>> From the Silicon Angle Media Office in Boston Massachusetts, it's "The Cube." (groovy techno music) Now, here's your host, Dave Vellante. >> Hello, everyone. The rise of open source is really powering the digital economy. And in a world where every company is essentially under pressure to become a software firm, open source software really becomes the linchpin of digital services for both incumbents and, of course, digital natives. Here's the challenge, is when developers tap and apply open source, they're often bringing in hundreds, or even thousands of lines of code that reside in open sourced packages and libraries. And these code bases, they have dependencies, and essentially hidden traps. Now typically, security vulnerabilities in code, they're attacked after the software's developed. Or maybe thrown over the fence to the sec-ops team and SNYK is a company that set out to solve this problem within the application development life cycle, not after the fact as a built-on. Now, with us to talk about this mega-trend is Peter McKay, a friend of The Cube and CEO of SNYK. Peter, great to see you again. >> Good to see you, dude. >> So I got to start with the name. SNYK, what does it mean? >> SNYK, So Now You Know. You know, people it's sneakers sneak. And they tend to use the snick. So it's SNYK or snick. But it is SNYK and it stands for So Now You Know. Kind of a security, so now you know a lot more about your applications than you ever did before. So it's kind of a fitting name. >> So you heard my narrative upfront. Maybe you can add a little color to that and provide some additional background. >> Yeah, I mean, it's a, you know, when you think of the larger trends that are going on in the market, you know, every company is going through this digital transformation. You know, and every CEO, it's the number one priority. We've got to change our business from, you know, financial services, healthcare, insurance company, whatever, are all switching to digital, you know, more of a software company. And with that, more software equals more software risk and cybersecurity continues to be, you know, a major. I think 72% of CEOs worry about cybersecurity as a top issue in protecting companies' data. And so for us, we've been in the software in the security space for the four and a half years. I've been in the security space since, you know, Watchfire 20 years ago. And right now, with more and more, as you said, open source and containers, the challenge of being able to address the cybersecurity issues that have never been more challenging. And so especially when you add the gap between the need for security professionals and what they have. I think it's four million open positions for security people. So you know, with all this added risk, more and more open source, more and more digitization, it's created this opportunity in the market where you're traditional approaches to addressing security don't work today, you know? Like you said, throwing it over the fence and having someone in security, you know, check and make sure and finding all these vulnerabilities, and throw it back to developers to fix is very slow and something at this point is not driving to success. >> So talk a little bit more about what attracted you to SNYK early. I mean, you've been with the company, you're at least involved in the company for a couple years now. What were the trends that you saw, and what was it about SNYK that, you know, led you to become an investor and ultimately, CEO? >> Yeah, so four years involved in the business. So you know, I've always loved the security space. I've been in it for a number, almost 20 years. So I enjoy the space. You know, I've watched it. The founder, Guy Podjarny, one of the founders of SNYK, has been a friend of mine for 16 years from back in the Watchfire days. So we've always stayed connected. I've always worked well together with him. And so when you started, and I was on the board, the first board member of the company, so I could see what was going on, and it was this, you know, changing, kind of the right place at the right time in terms of developer first security. Really taking all the things that are going on in the security space that impacts a developer or can be addressed by the developer, and embedding it into the software into that developer community, in a way that developers use, the tools that they use. So it's a developer-first mindset with security expertise built-in. And so when you look at the market, the number of open source container evolution, you know, it's a huge market opportunity. Then you look at the business momentum, just took off over the past, you know, four years. That it was something that I was getting more and more involved in. And then when Guy asked me to join as the CEO, it was like, "Sure, what took you so long?" (Dave laughing) >> We had Guy on at Node JS Summit. I want to say it was a couple years ago now. And what he was describing is when you package, take the example of Node. When you package code in Node, you bring in all these dependencies, kind of what I was talking about there, but the challenge that he sort of described was really making it seamless as part of the development workflow. It seems like that's unique to SNYK. Maybe you could talk about-- >> Yeah, it is. And you know, we've built it from the ground up. You know, it's very difficult. If it was a security tool for security people, and then say, "Oh, let's adapt it for the developer," that is almost impossible. Why I think we've been so successful from the 400,000 developers in the community using Freemium to paid, was we built it from the ground up for developer, embedded into the application-development life cycle. Into their process, the look and feel, easy for them to use, easy for them to try it, and then we focused on just developer adoption. A great experience, developers will continue to use it and expand with it. And most of our opportunities that we've been successful at, the customers, we have over 400 customers. That had been this try, you know, start it with the community. They used the Freemium, they tried it for their new application, then they tried it for all their new, and then they go back and replace the old. So it was kind of this Freemium, land and expand has been a great way for developers to try it, use it. Does it work, yes, buy more. And that's the way we work. >> We're really happy, Peter, that you came on because you've got some news today that you're choosing to share with us in our Cube community. So it's around financing, bring us up to date. What's the news? >> Yeah so you know, I'd say four months ago, five months ago, we raised a $70 million round from great investors. And that was really led by one of our existing investors, who kind of knew us the best and it was you know, Excel Venture, and then Excel Growth came in and led the $70 million round. And part of that was a few new investors that came in and Stripes, which is you know a very large growth equity investor were part of that $70 million round said you know, preempted it and said, "Look it, we know you don't need the money, but we want to," you know, "We want to preempt. We believe your customer momentum," here we did, you know, five or six really large deals. You know, one, 700, seven million, 7.4 million, one's 3.5 million. So we