We know developers, that is what we do and going into cloud in this way is just it's extremely natural for us. And I think that the same can't really be said for everyone who's trying to move into cloud. Like we've got history of working with developers, building platforms, we've entire division devoted to developer tooling right. >> I want to ask you about two things that comes up a lot, one is very trendy, one is kind of not so trendy but super important, one is AI. >> Yes. >> AI with software units impact disrupt storage and with virtual kubelets this is going to be changing storage game buts going to enhance the machine learning and AI capability. The other one is data warehousing or data analytics. Two very important trends, one is certainly a driver for growth and has a lot of sex appeal as the AI machine learning but all the analytics being done on cloud whether it's an IOT device, this is like a nice use case for containers and orchestration. Your comment and reaction for those two trends. >> Yeah and you know I think that AI and deep learning generally is something that we see driving a ton of demand for container orchestration. I've worked lots of customers including folks like OpenAI on there Kubernetes infrastructure running on a Azure today. Something that Elon Musk actually proudly mention, that was a good moment for the containers (chuckling) >> Get a free Tesla. Brokerage some Teslas and get that new one, goes from 0 to 100 and 4.5 seconds. >> Right yeah. >> So you got a good customer, OpenAI, what was the impact of them? What was the big? >> Well you know this is ultimately about empowering people, in this case they happen to be data scientists, to get their job done in a way where I mean I look at it has we're doing our jobs in the infrastructure space if the infrastructure disappears. The more conceptual overhead we're bringing to developers that means we're not doing our job. >> So question then specifically is deep learning in AI, is it enhanced by containers and Kubernetes? >> Absolutely. >> What order of magnitude? >> I don't know but in order of magnitude in enhancement I would argue. >> Just underlying that the really important piece is we're talking about data here >> Yes. >> and one of the things we've been kind of trying to tackle the last couple years of containers is you know storage and that's carried over to Kubernetes, how's Microsoft involved? What's you're you know prognosis as to where we go with cloud native storage? >> Yeah that's a fascinating question and I actually, so back in the early days when I was still contributing to Docker, I was one of the largest external contributors to the Docker Project earlier in my career. I actually wrote some of the storage stuff and so I've been going around Dockers inception 2013 saying don't run databases in containers. It's not cause you can't, right, you can, but just because you can doesn't mean you should (chuckling) >> Exactly. >> and I think that you know as somebody who has worked in my career as on the operation side things like an SLA mean a lot and so this leads me to another one of our announcements at the show which is the Open Service Broker for Azure. Now what we've done, thanks to the Cloud Foundry Foundation who basically took the service broker concept and spun it out, we now are able to take the world of Kubernetes and bridge it to the world of Azure services, data services being sort of some of the most interesting. Now the demo that I like to show this is WordPress which by the way sounds silly but WordPress powers tons of the web today still. WordPress is a PHP application and a MySQL database. Well if you're going to run WordPress at scale you're going to want to run that MySQL in a container? Probably not, you're probably going to want to use something like Azure database for MySQL which comes with an SLA, backup/restore, DR, ops team by Microsoft to manage the whole thing right. So but then the question is well I want to use Kubernetes right so how do I do that right, well with the Open Service Broker for Azure we actually shipped a helm chart. We can helm install Azure WordPress and it will install in Kubernetes the same way you would a container based system and behind the scenes it uses the broker to go spin up a Postgres, sorry a MySQL and dynamically attach it. Now the coolest thing to me about this yeah is the agility but I think that one of the underrated features is the security. The developer who does that doesn't ever touch credentials, the passwords are automatically generated and automatically injected into the application so you get to do things with rotations without ever touching the app. >> So we're at publisher we use WordPress, we'd love, will this help us with scale if we did Azure? >> Absolutely. After this is over we'll go set it up. (laughing) >> I love WordPress but when it breaks down well this is the whole point of where auto scaling shows a little bit of its capabilities in the world is that, PHP does you'd like to have more instances >> Yeah. >> that would be a use case. Okay Redshift in Amazon wasn't talking about much at re:Invent last week. We don't hear a lot of talk around the data warehouse which is a super important way to think about collecting data in cloud and is that going to be an enhanced feature because people want to do analytics. There's a huge analytics audience out there, they're moving off of tera-data. They're doing you guys have a lot of analytics at Microsoft. They might have moved from Hadoop or Hive or somewhere else so there's a lot of analytics workloads that would be prime or at least potentially prime for Kubernetes. >> Yeah I think >> Or is that not fully integrated. >> No I think it's interesting, I mean for us we look at, I personally think using something like the service broker, Open Service Broker API to bridge to something like a data lake or some of these other Azure hosted services is probably the better way of doing that because if you're going to run it on containers, these massive data warehouses, yes you can do it, but the operational burden is high, >> So your point about the >> its really high. >> database earlier. >> Yeah. Same general point there. Now can you do it? Do we see people doing it? Absolutely right. >> Yeah, they do you things sometimes that they shouldn't be doing. >> Yeah and of course back to the deep learning example those are typically big large training models that have similar characteristics. >> Alright as a newbie inside Azure, not new to the industry and the community, >> Yep. >> share some color. What's it like in there? Obviously a number two to Amazon, you guys have great geography presence, you're adding more and more services every day at Azure, what's the vibe, what's the mojo like over there, and share some inside baseball. >> Yeah I got to say so really I'm just saying it's a really exciting place to work. Things are moving so fast, we're growing so fast, customers really want what we're building. Honestly day to day I'm not spending a lot of time looking out I'm spending a lot of time dealing with enterprises who want to use our cloud products. >> And one of the top things that you have on your p.m. list that are the top stack ranked features people want? >> I think a lot of this comes down, in general I think this whole space is approaching a level of enterprise friendliness and enterprise hardening where we want to start adding governance, and adding security, and adding role based access controls across the board and really making this palatable to high trust environment. So I think a lot that's a lot of our focus. >> Stability, ease of use. >> Stability, ease of use are always there. I think the enterprise hardening and things like v-net support for all of our services, v-net service endpoints, those are some things that are high on the list. >> Gabe Monroy, lead product manager for containers at Microsoft Azure Cloud. Great to have you on and love to talk more about geographies and moving apps around the network and multi-cloud but another time, thanks for the time. >> Another time. >> It's the Cube live coverage I'm John Ferrier co-founder of [Inaudible 00:15:21]. Stu Miniman with Wikibon, back with more live coverage after this short break.

>> Announcer: Live from Orlando, Florida. It's the CUBE. Covering Grace Hopper Celebration of Women in Computing. Brought to you by Silicon Angle Media. >> Welcome back everybody. Jeff Frick here with the Cube. We are winding down day three of the Grace Hopper Celebration of Women in Computing in Orlando. It's 18,000, mainly women, a couple of us men hangin' out. It's been a phenomenal event again. It always amazes me to run into first timers that have never been to the Grace Hopper event. It's a must do if you're in this business and I strongly encourage you to sign up quickly 'cause I think it sells out in about 15 minutes, like a good rock concert. But we're excited to have our next guest. She's Rachel Faber Tobac, UX Research at Course Hero. Rachel, great to see you. >> Thank you so much for having me on. >> Absolutely. So, Course Hero. Give people kind of an overview of what Course Hero is all about. >> Yup. So we are an online learning platform and we help about 200 million students and educators master their classes every year. So we have all the notes, >> 200 million. >> Yes, 200 million! We have all the notes, study guides, resources, anything a student would need to succeed in their classes. And then anything an educator would need to prepare for their classes or connect with their students. >> And what ages of students? What kind of grades? >> They're usually in college, but sometimes we help high schoolers, like AP students. >> Okay. >> Yeah. >> But that's not why you're here. You want to talk about hacking. So you are, what you call a "white hat hacker". >> White hat. >> So for people that aren't familiar with the white hat, >> Yeah. >> We all know about the black hat conference. What is a white hat hacker. >> So a "white hat hacker" is somebody >> Sounds hard to say three times fast. >> I know, it's a tongue twister. A white hat hacker is somebody who is a hacker, but they're doing it to help people. They're trying to make sure that information is kept safer rather than kind of letting it all out on the internet. >> Right, right. Like the old secret shoppers that we used to have back in the pre-internet days. >> Exactly. Exactly. >> So how did you get into that? >> It's a very non-linear story. Are you ready for it? >> Yeah. >> So I started my career as a special education teacher. And I was working with students with special needs. And I wanted to help more people. So, I ended up joining Course Hero. And I was able to help more people at scale, which was awesome. But I was interested in kind of more of the technical side, but I wasn't technical. So my husband went to Defcon. 'cause he's a cyber security researcher. And he calls me at Defcon about three years ago, and he's like, Rach, you have to get over here. I'm like, I'm not really technical. It's all going to go over my head. Why would I come? He's like, you know how you always call companies to try and get our bills lowered? Like calling Comcast. Well they have this competition where they put people in a glass booth and they try and have them do that, but it's hacking companies. You have to get over here and try it. So I bought a ticket to Vegas that night and I ended up doing the white hat hacker competition called The Social Engineering Capture the Flag and I ended up winning second, twice in a row as a newb. So, insane. >> So you're hacking, if I get this right, not via kind of hardcore command line assault. You're using other tools. So like, what are some of the tools that are vulnerabilities that people would never think about. >> So the biggest tool that I use is actually Instagram, which is really scary. 