started getting these bigger deals and we doubled since the $70 million round. And so we said, "Okay, we want to make money not the issue." So they led the next round, which is $150 million round, at a valuation of over a billion. That really allows us now to, with the number of other really top tier, (mumbles) and Tiger and Trend and others, who have been part of watching the space and understand the market. And are really helping us grow this business internationally. So it's an exciting time. So you know, again, we weren't looking to raise. This was something that kind of came to us and you know, when people are that excited about it like we are and they know us the best because they've been part of our board of directors since their round, it allows us to do the things that we want to do faster. >> So $150 million raise this round, brings you up to the 250, is that correct? >> Yes, 250. >> And obviously, an up-round. So congratulations, that's great. >> Yeah, you know, I think a big part of that is you know, we're not, I mean, we've always been very fiscally responsible. I mean, yes we have the money and most of it's still in the bank. We're growing at the pace that we think is right for us and right for the market. You know, we continue to invest product, product, product, is making sure we continue our product-led organization. You know, from that bottoms up, which is something we continue to do. This allows us to accelerate that more aggressively, but also the community, which is a big part of what makes that, you know, when you have a bottoms up, you need to have that community. And we've grown that and we're going to continue to invest aggressively and build in that community. And lastly, go to market. Not only invest, invest aggressively in the North America, but also Europe and APJ, which, you know, a lot of the things we've learned from my Veeam experience, you know how to grow fast, go big or go home. You know, are things that we're going to do but we're going to do it in the right way. >> So the Golden Rule is product and sales, right? >> Yes, you're either building it or selling it. >> Right, that's kind of where you're going to put your money. You know, you talk a lot about people, companies will do IPOs to get seen, but companies today, I mean, even software companies, which is a capital-efficient industry, they raise a lot of dough and they put it towards promotion to compete. What are your thoughts on that? >> You know, we've had, the model is very straightforward. It's bottoms up, you know? Developers, you know, there's 28 million developers in the world, you know? What we want is every one of those 28 million to be using our product. Whether it's free or paid, I want SNYK used in every application-development life cycle. If you're one developer, or you're a sales force with standardized on 12,000 developers, we want them using SNYK. So for us, it's get it in the hands. And that, you know, it's not like-- developers aren't going to look at Super Bowl ads, they're not going to be looking. It's you know, it's finding the ways, like the conference. We bought the DevSecCon, you know, the conference for developer security. Another way to promote kind of our, you know, security for developers and grow that developer community. That's not to say that there isn't a security part. Because, you know, what we do is help security organizations with visibility and finding a much more scalable way that gets them out of the, you know, the slows-down, the speed bump to the moving apps more aggressively into production. And so this is very much about helping security people. A lot of times the budgets do come from security or dev-ops. But it's because of our focus on the developer and the success of fixing, finding, fixing, and auto-remediating that developer environment is what makes us special. >> And it's sounds like a key to your success is you're not asking developer to context switch into a new environment, right? It's part of their existing workflow. >> It has to be, right? Don't change how they do their job, right? I mean, their job is to develop incredible applications that are better than the competitors, get them to market faster than they can, than they've ever been able to do before and faster than the competitor, but do it securely. Our goal is to do the third, but not sacrifice on one and two, right? Help you drive it, help you get your applications to market, help you beat your competition, but do it in a secure fashion. So don't slow them down. >> Well, the other thing I like about you guys is the emphasis is on fixing. It's not just alerting people that there's a problem. I mean, for instance, a company like Red Hat, is that they're going to put a lot of fixes in. But you, of course, have to go implement them. What you're doing is saying, "Hey, we're going to do that for you. Push the button and then we'll do it," right? So that, to me, that's important because it enables automation, it enables scale. >> Exactly, and I think this has been one of the challenges for kind of more of the traditional legacy, is they find a whole bunch of vulnerabilities, right? And we feel as though just that alone, we're the best in the world at. Finding vulnerabilities in applications in open source container. And so the other part of it is, okay, you find all them, but prioritizing what it is that I should fix first? And that's become really big issue because the vulnerabilities, as you can imagine, continue to grow. But focusing on hey, fix this top 10%, then the next, and to the extent you can, auto-fix. Auto-remediate those problems, that's ultimately, we're measured by how many vulnerabilities do we fix, right? I mean, finding them, that's one thing. But fixing them is how we judge a successful customer. And now it's possible. Before, it was like, "Oh, okay, you're just going to show me more things." No, when you talk about Google and Salesforce and Intuit, and all of our customers, they're actually getting far better. They're seeing what they have in terms of their exposure, and they're fixing the problems. And that's ultimately what we're focused on. >> So some of those big whales that you just mentioned, it seems to me that the value proposition for those guys, Peter, is the quality of the code that they can develop and obviously, the time that it takes to do that. But if you think about it more of a traditional enterprise, which I'm sure is part of your (mumbles), they'll tell you, the (mumbles) will tell you our biggest problem is we don't have enough people with the skills. Does this help? >> It absolutely-- >> And how so? >> Yeah, I mean, there's a massive gap in security expertise. And the current approach, the tools, are, you know, like you said at the very beginning, it's I'm doing too late in the process. I need to do it upstream. So you've got to leverage the 28 million developers that are developing the applications. It's the only way to solve the problem of, you know, this application security challenge. We call it Cloud Dative Application Security, which all these applications usually are new apps that they're moving into the Cloud. And so to really fix it, to solve the problem, you got to embed it, make it really easy for developers to leverage SNYK in their whole, we call it, you know, it's that concept of shift left, you know? Our view is that it needs to be embedded within the development process. And that's how you fix the problem. >> And talk about the business model again. You said it's Freemium model, you just talked about a big seven figure deals that you're doing and that starts with a Freemium, and then what? I upgrade to a subscription and then it's a land and expand? Describe that. >> Yeah we call it, it's you know, it's the community. Let's get every developer in a community. 28 million, we want to get into our community. From there, you know, leverage our Freemium, use it. You know, we encourage you to use it. Everybody to use our Freemium. And it's full functionality. It's not restricted in anyway. You can use it. And there's a subset of those that are ready to say, "Look it, I want to use the paid version," which allows me to get more visibility across more developers. So as you get larger organization, you want to leverage the power of kind of a bigger, managing multiple developers, like a lot of, in different teams. And so that kind of gets that shift to that paid. Then it goes into that Freemium, land, expand, we call it explode. Sales force, kind of explode. And then renew. That's been our model. Get in the door, get them using Freemium, we have a great experience, go to paid. And that's usually for an application, then it goes to 10 applications, and then 300 developers and then the way we price is by developer. So the more developers who use, the better your developer adoption, the bigger the ultimate opportunity is for us. >> There's a subscription service right? >> All subscription. >> Okay and then you guys have experts that are identifying vulnerabilities, right? You put them into a database, presumably, and then you sort of operationalize that into your software and your service. >> Yeah, we have 15 people in our security team that do nothing everyday but looking for the next vulnerability. That's our vulnerability database, in a large case, is a lot of our big companies start with the database. Because you think of like Netflix and you think of Facebook, all of these companies have large security organizations that are looking for issues, looking for vulnerabilities. And they're saying, "Well okay, if I can get that feed from you, why do I have my own?" And so a lot of companies start just with the database feed and say, "Look, I'll get rid of mine, and use yours." And then eventually, we'll use this scanning and we'll evolve down the process. But there's no doubt in the market people who use our solution or other solution will say our known the database of known vulnerabilities, is far better than anybody else in the market. >> And who do you sell to, again? Who are the constituencies? Is it sec-ops, is it, you know, software engineering? Is it developers, dev-ops? >> Users are always developers. In some cases dev-ops, or dev-sec. Apps-sec, you're starting to see kind of the world, the developer security becoming bigger. You know, as you get larger, you're definitely security becomes a bigger part of the journey and some of the budget comes from the security teams. Or the risk or dev-ops. But I think if we were to, you know, with the user and some of the influencers from developers, dev-ops, and security are kind of the key people in the equation. >> Is your, you have a lot of experience in the enterprise. How do you see your go to market in this world different, given that it's really a developer constituency that you're targeting? I mean, normally, you'd go out, hire a bunch of expensive sales guys, go to market, is that the model or is it a little different here because of the target? >> Yeah, you know, to be honest, a lot of the momentum that we've had at this point has been inbound. Like most of the opportunities that come in, come to us from the community, from this ground up. And so we have a very large inside sales team that just kind of follows up on the inbound interest. And that's still, you know, 65, 70% of the opportunities that come to us both here and Europe and APJ, are coming from the community inbound. Okay, I'm using 10 licenses of SNYK, you know, I want to get the enterprise version of it. And so that's been how we've grown. Very much of a very cost-effective inside sales. Now, when you get to the Googles and Salesforces and Nordstroms of the world, and they have already 500 licenses us, either paid or free, then we usually have more of a, you know, senior sales person that will be involved in those deals. >> To sort of mine those accounts. But it's really all about driving the efficiency of that inbound, and then at some point driving more inbound and sort of getting that flywheel effect. >> Developer adoption, developer adoption. That's the number one driver for everybody in our company. We have a customer success team, developer adoption. You know, just make the developer successful and good things happen to all the other parts of the organization. >> Okay, so that's a key performance indicator. What are the, let's wrap kind of the milestones and the things that you want to accomplish in the next, let's call it 12 months, 18 months? What should we be watching? >> Yeah, so I mean it continues to be the community, right? The community, recruiting more developers around the globe. We're expanding, you know, APJ's becoming a bigger part. And a lot of it is through just our efforts and just building out this community. We now have 20 people, their sole job is to build out, is to continue to build our developer community. Which is, you know, content, you know, information, how to learn, you know, webinars, all these things that are very separate and apart from the commercial side of the business and the community side of the business. So community adoption is a critical measurement for us, you know, yeah, you look at Freemium adoption. And then, you know, new customers. How are we adding new customers and retaining our existing customers? And you know, we have a 95% retention rate. So it's very sticky because you're getting the data feed, is a daily data feed. So it's like, you know, it's not one that you're going to hook on and then stop at any time soon. So you know, those are the measurements. You look at your community, you look at your Freemium, you look at your customer growth, your retention rates, those are all the things that we measure our business by. >> And your big pockets of brain power here, obviously in Boston, kind of CEO's prerogative, you got a big presence in London, right? And also in Israel, is that correct? >> Yeah, I would say we have four hubs and then we have a lot of remote employees. So, you know, Tel Aviv, where a lot of our security expertise is, in London, a lot of engineering. So between London and Tel Aviv is kind of the security teams, the developers are all in the community is kind of there. You know, Boston, is kind of more go to market side of things, and then we have Ottawa, which is kind of where Watchfire started, so a lot of good security experience there. And then, you know, we've, like a lot of modern companies, we hired the best people wherever we can find them. You know, we have some in Sydney, we've got some all around the world. Especially security, where finding really good security talent is a challenge. And so we're always looking for the best and brightest wherever they