60% of the information that I need to hack a company, I find on Instagram via geolocation. So people are taking pictures of their computers, their work stations. I can get their browser, their version information and then I can help infiltrate that company by calling them over the phone. It's called vishing. So I'll call them and try and get them to go to a malicious link over the phone and if I can do that, I can own their company, by kind of presenting as an insider and getting in that way. (chuckling) It's terrifying. >> So we know phishing right? I keep wanting to get the million dollars from the guy in Africa that keeps offering it to me. >> (snickers) Right. >> I don't whether to bite on that or. >> Don't click the link. >> Don't click the link. >> No. >> But that interesting. So people taking selfies in the office and you can just get a piece of the browser data and the background of that information. >> Yep. >> And that gives you what you need to do. >> Yeah, so I'll find a phone number from somebody. Maybe they take a picture of their business card, right? I'll call that number. Test it to see if it works. And then if it does, I'll call them in that glass booth in front of 400 people and attempt to get them to go to malicious links over the phone to own their company or I can try and get more information about their work station, so we could, quote unquote, tailor an exploit for their software. >> Right. Right. >> We're not actually doing this, right? We're white hat hackers. >> Right. >> If we were the bad guys. >> You'd try to expose the vulnerability. >> Right. The risk. >> And what is your best ruse to get 'em to. Who are you representing yourself as? >> Yeah, so. The representation thing is called pre-texting. It's who you're pretending to be. If you've ever watched like, Catch Me If You Can. >> Right. Right. >> With Frank Abagnale Jr. So for me, the thing that works the best are low status pretext. So as a woman, I would kind of use what we understand about society to kind of exploit that. So you know, right now if I'm a woman and I call you and I'm like, I don't know how to trouble shoot your website. I'm so confused. I have to give a talk, it's in five minutes. Can you just try my link and see if it works on your end? (chuckling) >> You know? Right? You know, you believe that. >> That's brutal. >> Because there's things about our society that help you understand and believe what I'm trying to say. >> Right, right. >> Right? >> That's crazy and so. >> Yeah. >> Do you get, do you make money white hacking for companies? >> So. >> Do they pay you to do this or? Or is it like, part of the service or? >> It didn't start that way. >> Right. >> I started off just doing the Social Engineering Capture the Flag, the SECTF at Defcon. And I've done that two years in a row, but recently, my husband, Evan and I, co-founded a company, Social Proof Security. So we work with companies to train them about how social media can impact them from a social engineering risk perspective. >> Right. >> And so we can come in and help them and train them and understand, you know, via a webinar, 10 minute talk or we can do a deep dive and have them actually step into the shoes of a hacker and try it out themselves. >> Well I just thought the only danger was they know I'm here so they're going to go steal my bike out of my house, 'cause that's on the West Coast. I'm just curious and you may not have a perspective. >> Yeah. >> 'Cause you have niche that you execute, but between say, you know kind of what you're doing, social engineering. >> Yeah. >> You know, front door. >> God, on the telephone. Versus kind of more traditional phishing, you know, please click here. Million dollars if you'll click here versus, you know, what I would think was more hardcore command line. People are really goin' in. I mean do you have any sense for what kind of the distribution of that is, in terms of what people are going after? >> Right, we don't know exactly because usually that information's pretty confidential, >> Sure. when a hack happens. But we guess that about 90% of infiltrations start with either a phishing email or a vishing call. So they're trying to gain information so they can tailor their exploits for your specific machine. And then they'll go in and they'll do that like actual, you know, >> Right. >> technical hacking. >> Right. >> But, I mean, if I'm vishing you right and I'm talking to you over the phone and I get you to go to a malicious link, I can just kind of bypass every security protocol you've set up. I don't even a technical hacker, right? I just got into your computer because. >> 'Cause you're in 'Cause I'm in now, yup. >> I had the other kind of low profile way and I used to hear is, you know, you go after the person that's doin' the company picnic. You know Wordpress site. >> Yes. >> That's not thinking that that's an entry point in. You know, kind of these less obvious access points. >> Right. That's something that I talk about a lot actually is sometimes we go after mundane information. Something like, what pest service provider you use? Or what janitorial service you use? We're not even going to look for like, software on your machine. We might start with a softer target. So if I know what pest extermination provider you use, I can look them up on LinkedIn. See if they've tagged themselves in pictures in your office and now I can understand how do they work with you, what do their visitor badges look like. And then emulate all of that for an onsite attack. Something like, you know, really soft, right? >> So you're sitting in the key note, right? >> Yeah. >> Fei-Fei Li is talking about computer visualization learning. >> Right. >> And you know, Google running kagillions of pictures through an AI tool to be able to recognize the puppy from the blueberry muffin. >> Right. >> Um, I mean, that just represents ridiculous exploitation opportunity at scale. Even you know, >> Yeah. >> You kind of hackin' around the Instagram account, can't even begin to touch, as you said, your other thing. >> Right. >> You did and then you did it at scale. Now the same opportunity here. Both for bad and for good. >> I'm sure AI is going to impact social engineering pretty extremely in the future here. Hopefully they're protecting that data. >> Okay so, give a little plug so they'll look you up and get some more information. But what are just some of the really easy, basic steps that you find people just miss, that should just be, they should not be missing. From these basic things. >> The first thing is that if they want to take a picture at work, like a #TBT, right? It's their third year anniversary at their company. >> Right. Right. >> Step away from your work station. You don't need to take that picture in front of your computer. Because if you do, I'm going to see that little bottom line at the bottom and I'm going to see exactly the browser version, OS and everything like that. Now I'm able to exploit you with that information. So step away when you take your pictures. And if you do happen to take a picture on your computer. I know you're looking at computer nervously. >> I know, I'm like, don't turn my computer on to the cameras. >> Don't look at it! >> You're scarin' me Rachel. >> If you do take a picture of that. Then you don't want let someone authenticate with that information. So let's say I'm calling you and I'm like, hey, I'm with Google Chrome. I know that you use Google Chrome for your service provider. Has your network been slow recently? Everyone's network's been slow recently, right? >> Right. Right. >> So of course you're going to say yes. Don't let someone authenticate with that info. Think to yourself. Oh wait, I posted a picture of my work station recently. I'm not going to let them authenticate and I'm going to hang up. >> Interesting. All right Rachel. Well, I think the opportunity in learning is one thing. The opportunity in this other field is infinite. >> Yeah. >> So thanks for sharing a couple of tips. >> Yes. >> And um. >> Thank you for having me. >> Hopefully we'll keep you on the good side. We won't let you go to the dark side. >> I won't. I promise. >> All right. >> Rachel Faber Tobac and I'm Jeff Frick. You're watchin the Cube from Grace Hopper Celebration Women in Computing. Thanks for watching. (techno music)

>> Narrator: Live, from Munich, Germany, it's the CUBE. Covering IBM, Fast Track Your Data. Brought to you by IBM. >> Welcome to Munich everybody. This is a special presentation of the CUBE, Fast Track Your Data, brought to you by IBM. My name is Dave Vellante. And I'm here with my cohost, Jim Kobielus. Jim, good to see you. Really good to see you in Munich. >> Jim: I'm glad I made it. >> Thanks for being here. So last year Jim and I hosted a panel at New York City on the CUBE. And it was quite an experience. We had, I think it was nine or 10 data scientists and we felt like that was a lot of people to organize and talk about data science. Well today, we're going to do a repeat of that. With a little bit of twist on topics. And we've got five data scientists. We're here live, in Munich. And we're going to kick off the Fast Track Your Data event with this data science panel. So I'm going to now introduce some of the panelists, or all of the panelists. Then we'll get into the discussions. I'm going to start with Lillian Pierson. Lillian thanks very much for being on the panel. You are in data science. You focus on training executives, students, and you're really a coach but with a lot of data science expertise based in Thailand, so welcome. >> Thank you, thank you so much for having me. >> Dave: You're very welcome. And so, I want to start with sort of when you focus on training people, data science, where do you start? >> Well it depends on the course that I'm teaching. But I try and start at the beginning so for my Big Data course, I actually start back at the fundamental concepts and definitions they would even need to understand in order to understand the basics of what Big Data is, data engineering. So, terms like data governance. Going into the vocabulary that makes up the very introduction of the course, so that later on the students can really grasp the concepts I present to them. You know I'm teaching a deep learning course as well, so in that case I start at a lot more advanced concepts. So it just really depends on the level of the course. >> Great, and we're going to come back to this topic of women in tech. But you know, we looked at some CUBE data the other day. About 17% of the technology industry comprises women. And so we're a little bit over that on our data science panel, we're about 20% today. So we'll come back to that topic. But I don't know if there's anything you would add? >> I'm really passionate about women in tech and women who code, in particular. And I'm connected with a lot of female programmers through Instagram. And we're supporting each other. So I'd love to take any questions you have on what we're doing in that space. At least as far as what's happening across the Instagram platform. >> Great, we'll circle back to that. All right, let me introduce Chris Penn. Chris, Boston based, all right, SMI. Chris is a marketing expert. Really trying to help people understand how to get, turn data into value from a marketing perspective. It's a very important topic. Not only because we get people to buy stuff but also understanding some of the risks associated with things like GDPR, which is coming up. So Chris, tell us a little bit about your background and your practice. >> So I actually started in IT and worked at a start up. And that's where I made the transition to marketing. Because marketing has much better parties. But what's really interesting about the way data science is infiltrating marketing is the technology came in first. You know, everything went digital. And now we're at a point where there's so much data. And most marketers, they kind of got into marketing as sort of the arts and crafts field. And are realizing now, they need a very strong, mathematical, statistical background. So one of the things, Adam, the reason why we're here and IBM is helping out tremendously is, making a lot of the data more accessible to people who do not have a data science background and probably never will. >> Great, okay thank you. I'm going to introduce Ronald Van Loon. Ronald, your practice is really all about helping people extract value out of data, driving competitive advantage, business advantage, or organizational excellence. Tell us a little bit about yourself, your background, and your practice. >> Basically, I've three different backgrounds. On one hand, I'm a director at a data consultancy firm called Adversitement. Where we help companies to become data driven. Mainly large companies. I'm an advisory board member at Simply Learn, which is an e-learning platform, especially also for big data analytics. And on the other hand I'm a blogger and I host a series of webinars. >> Okay, great, now Dez, Dez Blanchfield, I met you on Twitter, you know, probably a couple of years ago. We first really started to collaborate last year. We've spend a fair amount of time together. You are a data scientist, but you're also a jack of all trades. You've got a technology background. You sit on a number of boards. You work very active with public policy. So tell us a little bit more about what you're doing these days, a little bit more about your background. >> Sure, I think my primary challenge these days is communication. Trying to join the dots between my technical background and deeply technical pedigree, to just plain English, every day language, and business speak. So bridging that technical world with what's happening in the boardroom. Toe to toe with the geeks to plain English to execs in boards. And just hand hold them and steward them through the journey of the challenges they're facing. Whether it's the enormous rapid of change and the pace of change, that's just almost exhaustive and causing them to sprint. But not just sprint in one race but in multiple lanes at the same time. As well as some of the really big things that are coming up, that we've seen like GDPR. So it's that communication challenge and just hand holding people through that journey and that mix of technical and commercial experience. >> Great, thank you, and finally Joe Caserta. Founder and president of Caserta Concepts. Joe you're a practitioner. You're in the front lines, helping organizations, similar to Ronald. Extracting value from data. Translate that into competitive advantage. Tell us a little bit about what you're doing these days in Caserta Concepts. >> Thanks Dave, thanks for having me. Yeah, so Caserta's been around. I've been doing this for 30 years now. And natural progressions have been just getting more from application development, to data warehousing, to big data analytics, to data science. Very, very organically, that's just because it's where businesses need the help the most, over the years. And right now, the big focus is governance. At least in my world. Trying to govern when you have a bunch of disparate data coming from a bunch of systems that you have no control over, right? Like social media, and third party data systems. Bringing it in and how to you organize it? How do you ingest it? How do you govern it? How do you keep it safe? And also help to define ownership of the data within an organization within an enterprise? That's also a very hot topic. Which ties back into GDPR. >> Great, okay, so we're going to be unpacking a lot of topics associated with the expertise that these individuals have. I'm going to bring in Jim Kobielus, to the conversation. Jim, the newest Wikibon analyst. And newest member of the SiliconANGLE Media Team. Jim, get us started off. >> Yeah, so we're at an event, at an IBM event where machine learning and data science are at the heart of it. There are really three core themes here. Machine learning and data science, on the one hand. Unified governance on the other. And hybrid data management. I want to circle back or focus on machine learning. Machine learning is the coin of the realm, right now in all things data. Machine learning is the heart of AI. Machine learning, everybody is going, hiring, data scientists to do machine learning. I want to get a sense from our panel, who are experts in this area, what are the chief innovations and trends right now on machine learning. Not deep learning, the core of machine learning. What's super hot? What's in terms of new techniques, new technologies, new ways of organizing teams to build and to train machine learning models? I'd like to open it up. Let's just start with Lillian. What are your thoughts about trends in machine learning? What's really hot? >> It's funny that you excluded deep learning from the response for this, because I think the hottest space in machine learning is deep learning. And deep learning is machine learning. I see a lot of collaborative platforms coming out, where people, data scientists are able to work together with other sorts of data professionals to reduce redundancies in workflows. And create more efficient data science systems. >> Is there much uptake of these crowd sourcing environments for training machine learning wells. Like CrowdFlower, or Amazon Mechanical Turk, or Mighty AI? Is that a huge trend in terms of the workflow of data science or machine learning, a lot of that? >> I don't see that crowdsourcing is like, okay maybe I've been out of the crowdsourcing space for a while. But I was working with Standby Task Force back in 2013. And we were doing a lot of crowdsourcing. And I haven't seen the industry has been increasing, but I could be wrong. I mean, because there's no, if you're building automation models, most of the, a lot of the work that's being crowdsourced could actually be automated if someone took the time to just build the scripts and build the models. And so I don't imagine that, that's going to be a trend that's increasing. >> Well, automation machine learning pipeline is fairly hot, in terms of I'm seeing more and more research. Google's doing a fair amount of automated machine learning. The panel, what do you think about automation, in terms of the core modeling tasks involved in machine learning. Is that coming along? Are data scientists in danger of automating themselves out of a job? >> I don't think there's a risk of data scientist's being put out of a job. Let's just put that on the thing. I do think we need to get a bit clearer about this meme of the mythical unicorn. But to your call point about machine learning, I think what you'll see, we saw the cloud become baked into products, just as a given. I think machine learning is already crossed this threshold. We just haven't necessarily noticed or caught up. And if we look at, we're at an IBM event, so let's just do a call out for them. The data science experience platform, for example. Machine learning's built into a whole range of things around algorithm and data classification. And there's an assisted, guided model for how you get to certain steps, where you don't actually have to understand how machine learning works. You don't have to understand how the algorithms work. It shows you the different options you've got and you can choose them. So you might choose regression. And it'll give you different options on how to do that. So I think we've already crossed this threshold of baking in machine learning and baking in the data science tools. And we've seen that with Cloud and other technologies where, you know, the Office 365 is not, you can't get a non Cloud Office 365 account, right? I think that's already happened in machine learning. What we're seeing though, is organizations even as large as the Googles still in catch up mode, in my view, on some of the shift that's taken place. So we've seen them write little games and apps where people do doodles and then it runs through the ML library and says, "Well that's a cow, or a unicorn, or a duck." And you get awards, and gold coins, and whatnot. But you know, as far as 12 years ago I was working on a project, where we had full size airplanes acting as drones. And we mapped with two and 3-D imagery. With 2-D high res imagery and LiDAR for 3-D point Clouds. We were finding poles and wires for utility companies, using ML before it even became a trend. And baking it right into the tools. And used to store on our web page and clicked and pointed on. >> To counter Lillian's point, it's not crowdsourcing but crowd sharing that's really powering a lot of the rapid leaps forward. If you look at, you know, DSX from IBM. Or you look at Node-RED, huge number of free workflows that someone has probably already done the thing that you are trying to do. Go out and find in the libraries, through Jupyter and R Notebooks, there's an ability-- >> Chris can you define before you go-- >> Chris: Sure. >> This is great, crowdsourcing versus crowd sharing. What's the distinction? >> Well, so crowdsourcing, kind of, where in the context of the question you ask is like I'm looking for stuff that other people, getting people to do stuff that, for me. It's like asking people to mine classifieds. Whereas crowd sharing, someone has done the thing already, it already exists. You're not purpose built, saying, "Jim, help me build this thing." It's like, "Oh Jim, you already "built this thing, cool. "So can I fork it and make my own from it?" >> Okay, I see what you mean, keep going. >> And then, again, going back to earlier. In terms of the advancements. Really deep learning, it probably is a good idea to just sort of define these things. Machine learning is how machines do things without being explicitly programmed to do them. Deep learning's like if you can imagine a stack of pancakes, right? Each pancake is a type of machine learning algorithm. And your data is the syrup. You pour the data on it. It goes from layer, to layer, to layer, to layer, and what you end up with at the end is breakfast. That's the easiest analogy for what deep learning is. Now imagine a stack of pancakes, 500 or 1,000 high, that's where deep learning's going now. >> Sure, multi layered machine learning models, essentially, that have the ability to do higher levels of abstraction. Like image analysis, Lillian? >> I had a comment to add about automation and data science. Because there are a lot of tools that are able to, or applications that are able to use data science algorithms and output results. But the reason that data scientists aren't in risk of losing their jobs, is because just because you can get the result, you also have to be able to interpret it. Which means you have to understand it. And that involves deep math and statistical understanding. Plus domain expertise. So, okay, great, you took out the coding element but that doesn't mean you can codify a person's ability to understand and apply that insight. >> Dave: Joe, you have something to add? >> I could just add that I see the trend. Really, the reason we're talking about it today is machine learning is not necessarily, it's not new, like Dez was saying. But what's different is the accessibility of it now. It's just so easily accessible. All of the tools that are coming out, for data, have machine learning built into it. So the machine learning algorithms, which used to be a black art, you know, years ago, now is just very easily accessible. That you can get, it's part of everyone's toolbox. And the other reason that we're talking about it more, is that data science is starting to become a core curriculum in higher education. Which is something that's new, right? That didn't exist 10 years ago? But over the past five years, I'd say, you know, it's becoming more and more easily accessible for education. So now, people understand it. And now we have it accessible in our tool sets. So now we can apply it. And I think that's, those two things coming together is really making it becoming part of the standard of doing analytics. And I guess the last part is, once we can train the machines to start doing the analytics, right? And get smarter as it ingests more data. And then we can actually take that and embed it in our applications. That's the part that you still need data scientists to create that. But once we can have standalone appliances that are intelligent, that's when we're going to start seeing, really, machine learning and artificial intelligence really start to take off even more. >> Dave: So I'd like to switch gears a little bit and bring Ronald on. >> Okay, yes. >> Here you go, there. >> Ronald, the bromide in this sort of big data world we live in is, the data is the new oil. You got to be a data driven company and many other cliches. But when you talk to organizations and you start to peel the onion. You find that most companies really don't have a good way to connect data with business impact and business value. What are you seeing with your clients and just generally in the community, with how companies are doing that? How should they do that? I mean, is that something that is a viable approach? You don't see accountants, for example, quantifying the value of data on a balance sheet. There's no standards for doing that. And so it's sort of this fuzzy concept. How are and how should organizations take advantage of data and turn it into value. >> So, I think in general, if you look how companies look at data. They have departments and within the departments they have tools specific for this department. And what you see is that there's no central, let's say, data collection. There's no central management of governance. There's no central management of quality. There's no central management of security. Each department is manages their data on their own. So if you didn't ask, on one hand, "Okay, how should they do it?" It's basically go back to the drawing table and say, "Okay, how should we do it?" We should collect centrally, the data. And we should take care for central governance. We should take care for central data quality. We should take care for centrally managing this data. And look from a company perspective and not from a department perspective what the value of data is. So, look at the perspective from your whole company. And this means that it has to be brought on one end to, whether it's from C level, where most of them still fail to understand what it really means. And what the impact can be for that company. >> It's a hard problem. Because data by its' very nature is now so decentralized. But Chris you have a-- >> The thing I want to add to that is, think about in terms of valuing data. Look at what it would cost you for data breach. Like what is the expensive of having your data compromised. If you don't have governance. If you don't have policy in place. Look at the major breaches of the last couple years. And how many billions of dollars those companies lost in market value, and trust, and all that stuff. That's one way you can value data very easily. "What will it cost us if we mess this up?" >> So a lot of CEOs will hear that and say, "Okay, I get it. "I have to spend to protect myself, "but I'd like to make a little money off of this data thing. "How do I do that?" >> Well, I like to think of it, you know, I think data's definitely an asset within an organization. And is becoming more and more of an asset as the years go by. But data is still a raw material. And that's the way I think about it. In order to actually get the value, just like if you're creating any product, you start with raw materials and then you refine it. And then it becomes a product. For data, data