are. >> Well, Peter, congratulations on the raise, the new role, really, thank you for coming in and sharing with The Cube community. Really appreciate it. >> Well, it's great to be here. Always enjoy the conversations, especially the Patriots, Red Sox, kind of banter back and forth. It's always good. >> Well, how do you feel about that? >> Which one? >> Well, the Patriots, you know, sort of strange that they're not deep into the playoffs, I mean, for us. But how about the Red Sox now? Is it a team of shame? All my friends who were sort of jealous of Boston sports are saying you should be embarrassed, what are your thoughts? >> It's all about Houston, you know? Alex Cora, was one of the assistant coaches at Houston where all the issues are, I'm not sure those issues apply to Boston, but we'll see, TBD. TBD, I am optimistic as usual. I'm a Boston fan making sure that there isn't any spillover from the Houston world. >> Well we just got our Sox tickets, so you know, hopefully, they'll recover quickly, you know, from this. >> They will, they got to get a coach first. >> Yeah, they got to get a coach first. >> We need something to distract us from the Patriots. >> So you're not ready to attach an asterisk yet to 2018? >> No, no. No, no, no. >> All right, I like the optimism. Maybe you made the right call on Tom Brady. >> Did I? >> Yeah a couple years ago. >> Still since we talked what, two in one. And they won one. >> So they were in two, won one, and he threw for what, 600 yards in the first one so you can't, it wasn't his fault. >> And they'll sign him again, he'll be back. >> Is that your prediction? I hope so. >> I do, I do. >> All right, Peter. Always a pleasure, man. >> Great to see you. >> Thank you so much, and thank you for watching everybody, we'll see you next time. (groovy techno music)

>> Live from San Jose in the heart of Silicon Valley, it's theCUBE, covering DataWorks Summit 2018, brought to you by Hortonworks. >> Welcome back to theCUBE's live coverage of DataWorks here in San Jose, California. I'm your host, Rebecca Knight, along with my co-host, James Kobielus. We have with us Eric Herzog. He is the Chief Marketing Officer and VP of Global Channels at the IBM Storage Division. Thanks so much for coming on theCUBE once again, Eric. >> Well, thank you. We always love to be on theCUBE and talk to all of theCUBE analysts about various topics, data, storage, multi-cloud, all the works. >> And before the cameras were rolling, we were talking about how you might be the biggest CUBE alum in the sense of you've been on theCUBE more times than anyone else. >> I know I'm in the top five, but I may be number one, I have to check with Dave Vellante and crew and see. >> Exactly and often wearing a Hawaiian shirt. >> Yes. >> Yes, I was on theCUBE last week from CISCO Live. I was not wearing a Hawaiian shirt. And Stu and John gave me a hard time about why was not I wearing a Hawaiian shirt? So I make sure I showed up to the DataWorks show- >> Stu, Dave, get a load. >> You're in California with a tan, so it fits, it's good. >> So we were talking a little bit before the cameras were rolling and you were saying one of the points that is sort of central to your professional life is it's not just about the storage, it's about the data. So riff on that a little bit. >> Sure, so at IBM we believe everything is data driven and in fact we would argue that data is more valuable than oil or diamonds or plutonium or platinum or silver to anything else. It is the most viable asset, whether you be a global Fortune 500, whether you be a midsize company or whether you be Herzogs Bar and Grill. So data is what you use with your suppliers, with your customers, with your partners. Literally everything around your company is really built around the data so most effectively managing it and make sure, A, it's always performant because when it's not performant they go away. As you probably know, Google did a survey that one, two, after one, two they go off your website, they click somewhere else so has to be performant. Obviously in today's 365, 7 by 24 company it needs to always be resilient and reliable and it always needs to be available, otherwise if the storage goes down, guess what? Your AI doesn't work, your Cloud doesn't work, whatever workload, if you're more traditional, your Oracle, Sequel, you know SAP, none of those workloads work if you don't have a solid storage foundation underneath your data driven enterprise. >> So with that ethos in mind, talk about the products that you are launching, that you newly launched and also your product roadmap going forward. >> Sure, so for us everything really is that storage is this critical foundation for the data driven, multi Cloud enterprise. And as I've said before on theCube, all of our storage software's now Cloud-ified so if you need to automatically tier out to IBM Cloud or Amazon or Azure, we automatically will move the data placement around from one premise out to a Cloud and for certain customers who may be multi Cloud, in this case using multiple private Cloud providers, which happens due to either legal reasons or procurement reasons or geographic reasons for the larger enterprises, we can handle that as well. That's part of it, second thing is we just announced earlier today an artificial intelligence, an AI reference architecture, that incorporates a full stack from the very bottom, both servers and storage, all the way up through the top layer, then the applications on top, so we just launched that today. >> AI for storage management or AI for run a range of applications? >> Regular AI, artificial intelligence from an application perspective. So we announced that reference architecture today. Basically think of the reference architecture as your recipe, your blueprint, of how to put it all together. Some of the components are from IBM, such as Spectrum Scale and Spectrum Computing from my division, our servers from our Cloud division. Some are opensource, Tensor, Caffe, things like that. Basic gives you what the stack needs to be, and what you need to do in various AI workloads, applications and use cases. >> I believe you have distributed deep learning as an IBM capability, that's part of that stack, is that correct? >> That is part of the stack, it's like in the middle of the stack. >> Is it, correct me if I'm wrong, that's containerization of AI functionality? >> Right. >> For distributed deployment? >> Right. >> In an orchestrated Kubernetes fabric, is that correct? >> Yeah, so when you look at it from an IBM perspective, while we clearly support the virtualized world, the VM wares, the hyper V's, the KVMs and the OVMs, and we will continue to do that, we're also heavily invested in the container environment. For example, one of our other divisions, the IBM Cloud Private division, has announced a solution that's all about private Clouds, you can either get it hosted at IBM or literally buy our stack- >> Rob Thomas in fact demoed it this morning, here. >> Right, exactly. And you could create- >> At DataWorks. >> Private Cloud initiative, and there are companies that, whether it be for security purposes or whether it be for legal reasons or other reasons, don't want to use public Cloud providers, be it IBM, Amazon, Azure, Google or any of the big public Cloud providers, they want a private Cloud and IBM either A, will host it or B, with IBM Cloud Private. All of that infrastructure is built around a containerized environment. We support the older world, the virtualized world, and the newer world, the container world. In fact, our storage, allows you to have persistent storage in a container's environment, Dockers and Kubernetes, and that works on all of our block storage and that's a freebie, by the way, we don't charge for that. >> You've worked in the data storage industry for a long time, can you talk a little bit about how the marketing message has changed and evolved since you first began in this industry and in terms of what customers want to hear and what assuages their fears? >> Sure, so nobody cares about speeds and feeds, okay? Except me, because I've been doing storage for 32 years. >> And him, he might care. (laughs) >> But when you look at it, the decision makers today, the CIOs, in 32 years, including seven start ups, IBM and EMC, I've never, ever, ever, met a CIO who used to be a storage guy, ever. So, they don't care. They know that they need storage and the other infrastructure, including servers and networking, but think about it, when the app is slow, who do they blame? Usually they blame the storage guy first, secondarily they blame the server guy, thirdly they blame the networking guy. They never look to see that their code stack is improperly done. Really what you have to do is talk applications, workloads and use cases which is what the AI reference architecture does. What my team does in non AI workloads, it's all about, again, data driven, multi Cloud infrastructure. They want to know how you're going to make a new workload fast AI. How you're going to make their Cloud resilient whether it's private or hybrid. In fact, IBM storage sells a ton of technology to large public Cloud providers that do not have the initials IBM. We sell gobs of storage to other public Cloud providers, both big, medium and small. It's really all about the applications, workloads and use cases, and that's what gets people excited. You basically need a position, just like I talked about with the AI foundations, storage is the critical foundation. We happen to be, knocking on wood, let's hope there's no earthquake, since I've lived here my whole life, and I've been in earthquakes, I was in the '89 quake. Literally fell down a bunch of stairs in the '89 quake. If there's an earthquake as great as IBM storage is, or any other storage or servers, it's crushed. Boom, you're done! Okay, well you need to make sure that your infrastructure, really your data, is covered by the right infrastructure and that it's always resilient, it's always performing and is always available. And that's what IBM drives is about, that's the message, not about how many gigabytes per second in bandwidth or what's the- Not that we can't spew that stuff when we talk to the right person but in general people don't care about it. What they want to know is, "Oh that SAP workload took 30 hours and now it takes 30 minutes?" We have public references that will say that. "Oh, you mean I can use eight to ten times less storage for the same money?" Yes, and we have public references that will say that. So that's what it's really about, so storage is really more from really a speeds and feeds Nuremberger sort of thing, and now all the Nurembergers are doing AI and Caffe and TensorFlow and all of that, they're all hackers, right? It used to be storage guys who used to do that and to a lesser extent server guys and definitely networking guys. That's all shifted to the software side so you got to talk the languages. What can we do with Hortonworks? By the way we were named in Q1 of 2018 as the Hortonworks infrastructure partner of the year. We work with Hortonworks all time, at all levels, whether it be with our channel partners, whether it be with our direct end users, however the customer wants to consume, we work with Hortonworks very closely and other providers as well in that big data analytics and the AI infrastructure world, that's what we do. >> So the containerizations side of the IBM AI stack, then the containerization capabilities in Hortonworks Data Platform 3.0, can you give us a sense for how you plan to, or do you plan at IBM, to work with Hortonworks to bring these capabilities, your reference architecture, into more, or bring their environment for that matter, into more of an alignment with what you're offering? >> So we haven't an exact decision of how we're going to do it, but we interface with Hortonworks on a continual basis. >> Yeah. >> We're working to figure out what's the right solution, whether that be an integrated solution of some type, whether that be something that we do through an adjunct to our reference architecture or some reference architecture that they have but we always make sure, again, we are their partner of the year for infrastructure named in Q1, and that's because we work very tightly with Hortonworks and make sure that what we do ties out with them, hits the right applications, workloads and use cases, the big data world, the analytic world and the AI world so that we're tied off, you know, together to make sure that we deliver the right solutions to the end user because that's what matters most is what gets the end users fired up, not what gets Hortonworks or IBM fired up, it's what gets the end users fired up. >> When you're trying to get into the head space of the CIO, and get your message out there, I mean what is it, what would you say is it that keeps them up at night? What are their biggest pain points and then how do you come in and solve them? >> I'd say the number one pain point for most CIOs is application delivery, okay? Whether that be to the line of business, put it this way, let's take an old workload, okay? Let's take that SAP example, that CIO was under pressure because they were trying, in this case it was a giant retailer who was shipping stuff every night, all over the world. Well guess what? The green undershirts in the wrong size, went to Paducah, Kentucky and then one of the other stores, in Singapore, which needed those green shirts, they ended up with shoes and the reason is, they couldn't run that SAP workload in a couple hours. Now they run it in 30 minutes. It used to take 30 hours. So since they're shipping every night, you're basically missing a cycle, essentially and you're not delivering the right thing from a retail infrastructure perspective to each of their nodes, if you will, to their retail locations. So they care about what do they need to do to deliver to the business the right applications, workloads and use cases on the right timeframe and they can't go down, people get fired for that at the CIO level, right? If something goes down, the CIO is gone and obviously for certain companies that are more in the modern mode, okay? People who are delivering stuff and their primary transactional vehicle is the internet, not retail, not through partners, not through people like IBM, but their primary transactional vehicle is a website, if that website is not resilient, performant and always reliable, then guess what? They are shut down and they're not selling anything to anybody, which is to true if you're Nordstroms, right? Someone can always go into the store and buy something, right, and figure it out? Almost