is a raw material. You need to refine it. And then the insight is the product. And that's really where the value is. And the insight is absolutely, you can monetize your insight. >> So data is, abundant insights are scarce. >> Well, you know, actually you could say that intermediate between insights and the data are the models themselves. The statistical, predictive, machine learning models. That are a crystallization of insights that have been gained by people called data scientists. What are your thoughts on that? Are statistical, predictive, machine learning models something, an asset, that companies, organizations, should manage governance of on a centralized basis or not? >> Well the models are essentially the refinery system, right? So as you're refining your data, you need to have process around how you exactly do that. Just like refining anything else. It needs to be controlled and it needs to be governed. And I think that data is no different from that. And I think that it's very undisciplined right now, in the market or in the industry. And I think maturing that discipline around data science, I think is something that's going to be a very high focus in this year and next. >> You were mentioning, "How do you make money from data?" Because there's all this risk associated with security breaches. But at the risk of sounding simplistic, you can generate revenue from system optimization, or from developing products and services. Using data to develop products and services that better meet the demands and requirements of your markets. So that you can sell more. So either you are using data to earn more money. Or you're using data to optimize your system so you have less cost. And that's a simple answer for how you're going to be making money from the data. But yes, there is always the counter to that, which is the security risks. >> Well, and my question really relates to, you know, when you think of talking to C level executives, they kind of think about running the business, growing the business, and transforming the business. And a lot of times they can't fund these transformations. And so I would agree, there's many, many opportunities to monetize data, cut costs, increase revenue. But organizations seem to struggle to either make a business case. And actually implement that transformation. >> Dave, I'd love to have a crack at that. I think this conversation epitomizes the type of things that are happening in board rooms and C suites already. So we've really quickly dived into the detail of data. And the detail of machine learning. And the detail of data science, without actually stopping and taking a breath and saying, "Well, we've "got lots of it, but what have we got? "Where is it? "What's the value of it? "Is there any value in it at all?" And, "How much time and money should we invest in it?" For example, we talk of being about a resource. I look at data as a utility. When I turn the tap on to get a drink of water, it's there as a utility. I counted it being there but I don't always sample the quality of the water and I probably should. It could have Giardia in it, right? But what's interesting is I trust the water at home, in Sydney. Because we have a fairly good experience with good quality water. If I were to go to some other nation. I probably wouldn't trust that water. And I think, when you think about it, what's happening in organizations. It's almost the same as what we're seeing here today. We're having a lot of fun, diving into the detail. But what we've forgotten to do is ask the question, "Well why is data even important? "What's the reasoning to the business? "Why are we in business? "What are we doing as an organization? "And where does data fit into that?" As opposed to becoming so fixated on data because it's a media hyped topic. I think once you can wind that back a bit and say, "Well, we have lot's of data, "but is it good data? "Is it quality data? "Where's it coming from? "Is it ours? "Are we allowed to have it? "What treatment are we allowed to give that data?" As you said, "Are we controlling it? "And where are we controlling it? "Who owns it?" There's so many questions to be asked. But the first question I like to ask people in plain English is, "Well is there any value "in data in the first place? "What decisions are you making that data can help drive? "What things are in your organizations, "KPIs and milestones you're trying to meet "that data might be a support?" So then instead of becoming fixated with data as a thing in itself, it becomes part of your DNA. Does that make sense? >> Think about what money means. The Economists' Rhyme, "Money is a measure for, "a systems for, a medium, a measure, and exchange." So it's a medium of exchange. A measure of value, a way to exchange something. And a way to store value. Data, good clean data, well governed, fits all four of those. So if you're trying to figure out, "How do we make money out of stuff." Figure out how money works. And then figure out how you map data to it. >> So if we approach and we start with a company, we always start with business case, which is quite clear. And defined use case, basically, start with a team on one hand, marketing people, sales people, operational people, and also the whole data science team. So start with this case. It's like, defining, basically a movie. If you want to create the movie, You know where you're going to. You know what you want to achieve to create the customer experience. And this is basically the same with a business case. Where you define, "This is the case. "And this is how we're going to derive value, "start with it and deliver value within a month." And after the month, you check, "Okay, where are we and how can we move forward? "And what's the value that we've brought?" >> Now I as well, start with business case. I've done thousands of business cases in my life, with organizations. And unless that organization was kind of a data broker, the business case rarely has a discreet component around data. Is that changing, in your experience? >> Yes, so we guide companies into be data driven. So initially, indeed, they don't like to use the data. They don't like to use the analysis. So that's why, how we help. And is it changing? Yes, they understand that they need to change. But changing people is not always easy. So, you see, it's hard if you're not involved and you're not guiding it, they fall back in doing the daily tasks. So it's changing, but it's a hard change. >> Well and that's where this common parlance comes in. And Lillian, you, sort of, this is what you do for a living, is helping people understand these things, as you've been sort of evangelizing that common parlance. But do you have anything to add? >> I wanted to add that for organizational implementations, another key component to success is to start small. Start in one small line of business. And then when you've mastered that area and made it successful, then try and deploy it in more areas of the business. And as far as initializing big data implementation, that's generally how to do it successfully. >> There's the whole issue of putting a value on data as a discreet asset. Then there's the issue, how do you put a value on a data lake? Because a data lake, is essentially an asset you build on spec. It's an exploratory archive, essentially, of all kinds of data that might yield some insights, but you have to have a team of data scientists doing exploration and modeling. But it's all on spec. How do you put a value on a data lake? And at what point does the data lake itself become a burden? Because you got to store that data and manage it. At what point do you drain that lake? At what point, do the costs of maintaining that lake outweigh the opportunity costs of not holding onto it? >> So each Hadoop note is approximately $20,000 per year cost for storage. So I think that there needs to be a test and a diagnostic, before even inputting, ingesting the data and storing it. "Is this actually going to be useful? "What value do we plan to create from this?" Because really, you can't store all the data. And it's a lot cheaper to store data in Hadoop then it was in traditional systems but it's definitely not free. So people need to be applying this test before even ingesting the data. Why do we need this? What business value? >> I think the question we need to also ask around this is, "Why are we building data lakes "in the first place? "So what's the function it's going to perform for you?" There's been a huge drive to this idea. "We need a data lake. "We need to put it all somewhere." But invariably they become data swamps. And we only half jokingly say that because I've seen 90 day projects turn from a great idea, to a really bad nightmare. And as Lillian said, it is cheaper in some ways to put it into a HDFS platform, in a technical sense. But when we look at all the fully burdened components, it's actually more expensive to find Hadoop specialists and Spark specialists to maintain that cluster. And invariably I'm finding that big data, quote unquote, is not actually so much lots of data, it's complex data. And as Lillian said, "You don't always "need to store it all." So I think if we go back to the question of, "What's the function of a data lake in the first place? "Why are we building one?" And then start to build some fully burdened cost components around that. We'll quickly find that we don't actually need a data lake, per se. We just need an interim data store. So we might take last years' data and tokenize it, and analyze it, and do some analytics on it, and just keep the meta data. So I think there is this rush, for a whole range of reasons, particularly vendor driven. To build data lakes because we think they're a necessity, when in reality they may just be an interim requirement and we don't need to keep them for a long term. >> I'm going to attempt to, the last few questions, put them all together. And I think, they all belong together because one of the reasons why there's such hesitation about progress within the data world is because there's just so much accumulated tech debt already. Where there's a new idea. We go out and we build it. And six months, three years, it really depends on how big the idea is, millions of dollars is spent. And then by the time things are built the idea is pretty much obsolete, no one really cares anymore. And I think what's exciting now is that the speed to value is just so much faster than it's ever been before. And I think that, you know, what makes that possible is this concept of, I don't think of a data lake as a thing. I think of a data lake as an ecosystem. And that ecosystem has evolved so much more, probably in the last three years than it has in the past 30 years. And it's exciting times, because now once we have this ecosystem in place, if we have a new idea, we can actually do it in minutes not years. And that's really the exciting part. And I think, you know, data lake versus a data swamp, comes back to just traditional data architecture. And if you architect your data lake right, you're going to have something that's substantial, that's you're going to be able to harness and grow. If you don't do it right. If you just throw data. If you buy Hadoop cluster or a Cloud platform and just throw your data out there and say, "We have a lake now." yeah, you're going to create a mess. And I think taking the time to really understand, you know, the new paradigm of data architecture and modern data engineering, and actually doing it in a very disciplined way. If you think about it, what we're doing is we're building laboratories. And if you have a shabby, poorly built laboratory, the best scientist in the world isn't going to be able to prove his theories. So if you have a well built laboratory and a clean room, then, you know a scientist can get what he needs done very, very, very efficiently. And that's the goal, I think, of data management today. >> I'd like to just quickly add that I totally agree with the challenge between on premise and Cloud mode. And I think one of the strong themes of today is going to be the hybrid data management challenge. And I think organizations, some organizations, have rushed to adopt Cloud. And thinking it's a really good place to dump the data and someone else has to manage the problem. And then they've ended up with a very expensive death by 1,000 cuts in some senses. And then others have been very reluctant as a result of not gotten access to rapid moving and disruptive technology. So I think there's a really big challenge to get a basic conversation going around what's the value using Cloud technology as in adopting it, versus what are the risks? And when's the right time to move? For example, should we Cloud Burst for workloads? Do we move whole data sets in there? You know, moving half a petabyte of data into a Cloud platform back is a non-trivial exercise. But moving a terabyte isn't actually that big a deal anymore. So, you know, should we keep stuff behind the firewalls? I'd be interested in seeing this week where 80% of the data, supposedly is. And just push out for Cloud tools, machine learning, data