all old retailers have not only a connection to core but they literally have a server and storage in every retail location so if the core goes down, guess what, they can transact. In the era of the internet, you don't do that anymore. Right? If you're shipping only on the internet, you're shipping on the internet so whether it be a new workload, okay? An old workload if you're doing the whole IOT thing. For example, I know a company that I was working with, it's a giant, private mining company. They have those giant, like three story dump trucks you see on the Discovery Channel. Those things cost them a hundred million dollars, so they have five thousand sensors on every dump truck. It's a fricking dump truck but guess what, they got five thousand sensors on there so they can monitor and make sure they take proactive action because if that goes down, whether these be diamond mines or these be Uranium mines or whatever it is, it costs them hundreds of millions of dollars to have a thing go down. That's, if you will, trying to take it out of the traditional, high tech area, which we all talk about, whether it be Apple or Google, or IBM, okay great, now let's put it to some other workload. In this case, this is the use of IOT, in a big data analytics environment with AI based infrastructure, to manage dump trucks. >> I think you're talking about what's called, "digital twins" in a networked environment for materials management, supply chain management and so forth. Are those requirements growing in terms of industrial IOT requirements of that sort and how does that effect the amount of data that needs to be stored, the sophistication of the AI and the stream competing that needs to be provisioned? Can you talk to that? >> The amount of data is growing exponentially. It's growing at yottabytes and zettabytes a year now, not at just exabytes anymore. In fact, everybody on their iPhone or their laptop, I've got a 10GB phone, okay? My laptop, which happens to be a Power Book, is two terabytes of flash, on a laptop. So just imagine how much data's being generated if you're doing in a giant factory, whether you be in the warehouse space, whether you be in healthcare, whether you be in government, whether you be in the financial sector and now all those additional regulations, such as GDPR in Europe and other regulations across the world about what you have to do with your healthcare data, what you have to do with your finance data, the amount of data being stored. And then on top of it, quite honestly, from an AI big data analytics perspective, the more data you have, the more valuable it is, the more you can mine it or the more oil, it's as if the world was just oil, forget the pollution side, let's assume oil didn't cause pollution. Okay, great, then guess what? You would be using oil everywhere and you wouldn't be using solar, you'd be using oil and by the way you need more and more and more, and how much oil you have and how you control that would be the power. That right now is the power of data and if anything it's getting more and more and more. So again, you always have to be able to be resilient with that data, you always have to interact with things, like we do with Hortonworks or other application workloads. Our AI reference architecture is another perfect example of the things you need to do to provide, you know, at the base infrastructure, the right foundation. If you have the wrong foundation to a building, it falls over. Whether it be your house, a hotel, this convention center, if it had the wrong foundation, it falls over. >> Actually to follow the oil analogy just a little bit further, the more of this data you have, the more PII there is and it usually, and the more the workloads need to scale up, especially for things like data masking. >> Right. >> When you have compliance requirements like GDPR, so you want to process the data but you need to mask it first, therefore you need clusters that conceivably are optimized for high volume, highly scalable masking in real time, to drive the downstream app, to feed the downstream applications and to feed the data scientist, you know, data lakes, whatever, and so forth and so on? >> That's why you need things like Incredible Compute which IBM offers with the Power Platform. And why you need storage that, again, can scale up. >> Yeah. >> Can get as big as you need it to be, for example in our reference architecture, we use both what we call Spectrum Scale, which is a big data analytics workload performance engine, it has multiple threaded, multi tasking. In fact one of the largest banks in the world, if you happen to bank with them, your credit card fraud is being done on our stuff, okay? But at the same time we have what's called IBM Cloud Object Storage which is an object store, you want to take every one of those searches for fraud and when they find out that no one stole my MasterCard or the Visa, you still want to put it in there because then you mine it later and see patterns of how people are trying to steal stuff because it's all being done digitally anyway. You want to be able to do that. So you A, want to handle it very quickly and resiliently but then you want to be able to mine it later, as you said, mining the data. >> Or do high value anomaly detection in the moment to be able to tag the more anomalous data that you can then sift through later or maybe in the moment for realtime litigation. >> Well that's highly compute intensive, it's AI intensive and it's highly storage intensive on a performance side and then what happens is you store it all for, lets say, further analysis so you can tell people, "When you get your Am Ex card, do this and they won't steal it." Well the only way to do that, is you use AI on this ocean of data, where you're analyzing all this fraud that has happened, to look at patterns and then you tell me, as a consumer, what to do. Whether it be in the financial business, in this case the credit card business, healthcare, government, manufacturing. One of our resellers actually developed an AI based tool that can scan boxes and cans for faults on an assembly line and actually have sold it to a beer company and to a soda company that instead of people looking at the cans, like you see on the Food Channel, to pull it off, guess what? It's all automatically done. There's no people pulling the can off, "Oh, that can is damaged" and they're looking at it and by the way, sometimes they slip through. Now, using cameras and this AI based infrastructure from IBM, with our storage underneath the hood, they're able to do this. >> Great. Well Eric thank you so much for coming on theCUBE. It's always been a lot of fun talking to you. >> Great, well thank you very much. We love being on theCUBE and appreciate it and hope everyone enjoys the DataWorks conference. >> We will have more from DataWorks just after this. (techno beat music)