science tools, whatever they might be, cognitive analytics, et cetera. And keep the bulk of the data on premise. Or should we just move whole spools into the Cloud? There is no one size fits all. There's no silver bullet. Every organization has it's own quirks and own nuances they need to think through and make a decision themselves. >> Very often, Dez, organizations have zonal architectures so you'll have a data lake that consists of a no sequel platform that might be used for say, mobile applications. A Hadoop platform that might be used for unstructured data refinement, so forth. A streaming platform, so forth and so on. And then you'll have machine learning models that are built and optimized for those different platforms. So, you know, think of it in terms of then, your data lake, is a set of zones that-- >> It gets even more complex just playing on that theme, when you think about what Cisco started, called Folk Computing. I don't really like that term. But edge analytics, or computing at the edge. We've seen with the internet coming along where we couldn't deliver everything with a central data center. So we started creating this concept of content delivery networks, right? I think the same thing, I know the same thing has happened in data analysis and data processing. Where we've been pulling social media out of the Cloud, per se, and bringing it back to a central source. And doing analytics on it. But when you think of something like, say for example, when the Dreamliner 787 from Boeing came out, this airplane created 1/2 a terabyte of data per flight. Now let's just do some quick, back of the envelope math. There's 87,400 fights a day, just in the domestic airspace in the USA alone, per day. Now 87,400 by 1/2 a terabyte, that's 43 point five petabytes a day. You physically can't copy that from quote unquote in the Cloud, if you'll pardon the pun, back to the data center. So now we've got the challenge, a lot of our Enterprise data's behind a firewall, supposedly 80% of it. But what's out at the edge of the network. Where's the value in that data? So there are zonal challenges. Now what do I do with my Enterprise versus the open data, the mobile data, the machine data. >> Yeah, we've seen some recent data from IDC that says, "About 43% of the data "is going to stay at the edge." We think that, that's way understated, just given the examples. We think it's closer to 90% is going to stay at the edge. >> Just on the airplane topic, right? So Airbus wasn't going to be outdone. Boeing put 4,000 sensors or something in their 787 Dreamliner six years ago. Airbus just announced an 83, 81,000 with 10,000 sensors in it. Do the same math. Now the FAA in the US said that all aircraft and all carriers have to be, by early next year, I think it's like March or April next year, have to be at the same level of BIOS. Or the same capability of data collection and so forth. It's kind of like a mini GDPR for airlines. So with the 83, 81,000 with 10,000 sensors, that becomes two point five terabytes per flight. If you do the math, it's 220 petabytes of data just in one day's traffic, domestically in the US. Now, it's just so mind boggling that we're going to have to completely turn our thinking on its' head, on what do we do behind the firewall? What do we do in the Cloud versus what we might have to do in the airplane? I mean, think about edge analytics in the airplane processing data, as you said, Jim, streaming analytics in flight. >> Yeah that's a big topic within Wikibon, so, within the team. Me and David Floyer, and my other colleagues. They're talking about the whole notion of edge architecture. Not only will most of the data be persisted at the edge, most of the deep learning models like TensorFlow will be executed at the edge. To some degree, the training of those models will happen in the Cloud. But much of that will be pushed in a federated fashion to the edge, or at least I'm predicting. We're already seeing some industry moves in that direction, in terms of architectures. Google has a federated training, project or initiative. >> Chris: Look at TensorFlow Lite. >> Which is really fascinating for it's geared to IOT, I'm sorry, go ahead. >> Look at TensorFlow Lite. I mean in the announcement of having every Android device having ML capabilities, is Google's essential acknowledgment, "We can't do it all." So we need to essentially, sort of like a setting at home. Everyone's smartphone top TV box just to help with the processing. >> Now we're talking about this, this sort of leads to this IOT discussion but I want to underscore the operating model. As you were saying, "You can't just "lift and shift to the Cloud." You're not going to, CEOs aren't going to get the billion dollar hit by just doing that. So you got to change the operating model. And that leads to, this discussion of IOT. And an entirely new operating model. >> Well, there are companies that are like Sisense who have worked with Intel. And they've taken this concept. They've taken the business logic and not just putting it in the chip, but actually putting it in memory, in the chip. So as data's going through the chip it's not just actually being processed but it's actually being baked in memory. So level one, two, and three cache. Now this is a game changer. Because as Chris was saying, even if we were to get the data back to a central location, the compute load, I saw a real interesting thing from I think it was Google the other day, one of the guys was doing a talk. And he spoke about what it meant to add cognitive and voice processing into just the Android platform. And they used some number, like that had, double the amount of compute they had, just to add voice for free, to the Android platform. Now even for Google, that's a nontrivial exercise. So as Chris was saying, I think we have to again, flip it on its' head and say, "How much can we put "at the edge of the network?" Because think about these phones. I mean, even your fridge and microwave, right? We put a man on the moon with something that these days, we make for $89 at home, on the Raspberry Pie computer, right? And even that was 1,000 times more powerful. When we start looking at what's going into the chips, we've seen people build new, not even GPUs, but deep learning and stream analytics capable chips. Like Google, for example. That's going to make its' way into consumer products. So that, now the compute capacity in phones, is going to, I think transmogrify in some ways because there is some magic in there. To the point where, as Chris was saying, "We're going to have the smarts in our phone." And a lot of that workload is going to move closer to us. And only the metadata that we need to move is going to go centrally. >> Well here's the thing. The edge isn't the technology. The edge is actually the people. When you look at, for example, the MIT language Scratch. This is kids programming language. It's drag and drop. You know, kids can assemble really fun animations and make little movies. We're training them to build for IOT. Because if you look at a system like Node-RED, it's an IBM interface that is drag and drop. Your workflow is for IOT. And you can push that to a device. Scratch has a converter for doing those. So the edge is what those thousands and millions of kids who are learning how to code, learning how to think architecturally and algorithmically. What they're going to create that is beyond what any of us can possibly imagine. >> I'd like to add one other thing, as well. I think there's a topic we've got to start tabling. And that is what I refer to as the gravity of data. So when you think about how planets are formed, right? Particles of dust accrete. They form into planets. Planets develop gravity. And the reason we're not flying into space right now is that there's gravitational force. Even though it's one of the weakest forces, it keeps us on our feet. Oftentimes in organizations, I ask them to start thinking about, "Where is the center "of your universe with regard to the gravity of data." Because if you can follow the center of your universe and the gravity of your data, you can often, as Chris is saying, find where the business logic needs to be. And it could be that you got to think about a storage problem. You can think about a compute problem. You can think about a streaming analytics problem. But if you can find where the center of your universe and the center of your gravity for your data is, often you can get a really good insight into where you can start focusing on where the workloads are going to be where the smarts are going to be. Whether it's small, medium, or large. >> So this brings up the topic of data governance. One of the themes here at Fast Track Your Data is GDPR. What it means. It's one of the reasons, I think IBM selected Europe, generally, Munich specifically. So let's talk about GDPR. We had a really interesting discussion last night. So let's kind of recreate some of that. I'd like somebody in the panel to start with, what is GDPR? And why does it matter, Ronald? >> Yeah, maybe I can start. Maybe a little bit more in general unified governance. So if i talk to companies and I need to explain to them what's governance, I basically compare it with a crime scene. So in a crime scene if something happens, they start with securing all the evidence. So they start sealing the environment. And take care that all the evidence is collected. And on the other hand, you see that they need to protect this evidence. There are all kinds of policies. There are all kinds of procedures. There are all kinds of rules, that need to be followed. To take care that the whole evidence is secured well. And once you start, basically, investigating. So you have the crime scene investigators. You have the research lab. You have all different kind of people. They need to have consent before they can use all this evidence. And the whole reason why they're doing this is in order to collect the villain, the crook. To catch him and on the other hand, once he's there, to convict him. And we do this to have trust in the materials. Or trust in basically, the analytics. And on the other hand to, the public have trust in everything what's happened with the data. So if you look to a company, where data is basically the evidence, this is the value of your data. It's similar to like the evidence within a crime scene. But most companies don't treat it like this. So if we then look to GDPR, GDPR basically shifts the power and the ownership of the data from the company to the person that created it. Which is often, let's say the consumer. And there's a lot of paradox in this. Because all the companies say, "We need to have this customer data. "Because we need to improve the customer experience." So if you make it concrete and let's say it's 1st of June, so GDPR is active. And it's first of June 2018. And I go to iTunes, so I use iTunes. Let's go to iTunes said, "Okay, Apple please "give me access to my data." I want to see which kind of personal information you have stored for me. On the other end, I want to have the right to rectify all this data. I want to be able to change it and give them a different level of how they can use my data. So I ask this to iTunes. And then I say to them, okay, "I basically don't like you anymore. "I want to go to Spotify. "So please transfer all my personal data to Spotify." So that's possible once it's June 18. Then I go back to iTunes and say, "Okay, I don't like it anymore. "Please reduce my consent. "I withdraw my consent. "And I want you to remove all my "personal data for everything that you use." And I go to Spotify and I give them, let's say, consent for using my data. So this is a shift where you can, as a person be the owner of the data. And this has a lot of consequences, of course, for organizations, how to manage this. So it's quite simple for the consumer. They get the power, it's maturing the whole law system. But it's a big consequence of course for organizations. >> This is going to be a nightmare for marketers. But fill in some of the gaps there. >> Let's go back, so GDPR, the General Data Protection Regulation, was passed by the EU in 2016, in May of 2016. It is, as Ronald was saying, it's four basic things. The right to privacy. The right to be forgotten. Privacy built into systems by default. And the right to data transfer. >> Joe: It takes effect next year. >> It is already in effect. GDPR took effect in May of 2016. The enforcement penalties take place the 25th of May 2018. Now here's where, there's two things on the penalty side that are important for everyone to know. Now number one, GDPR is extra territorial. Which means that an EU citizen, anywhere on the planet has GDPR, goes with them. So say you're a pizza shop in Nebraska. And an EU citizen walks in, orders a pizza. Gives her the credit card and stuff like that. If you for some reason, store that data, GDPR now applies to you, Mr. Pizza shop, whether or not you do business in the EU. Because an EU citizen's data is with you. Two, the penalties are much stiffer then they ever have been. In the old days companies could simply write off penalties as saying, "That's the cost of doing business." With GDPR the penalties are up to 4% of your annual revenue or 20 million Euros, whichever is greater. And there may be criminal sanctions, charges, against key company executives. So there's a lot of questions about how this is going to be implemented. But one of the first impacts you'll see from a marketing perspective is all the advertising we do, targeting people by their age, by their personally identifiable information, by their demographics. Between now and May 25th 2018, a good chunk of that may have to go away because there's no way for you to say, "Well this person's an EU citizen, this person's not." People give false information all the time online. So how do you differentiate it? Every company, regardless of whether they're in the EU or not will have to adapt to it, or deal with the penalties. >> So Lillian, as a consumer this is designed to protect you. But you had a very negative perception of this regulation. >> I've looked over the GDPR and to me it actually looks like a socialist agenda. It looks like (panel laughs) no, it looks like a full assault on free enterprise and capitalism. And on its' face from a legal perspective, its' completely and wholly unenforceable. Because they're assigning jurisdictional rights to the citizen. But what are they going to do? They're going to go to Nebraska and they're going to call in the guy from the pizza shop? And call him into what court? The EU court? It's unenforceable from a legal perspective. And if you write a law that's unenforceable, you know, it's got to be enforceable in every element. It can't be just, "Oh, we're only "going to enforce it for Facebook and for Google. "But it's not enforceable for," it needs to be written so that it's a complete and actionable law. And it's not written in that way. And from a technological perspective it's not implementable. I think you said something like 652 EU regulators or political people voted for this and 10 voted against it. But what do they know about actually implementing it? Is it possible? There's all sorts of regulations out there that aren't possible to implement. I come from an environmental engineering background. And it's absolutely ridiculous because these agencies will pass laws that actually, it's not possible to implement those in practice. The cost would be too great. And it's not even needed. So I don't know, I just saw this and I thought, "You know, if the EU wants to," what they're essentially trying to do is regulate what the rest of the world does on the internet. And if they want to build their own internet like China has and police it the way that they want to. But Ronald here, made an analogy between data, and free enterprise, and a crime scene. Now to me, that's absolutely ridiculous. What does data and someone signing up for an email list have to do with a crime scene? And if EU wants to make it that way they can police their own internet. But they can't go across the world. They can't go to Singapore and tell Singapore, or go to the pizza shop in Nebraska and tell them how to run their business. >> You know, EU overreach in the post Brexit era, of what you're saying has a lot of validity. How far can the tentacles of the EU reach into other sovereign nations. >> What court are they going to call them into? >> Yeah. >> I'd like to weigh in on this. There are lots of unknowns, right? So I'd like us to focus on the things we do know. We've already dealt with similar situations before. In Australia, we introduced a goods and sales tax. Completely foreign concept. Everything you bought had 10% on it. No one knew how to deal with this. It was a completely new practice in accounting. There's a whole bunch of new software that had to be written. MYRB had to have new capability, but we coped. No one actually went to jail yet. It's decades later, for not complying with GST. So what it was, was a framework on how to shift from non sales tax related revenue collection. To sales tax related revenue collection. I agree that there are some egregious things built into this. I don't disagree with that at all. But I think if I put my slightly broader view of the world hat on, we have well and truly gone past the point in my mind, where data was respected, data was treated in a sensible way. I mean I get emails from companies I've never done business with. And when I follow it up, it's because I did business with a credit card company, that gave it to a service provider, that thought that I was going to, when I bought a holiday to come to Europe, that I might want travel insurance. Now some might say there's value in that. And other's say there's not, there's the debate. But let's just focus on what we're talking about. We're talking about a framework for governance of the treatment of data. If we remove all the emotive component, what we are talking about is a series of guidelines, backed by laws, that say, "We would like you to do this," in an ideal world. But I don't think anyone's going to go to jail, on day one. They may go to jail on day 180. If they continue to do nothing about it. So they're asking you to sort of sit up and pay attention. Do something about it. There's a whole bunch of relief around how you approach it. The big thing for me, is there's no get out of jail card, right? There is no get out of jail card for not complying. But there's plenty of support. I mean, we're going to have ambulance chasers everywhere. We're going to have class actions. We're going to have individual suits. The greatest thing to do right now is get into GDPR law. Because you seem to think data scientists are unicorn? >> What kind of life is that if there's ambulance chasers everywhere? You want to live like that? >> Well I think we've seen ad blocking. I use ad blocking as an example, right? A lot of organizations with advertising broke the internet by just throwing too much content on pages, to the point where they're just unusable. And so we had this response with ad blocking. I think in many ways, GDPR is a regional response to a situation where I don't think it's the exact right answer. But it's the next evolutional step. We'll see things evolve over time. >> It's funny you mentioned it because in the United States one of the things that has happened, is that with the change in political administrations, the regulations on what companies can do with your data have actually been laxened, to the point where, for example, your internet service provider can resell your browsing history, with or without your consent. Or your consent's probably buried in there, on page 47. And so, GDPR is kind of a response to saying, "You know what? "You guys over there across the Atlantic "are kind of doing some fairly "irresponsible things with what you allow companies to do." Now, to Lillian's point, no one's probably going to go after the pizza shop in Nebraska because they don't do business in the EU. They don't have an EU presence. And it's unlikely that an EU regulator's going to get on a plane from Brussels and fly to Topeka and say, or Omaha, sorry, "Come on Joe, let's get the pizza shop in order here." But for companies, particularly Cloud companies, that have offices and operations within the EU, they have to sit up and pay attention. So if you have any kind of EU operations, or any kind of fiscal presence in the EU, you need to get on board. >> But to Lillian's point it becomes a boondoggle for lawyers in the EU who want to go after deep pocketed companies like Facebook and Google. >> What's the value in that? It seems like regulators are just trying to create work for themselves. >> What about the things that say advertisers can do, not so much with the data that they have? With the data that they don't have. In other words, they have people called data scientists who build models that can do inferences on sparse data. And do amazing things in terms of personalization. What do you do about all those gray areas? Where you got machine learning models and so forth? >> But it applies-- >> It applies to personally identifiable information. But if you have a talented enough data scientist, you don't need the PII or even the inferred characteristics. If a certain type of behavior happens on your website, for example. And this path of 17 pages almost always leads to a conversion, it doesn't matter who you are or where you're coming from. If you're a good enough data scientist, you can build a model that will track that. >> Like you know, target, infer some young woman was pregnant. And they inferred correctly even though that was never divulged. I mean, there's all those gray areas that, how can you stop that slippery slope? >> Well I'm going to weigh in really quickly. A really interesting experiment for people to do. When people get very emotional about it I say to them, "Go to Google.com, "view source, put it in seven point Courier "font in Word and count how many pages it is." I guess you can't guess how many pages? It's 52 pages of seven point Courier font, HTML to render one logo, and a search field, and a click button. Now why do we need 52 pages of HTML source code and Java script just to take a search query. Think about what's being done in that. It's effectively a mini operating system, to figure out who you are, and what you're doing, and where you been. Now is that a good or bad thing? I don't know, I'm not going to make a judgment call. But what I'm saying is we need to stop and take a deep breath and say, "Does anybody need a 52 page, "home page to take a search query?" Because that's just the tip of the iceberg. >> To that point, I like the results that Google gives me. That's why I use Google and not Bing. Because I get better search results. So, yeah, I don't mind if you mine my personal data and give me, our Facebook ads, those are the only ads, I saw in your article that GDPR is going to take out targeted advertising. The only ads in the entire world, that I like are Facebook ads. Because I actually see products I'm interested in. And I'm happy to learn about that. I think, "Oh I want to research that. "I want to see this new line of products "and what are their competitors?" And I like the targeted advertising. I like the targeted search results because it's giving me more of the information that I'm actually interested in. >> And that's exactly what it's about. You can still decide, yourself, if you want to have this targeted advertising. If not, then you don't give consent. If you like it, you give consent. So if a company gives you value, you give consent back. So it's not that it's restricting everything. It's giving consent. And I think it's similar to what happened and the same type of response, what happened, we had the Mad Cow Disease here in Europe, where you had the whole food chain that needed to be tracked. And everybody said, "No, it's not required." But now it's implemented. Everybody in Europe does it. So it's the same, what probably going to happen over here as well. >> So what does GDPR mean for data scientists? >> I think GDPR is, I think it is needed. I think one of the things that may be slowing data science down is fear. People are afraid to share their data. Because they don't know what's going to be done with it. If there are some guidelines around it that should be enforced and I think, you know, I think it's been said but as long as a company could prove that it's doing due diligence to protect your data, I think no one is going to go to jail. I think when there's, you know, we reference a crime scene, if there's a heinous crime being committed, all right, then it's going to become obvious. And then you do go directly to jail. But I think having guidelines and even laws around privacy and protection of data is not necessarily a bad thing. You can do a lot of data, really meaningful data science, without understanding that it's Joe Caserta. All of the demographics about me. All of the characteristics about me as a human being, I think are still on the table. All that they're saying is that you can't go after Joe, himself, directly. And I think that's okay. You know, there's still a lot of things. We could still cure diseases without knowing that I'm Joe Caserta, right? As long as you know everything else about me. And I think that's really at the core, that's what we're trying to do. We're trying to protect the individual and the individual's data about themselves. But I think as far as how it affects data science, you know, a lot of our clients, they're afraid to implement things because they don't exactly understand what the guideline is. And they don't want to go to jail. So they wind up doing nothing. So now that we have something in writing that, at least, it's something that we can work towards, I think is a good thing. >> In many ways, organizations are suffering from the deer in the headlight problem. They don't understand it. And so they just end up frozen in the headlights. But I just want to go back one step if I could. We could get really excited about what it is and is not. But for me, the most critical thing there is to remember though, data breaches are happening. There are over 1,400 data breaches, on average, per