>> Announcer: From Austin, Texas, it's theCUBE covering DockerCon 2017. Brought to you by Docker and support from its ecosystem partners. (techno music) >> Welcome back. Hi, I'm Stu Miniman, joined with Jim Kobielus. You're watching theCUBE's SiliconANGLE Media's production of DockerCon 2017. We're the worldwide leader in live enterprise tech coverage. And we can't finish any DockerCon without having Jerry Chen on. So, Jerry, partner with Greylock, always a pleasure to interview you. We've had you on the Amazon shows a lot, Docker, other ecosystem shows, so, great to see ya. >> Stu, Jim. Hey, thanks for having me, as always. It's great to be here. >> Alright, so first of all, I mean, you invested back in the dotCloud days. Could you imagine, when you were meeting with Solomon and those guys and everything that we'd be here with 5,500 people as to where they'd go? What's your take on the growth? >> Every year just blows my mind, both in open-source community developers, ecosystem partners, and more recently, past year and a half, the enterprise customers that take Docker seriously, or replatformed applications on Docker, amazes me. I think I did an investment in 2013, and there were a few hundred thousand downloads of Docker, now there's billions and billions of containers being pulled. When I talk to CIOs that I deal with frequently, they're like, "Docker containers, what is this thing, pants?" And then, (laughter) three and a half, four years later, I can't have a conversation without a Fortune 500 CIO without talking about their Docker container strategy. >> By the way, I hear if you do send back a belt or something that's broken to the Docker people, they'll fix it for you, and maybe send some whale stickers. >> It's like the old school Nordstroms where they take any return. They're this urban store, with the four tires return to Nordstrom, return some pants, you'll be fine. >> You know, we work on container strategy, but we're also your repair shop for you know, men's apparel. So, it's always interesting to look at-- >> Jim: Integration fabric. >> Brilliant. You know, the maturation of technology, of ecosystem, of monetization. I feel like you talked about the growth of the containers. We've seen the ecosystem. It's gone through some fits and spurts and changes over the last couple of years. I think we're really well-received this week. And then there's the money maturation and how they mature that. What do you see? How does open-source fit into your investment strategy, and any commentary on Docker and beyond? >> I was thinking about this on the flight over here today. Open source today is very different than open source five years ago, 10 years ago, as 15. So what what Red Hat did 20 years ago, is very different than what Xen tried to do 10 years ago. When I was at VMware, very different from what Docker is doing today. And it's different in a couple ways. I think the way you monetize is different. Because you have cloud, and cloud changes things. The ecosystem's very different, because all of a sudden the developers, contributors, are not just kind of your misfits and rebels working on the weekends. They are Fortune 100, Fortune 500 companies. Their jobs are now dedicated to this. And then the business models of the developers' ecosystem, how you work with them is very different. So before, you had maybe one or two models to make money in open source. Or one or two ways to develop a community. We did that at Red Hat, which Greylock was lucky enough to be investors in years ago. I was at VMware around Cloud Foundry, we built that. We had a model mine, we had a spring source as well, and what you've seen Docker in the past three or four years, is they're really pioneering a way to bring open source and community ecosystem into the next 10-20 years. So I think it's one to watch. I think Solomon's probably as good as anybody understanding what developers need. >> So a little broader, what's your thoughts on developers today? You actually made the comment coming over, there's two big developer shows this week. You've got F8 and you've got DockerCon, two very different communities. >> Right, it's kind of funny. There's always this sense of, do you consider yourself a developer? So if I write a line of JavaScript, am I a developer? My two cents is yes. If I'm a developer, from JavaScript to Swift to Docker to cURL hacking, it's all great. But if you look at those two conferences, you have F8 going on right now, and the announcements there around augmented reality and messaging, and it's trying to be a platform, but they're doing many of the same things. You have a distribution platform be it Messenger or Facebook, and they're open sourcing technologies around the camera, the lens, the filters, to have developers a) go through the channel, b) add apps or widgets. It's really beyond my ability to comprehend these filters, but Docker today announced a couple great projects: Moby and Linux Kit, much the same way as trying to give tools to the ecosystem developers to build what they want. I think what you've learned is, if you give developers the building blocks, the "Legos" as they call it today, they're going to build some awesome structures. >> Jim was, we talked about coming in here as the role of how data science fits into the developers, and developer is such a broad term, as to what we have here. >> One of the core themes I have is that the data scientist is the nucleus of next generation developer because much of the IP that's being built in the applications now, is statistical models and machine learning and so forth, driving recommendation, but much of that development is being containerized using new tool kits and so forth. But it needs to be more containerized so you can deploy statistical predictive models, machine learning, deep porting to routing the string ecosystem into a hybrid cloud to perform various functions. >> Right now there's, in most companies, there's a data engineer, there's a data scientist, and the two typically work hand in hand. >> Jim: One manages Hadoop, the other one does the modeling. >> Does the modeling, so one speaks in R and Python and works in Jupyter Notebook, the other person runs on Hadoop or database or Redis. The two need to work together and so what you're seeing now and obviously we're investors of Cloudera, that's another great open source company, what you see now is either a) a set of tools and technologies to either blend the two together in some cases, either enable engineers to be more data scientists, or enable data scientists to be more engineers, but also see a bunch of technology tools that say, no, two different roles, I'm going to create tools purpose-built for the data scientists, create tools purpose-built for the power of a data engineer. And I think there's space for both to the extent that you have applications running from news feed or ads to predicting how my self-driving car should make a left turn, you're going to need tools that are used by both types of populations. >> I think Cloudera now has a collaboration environment in the data science department. IBM has something very similar with what they're doing, so it's a team that has specialties such as coders, such as data modelers and data engineers. Point well taken. Cloudera's made a major entrance into that space of collaborative development, of these rich stacks of IP, essentially, that include deterministic program code, but also probabilistic models in a deepening stack. >> I think you've seen Cloudera definitely follow that path from Hadoop and low-level file system HDFS, to these high-level tools for data scientists that's becoming a platform for machine learning for these next generation applications. I think you see Docker in the infrastructure analogy doing