day. And most of them are not trivial. And when we saw 1/2 a billion from Yahoo. And then one point one billion and then one point five billion. I mean, think about what that actually means. There were 47,500 Mongodbs breached in an 18 hour window, after an automated upgrade. And they were airlines, they were banks, they were police stations. They were hospitals. So when I think about frameworks like GDPR, I'm less worried about whether I'm going to see ads and be sold stuff. I'm more worried about, and I'll give you one example. My 12 year old son has an account at a platform called Edmodo. Now I'm not going to pick on that brand for any reason but it's a current issue. Something like, I think it was like 19 million children in the world had their username, password, email address, home address, and all this social interaction on this Facebook for kids platform called Edmodo, breached in one night. Now I got my hands on a copy. And everything about my son is there. Now I have a major issue with that. Because I can't do anything to undo that, nothing. The fact that I was able to get a copy, within hours on a dark website, for free. The fact that his first name, last name, email, mobile phone number, all these personal messages from friends. Nobody has the right to allow that to breach on my son. Or your children, or our children. For me, GDPR, is a framework for us to try and behave better about really big issues. Whether it's a socialist issue. Whether someone's got an issue with advertising. I'm actually not interested in that at all. What I'm interested in is companies need to behave much better about the treatment of data when it's the type of data that's being breached. And I get really emotional when it's my son, or someone else's child. Because I don't care if my bank account gets hacked. Because they hedge that. They underwrite and insure themselves and the money arrives back to my bank. But when it's my wife who donated blood and a blood donor website got breached and her details got lost. Even things like sexual preferences. That they ask questions on, is out there. My 12 year old son is out there. Nobody has the right to allow that to happen. For me, GDPR is the framework for us to focus on that. >> Dave: Lillian, is there a comment you have? >> Yeah, I think that, I think that security concerns are 100% and definitely a serious issue. Security needs to be addressed. And I think a lot of the stuff that's happening is due to, I think we need better security personnel. I think we need better people working in the security area where they're actually looking and securing. Because I don't think you can regulate I was just, I wanted to take the microphone back when you were talking about taking someone to jail. Okay, I have a background in law. And if you look at this, you guys are calling it a framework. But it's not a framework. What they're trying to do is take 4% of your business revenues per infraction. They want to say, "If a person signs up "on your email list and you didn't "like, necessarily give whatever "disclaimer that the EU said you need to give. "Per infraction, we're going to take "4% of your business revenue." That's a law, that they're trying to put into place. And you guys are talking about taking people to jail. What jail are you? EU is not a country. What jurisdiction do they have? Like, you're going to take pizza man Joe and put him in the EU jail? Is there an EU jail? Are you going to take them to a UN jail? I mean, it's just on its' face it doesn't hold up to legal tests. I don't understand how they could enforce this. >> I'd like to just answer the question on-- >> Security is a serious issue. I would be extremely upset if I were you. >> I personally know, people who work for companies who've had data breaches. And I respect them all. They're really smart people. They've got 25 plus years in security. And they are shocked that they've allowed a breach to take place. What they've invariably all agreed on is that a whole range of drivers have caused them to get to a bad practice. So then, for example, the donate blood website. The young person who was assist admin with all the right skills and all the right experience just made a basic mistake. They took a db dump of a mysql database before they upgraded their Wordpress website for the business. And they happened to leave it in a folder that was indexable by Google. And so somebody wrote a radio expression to search in Google to find sql backups. Now this person, I personally respect them. I think they're an amazing practitioner. They just made a mistake. So what does that bring us back to? It brings us back to the point that we need a safety net or a framework or whatever you want to call it. Where organizations have checks and balances no matter what they do. Whether it's an upgrade, a backup, a modification, you know. And they all think they do, but invariably we've seen from the hundreds of thousands of breaches, they don't. Now on the point of law, we could debate that all day. I mean the EU does have a remit. If I was caught speeding in Germany, as an Australian, I would be thrown into a German jail. If I got caught as an organization in France, breaching GDPR, I would be held accountable to the law in that region, by the organization pursuing me. So I think it's a bit of a misnomer saying I can't go to an EU jail. I don't disagree with you, totally, but I think it's regional. If I get a speeding fine and break the law of driving fast in EU, it's in the country, in the region, that I'm caught. And I think GDPR's going to be enforced in that same approach. >> All right folks, unfortunately the 60 minutes flew right by. And it does when you have great guests like yourselves. So thank you very much for joining this panel today. And we have an action packed day here. So we're going to cut over. The CUBE is going to have its' interview format starting in about 1/2 hour. And then we cut over to the main tent. Who's on the main tent? Dez, you're doing a main stage presentation today. Data Science is a Team Sport. Hillary Mason, has a breakout session. We also have a breakout session on GDPR and what it means for you. Are you ready for GDPR? Check out ibmgo.com. It's all free content, it's all open. You do have to sign in to see the Hillary Mason and the GDPR sessions. And we'll be back in about 1/2 hour with the CUBE. We'll be running replays all day on SiliconAngle.tv and also ibmgo.com. So thanks for watching everybody. Keep it right there, we'll be back in about 1/2 hour with the CUBE interviews. We're live from Munich, Germany, at Fast Track Your Data. This is Dave Vellante with Jim Kobielus, we'll see you shortly. (electronic music)

>> Narrator: It's theCUBE covering Sapphire Now 2017 brought to you by SAP Cloud Platform and HANA Enterprise Cloud. >> Welcome back everyone to our special Sapphire Now 2017 coverage in our Palo Alto Studios. We have folks on the ground in Orlando. It's the third day of Sapphire Now and we're bringing our friends and experts inside our new 4500 square foot studio where we're starting to get our action going and covering events anywhere they are from here. If we can't get there we'll do it from here in Palo Alto. Our next guest is Satyen Sangani, CEO of Alation. A hot start-up funded by Custom Adventures, Catalyst Data Collective, and I think Andreessen Horowitz is also an investor? >> Satyen: That's right. >> Satyen, welcome to the cube conversation here. >> Thank you for having me. >> So we are doing this special coverage, and I wanted to bring you in and discuss Sapphire Now as it relates to the context of the biggest wave hitting the industry, with waves are ones cloud. We've known that for a while. People surfing that one, then the data wave is coming fast, and I think this is a completely different animal in the sense of it's going to look different, but be just as big. Your business is in the data business. You help companies figure this out. Give us the update on, first take a minute talk about Alation, for the folks who aren't following you, what do you guys do, and then let's talk about data. >> Yeah. So for those of you that don't know about what Alation is, it's basically a data catalog. You know, if you think about all of the databases that exist in the enterprise, stuff on Prem, stuff in the cloud, all the BI tools like Tableau and MicroStrategy, and Business Objects. When you've got a lot of data that sits inside the enterprise today and a wide variety of legacy and modern tools, and what Alation does is, it creates a catalog, crawling all of those systems like Google crawls the web and effectively looks at all the logs inside of those systems, to understand how the data is interrelated and we create this data social graph, and it kind of looks >> John: It's a metadata catalog? >> We call you know, we don't use the word metadata because metadata is the word that people use when you know that's that's Johnny back in the corner office, Right? And people don't want to talk about metadata if you're a business person you think about metadata you're like, I don't, not my thing. >> So you guys are democratizing what data means to an organization? That's right. >> We just like to talk about context. We basically say, look in the same way that information, or in the same way when you're eating your food, you need, you know organic labeling to understand whether or not that's good or bad, we have on some level a provenance problem, a trust problem inside of data in the enterprise, and you need a layer of you know trust, and understanding in context. >> So you guys are a SAS, or you guys are a SAS solution, or are you a software subscription? >> We are both. Most of this is actually on Prem because most of the people that have the problem that Alation solves are very big complicated institutions, or institutions with a lot of data, or a lot of people trying to analyze it, but we do also have a SAS offering, and actually that's how we intersect with SAP Altiscale, and so we have a cloud base that's offering that we work with. >> Tell me about your relation SAP because you kind of backdoored in through an acquisition, quickly note that we'll get into the conversation. >> Yeah that's right, So Altiscale to big intersections, big data, and then they do big data in the cloud SAP acquired them last year and what we do is we provide a front-end capability for people to access that data in the cloud, so that as analysts want to analyze that data, as data governance folks want to manage that data, we provide them with a single catalog to do that. >> So talk about the dynamics in the industry because SAP clearly the big news there is the Leonardo, they're trying to create this framework, we just announced an alpha because everyone's got these names of dead creative geniuses, (Satyen laughs) We just ingest our Nostradamus products, Since they have Leonardo and, >> That's right. >> SAP's got Einstein, and IBM's got Watson, and Informatica has got Claire, so who thought maybe we just get our own version, but anyway, everyone's got some sort of like bot, or like AI program. >> Yep. >> I mean I get that, but the reality is, the trend is, they're trying to create a tool chest of platform re-platforming around tooling >> Satyen: Yeah. >> To make things easier. >> Satyen: Yeah. >> You have a lot of work in this area, through relation, trying to make things easier. >> Satyen: Yeah. >> And also they get the cloud, On-premise, HANA Enterprise Cloud, SAV cloud platform, meaning developers. So the convergence between developers, cloud, and data are happening. What's your take on that strategy? You think SAP's got a good move by going multi cloud, or should they, should be taking a different approach? >> Well I think they have to, I mean I think the economics in cloud, and the unmanageability, you know really human economics, and being able to have more and more being managed by third-party providers that are, you know, effectively like AWS, and how they skill, in the capability to manage at scale, and you just really can't compete if you're SAP, and you can't compete if your customers are buying, and assembling the toolkits On-premise, so they've got to go there, and I think every IT provider has to >> John: Got to go to the cloud you mean? >> They've got to go to the cloud, I think there's no question about it, you know I think that's at this point, a foregone conclusion in the world of enterprise IT. >> John: Yeah it's pretty obvious, I mean hybrid cloud is happening, that's really a gateway to multi-cloud, the submission is when I build Norton, a guest in latency multi-cloud issues there, but the reality is not every workloads gone there yet, a lot of analytics going on in the cloud. >> Satyen: Yeah. >> DevTest, okay check the box on DevTest >> Satyen: That's right. >> Analytics is all a ballgame right now, in terms of state of the art, your thoughts on the trends in how companies are using the cloud for analytics, and things that are challenges and opportunities. >> Yeah, I think there's, I think the analytics story in the cloud is a little bit earlier. I think that the transaction processing and the new applications, and the new architectures, and new integrations, certainly if you're going to build a new project, you're going to do that in the cloud, but I think the analytics in a stack, first of all there's like data gravity, right, you know there's a lot of gravity to that data, and moving it all into the cloud, and so if you're transaction processing, your behavioral apps are in the cloud, then it makes sense to keep the data in an AWS, or in the cloud. Conversely you know if it's not, then you're not going to take a whole bunch of data that sits on Prem and move it whole hog all the way to the cloud just because, right, that's super expensive, >> Yeah. >> You've got legacy. >> A lot of risks too and a lot of governance and a lot of compliance stuff as well. >> That's exactly right I mean if you're trying to comply with Basel II or GDPR, and you know you want to manage all that privacy information. How are you going to do that if you're going to move your data at the same time >> John: Yeah. >> And so it's a tough >> John: Great point. >> It's a tough move, I think from our perspective, and I think this is really important, you know we sort of say look, in a world where data is going to be on Prem, on the cloud, you know in BI tools, in databases and no SQL databases, on Hadoop, you're going to have data everywhere, and in that world where data is going to be in multiple locations and multiple technologies you got to figure out a way to manage. >> Yeah. I mean data sprawls all over the place, it's a big problem, oh and this oh and by the way that's a good thing, store it to your storage is getting cheaper and cheaper, data legs are popping out, but you have data links, for all you have data everywhere. >> Satyen: That's right. >> How are you looking at that problem as a start-up, and how a customer's dealing with that, and what is this a real issue, or is this still too early to talk about data sprawl? >> It's a real issue, I mean it, we liken it to the advent of the Internet in the time of traditional media, right, so you had you had traditional media, there were single sort of authoritative sources we all watched it may be CNN may be CBS we had the nightly news we had Newsweek, we got our information, also the Internet comes along, and anybody can blog about anything, right and so the cost of creating information is now this much lower anybody can create any reality anybody can store data anywhere, right, and so now you've got a world where, with tableau, with Hadoop, with redshift, you can build any stack you want to at any cost, and so now what do you do? Because everybody's creating their own thing, every Dev is doing their own thing, everybody's got new databases, new applications, you know software is eating the world right? >> And data it is eating software. >> And data is eating software, and so now you've got this problem where you're like look I got all this stuff, and I don't know I don't know what's fake news, what's real, what's alternative fact, what doesn't make any sense, and so you've got a signal and noise problem, and I think in that world you got to figure out how to get to truth, right, >> John: Yeah. And what's the answer to that in your mind, not that you have the answer, if you did, we'd be solving it better. >> Yeah. >> But I mean directionally where's the vector going in your mind? I try to talk to Paul Martino about this at bullpen capital he's a total analytics geek he doesn't think this big data can solve that yet but they started to see some science around trying to solve these problems with data. What's your vision on this? >> Satyen: Yeah you know so I believe that every I think that every developer is going to start building applications based on data I think that every business person is going to have an analytical role in their job because if they're not dealing with the world on the certainty, and they're not using all the evidence, at their disposable, they're not making the best decisions and obviously they're going to be more and more analysts and so you know at some level everybody is an analyst >> I wrote a post in 2008, my old blog was hosted on WordPress, before I started SilicionANGLE, data is the new developer kid. >> That's right. >> And I saw that early, and it was still not as clear to this now as obvious as least to us because we're in the middle, in this industry, but it's now part of the software fabric, it's like a library, like as developer you'd call a library of code software to come in and be part of your program >> Yeah >> Building blocks approach, Lego blocks, but now data as Lego blocks completely changes the game on things if you think of it that way. Where are we on that notion of you really using data as a development component, I mean it seems to be early, I don't, haven't seen any proof points, that says, well that company's actually using the data programmatically with software. >> Satyen: Yeah. well I mean look I think there's features in almost every software application whether it's you know 27% of the people clicked on this button into this particular thing, I mean that's a data based application right and so I think there is this notion that we talked a lot about, which is data literacy, right, and so that's kind of a weird thing, so what does that exactly mean? Well data is just information like a news article is information, and you got to decide whether it's good or it's bad, and whether you can come to a conclusion, or whether you can't, just as if you're using an API from a third-party developer you need documentation, you need context about that data, and people have to be intelligent about how they use it. >> And literacies also makes it, makes it addressable. >> That's right. >> If you have knowledge about data, at some point it's named and addressed at some point in a network. >> Satyen: Yeah. >> Especially Jada in motion, I mean data legs I get, data at rest, we start getting into data in motion, real-time data, every piece of data counts. Right? >> That's exactly right. And so now you've got to teach people about how to use this stuff you've got to give them the right data you got to make that discoverable you got to make that information usable you've got to get people to know who the experts are about the data, so they can ask questions, you know these are tougher problems, especially as you get more and more systems. >> All right, as a start up, you're a growing start-up, you guys are, are lean and mean, doing well. You have to go compete in this war. It's a lot of, you know a lot of big whales in there, I mean you got Oracle, SAP, IBM, they're all trying to transform, everybody is transforming all the incumbent winners, potential buyers of your company, or potentially you displacing this, as a young CEO, they you know eat their lunch, you have to go compete in a big game. How are you guys looking at that compass, I see your focus so I know a little bit about your plan, but take us through the mindset of a start-up CEO, that has to go into this world, you guys have to be good, I mean this is a big wave, see it's a big wave. >> Yeah. Nobody buys from a start-up unless you get, and a start-up could be even a company, less than a 100-200 people, I mean nobody's buying from a company unless there's a 10x return to value relative to the next best option, and so in that world how do you build 10x value? Well one you've got to have great technology, and then that's the start point, but the other thing is you've got to have deep focus on your customers, right, and so I think from our perspective, we build focus by just saying, look nobody understands data in your company, and by and large you've got to make money by understanding this data, as you do the digital transformation stuff, a big part of that is differentiating and making better products and optimizing based upon understanding your data because that helps you and your business make better decisions, >> John: Yeah. >> And so what we're going to do is help you understand that data better and faster than any other company can do. >> You really got to pick your shots, but what you're saying, if I hear you saying is as a start-up you got to hit the beachhead segment you want to own. >> Satyen: That's right. >> And own it. >> Satyen: That's exactly. >> No other decision, just get it, and then maybe get to a bigger scope later, and sequence around, and grow it that way. >> Satyen: You can't solve 10 problems >> Can't be groping for a beachhead if you don't know what you want, you're never going to get it. >> That's right. You can't solve 10 problems unless you solve one, right, and so you know I think we're at a phase where we've proven that we can scalably solved one, we've got customers like, you know Pfizer and Intuit and Citrix and Tesco and Tesla and eBay and Munich Reinsurance and so these are all you know amazing brands that are traditionally difficult to sell into, but you know I think from our perspective it's really about focus and just helping customers that are making that digital analytical transformation. Do it faster, and do it by enabling their people. >> But a lot going on this week for events, we had Informatica world this week, we got V-mon. We had Google I/O. We had Sapphire. It's a variety of other events going on, but I want to ask you kind of a more of a entrepreneurial industry question, which is, if we're going through the so-called digital transformation, that means a new modern era an old one movie transformed, yet I go to every event, and everyone's number one at something, that's like I was just at Informatica, they're number one in six squadrons. Michael Dell we're number in four every character, Mark Hurr at the press meeting said they're number one in all categories, Ross Perot think quote about you could be number one depends on how you slice the market, seems to be in play, my point is I kind of get a little bit, you know weirded out by that, but that is okay, you know I guess theCUBE's number one in overall live videos produced at an enterprise event, you know I, so we're number one at something, but my point is. >> Satyen: You really are. >> My point is, in a new transformation, what is the new scoreboard going to look like because a lot of things that you're talking about is horizontally integrated, there's new use cases developing, a new environment is coming online, so if someone wanted to actually try to keep score of who number one is and who's winning, besides customer wins, because that's clearly the one that you can point to and say hey they're winning customers, customer growth is good, outside of customer growth, what do you think will be the key requirements to get some sort of metric on who's really doing well these are the others, I mean we're not yet there with >> Yeah it's a tough problem, I mean you know used to be the world was that nobody gets fired for choosing choosing IBM. >> John: Yeah. >> Right, and I think that that brand credibility worked in a world where you could be conservative right, in this world I think, that looking for those measures, it is going to be really tough, and I think on some level that quest for looking for what is number one, or who is the best is actually the sort of fool's errand, and if that's what you're looking for, if you're looking for, you know what's the best answer for me based upon social signal, you know it's kind of like you know I'm going to go do the what the popular kids do in high school, I mean that could lead to you know a path, but it doesn't lead to the one that's going to actually get you satisfaction, and so on some level I think that customers, like you are the best signal, you know, always, >> John: Yeah, I mean it's hard, it's a rhetorical question, we ask it because, you know, we're trying to see not mystical with the path of fact called the fashion, what's fashionable. >> Satyen: Yeah. >> That's different. I mean talk about like really a cure metro, in the old days market share is one, actually IDC used a track who had market shares, and they would say based upon the number of shipments products, this is the market share winner, right? yeah that's pretty clean, I mean that's fairly clean, so just what it would be now? Number of instances, I mean it's so hard to figure out anyway, I digress. >> No, I think that's right, I mean I think I think it's really tough, that I think customers stories that, sort of map to your case. >> Yeah. It all comes back down to customer wins, how many customers you have was the >> Yeah and how much value they are getting out of your stuff. >> Yeah. That 10x value, and I think that's the multiplier minimum, if not more and with clouds and the scale is happening, you agree? >> Satyen: Yeah. >> It's going to get better. Okay thanks for coming on theCUBE. We have Satyen Sangani. CEO, co-founder of Alation, great start-up. Follow them on Twitter, these guys got some really good focus, learning about your data, because once you understand the data hygiene, you start think about ethics, and all the cool stuff happening with data. Thanks so much for coming on CUBE. More coverage, but Sapphire after the short break. (techno music)