low-level tools like Project Moby and Linux Kit, to high-level services around Docker Datacenter. So you can either have the basic tools for your low-level developer, or for the system admin or administrator who wants to operate or run the cloud, you have tools for him or her, too. >> It's interesting, you look at some of these projects and some of the maturation and pivots you see. We talked about dotCloud went over to Docker. You see a bunch of open stock companies that are now Kubernetes companies. I see companies that were big data, they're now, "Oh, I'm an AI or ML company." It's always like, it's usually not the tool, it's the wave. What is the driver? Is data the driver of our next wave there? Is it the application? Is it some combination of the two? Those are the two that I usually look at. Follow the data, follow the application. >> I would say it's data driving. It's really data application, it's data, and the applications make use of the data. Algorithms, I think, is a component. They're important, but they're a component. So what you see now is, to be on the right side of history, data is outstripping compute and storage, so the amount of videos and center data that we're generating from our phones, our cars, our homes, that is outstripping most of the other charts in compute, networking, whatever. That's definitely kind of a rising tide or a wave, as Stu was saying. Now how do we extract data, or value from this data? And historically, because you didn't have infrastructure, that cloud, or compute capacity to make use of this data, it was kind of stranded, so what you've seen in generation technologies like Hadoop or big data or cloud technologies like Docker did, is distribute your applications across a cloud. That's actually enabling you to now build applications to get value out of this data. And that value can be something like forecasting your sales this quarter. It can be about figuring which shade of brown belt you should wear with your pants, going back to our clothing analogy. Or it could be like, let me build a model around how this car or this drone should drive or fly itself. So you combine the vast amount of data, nearly infinite resource of compute, with these machine-learning or AI techniques. Machine learning is one AI technique, but all these other techniques, you can build another generation application, this new intelligent application to power everything from your home, your car, your watch, or your enterprise app, as wonderful as that is. >> Much of the sea change is less and less coding or programming is actually being done or needs to be done because more of the application logic is being distilled directly from the data in the form of machine learning. There's automated machine learning tools that are coming. Google has been a major investor as is Facebook in automated machine learning. >> I would say application logic from the inside, right. So in my mind, application logic, an application is reflecting business process. Hire to fire, order to cash. You still need a program that does logic. Data in itself, or AI in itself without that context, without that business process, is meaningless, right. Just having a model around Jim or Stu, it doesn't matter unless you're trying to buy something. Google pioneered machine learning in a workflow perfectly. You're searching for something, they knew who you were based upon history, you're searching the right ad and say, "Oh, you really want to buy a car, you want to buy a house." So in the workflow, or in the application logic of a search, they used ML to serve you timely information. Now if you're an enterprise, you're looking at help desk tickets, be it ITSM like ServiceNow, or support tickets like Zendesk supporting B to C support tickets. That's a workflow, there's application logic. They take information on a user or a grumpy customer, and they do things like automatically respond to a help ticket, reset your password, provision a server. So I think when you have AI or have applications using this data in the context of a business process, that's magic. And I think we're seeing some core technologies like TensorFlow out there that are super compelling. But we're seeing a generation of developers and founders take that technology, apply it to a problem, it could be HR or CRM, ITSM, or true vertical. Construction, finance, health care. >> Jim: Streaming media analytics is a core area where that's coming in. >> Media analytics because there's a ton of data. Understand what you watch and what you want to see, and so you apply things to a vertical, like health care, or apply the technology to a problem space like media analytics, and you have a wonderful application and hopefully a great company. >> Jerry, we've talked a lot at the cloud shows about how do the startups maintain relevant and get involved when there's all of these platforms. We talked about what Google does, Amazon of course is eating the entire world in everything. Microsoft is making lot of moves here. How do companies, what do you look for? Has your investment strategy changed at all in the last couple of years? >> It is daunting. I think about this a lot in terms of business models and defensibility, and the question goes, what are the sustainable moats you can build around your business as a startup anymore? 'Cause you feel like economies of scale and ecosystems, network effects, those were historically big defensive moats for a Windows operating system. Now those apply to Facebook's platform, Apple's platform, or AWS. They have scale and they have network effects for the ecosystem, so now your startup is saying, okay, how can I either a) overcome those moats, or b) how can I develop my own IP or my own moats around myself that I can actually sustain and thrive in this generation. I think you got to play a different game. As a startup, you're not going to try to out-scale Google or Microsoft; leave that to Amazon and those three or four players. But you can get scale in a domain, so either a problem space like autonomous vehicles, security is a great one, or vertical construction or health care. You redefine the market that you can dominate, can you build your own moat around that IP. >> It's interesting. did you hear Adrian Cockcroft who went from Battery Ventures over to AWS. He's like, "Well, rather than go startup that business, "come build that next thing at Amazon "and we'll do it there." Is that a viable way for people with the entrepreneurial spirit to go be part of that two-pizza team doing something cool inside a large platform? >> I think Adrian probably has motivation and more developers on Amazon now, but I would say most of our companies, not all, but a lot of them started at Amazon. Some start in ads, some start in Google, some start with their own data centers. I think what they believe is they'll get started in one of these clouds but I don't believe, so we talked about this first, it's not a one-cloud-rules-all world. I think there'll be three or four, if not more, clouds in every different geography from Europe to Asia to Russia to the US, will have different clouds, different players. So I think it's fine to get started in Amazon and be a two-pizza team with the other two-pizza team, but over time I see these applications being cross-cloud, and that's where something like Docker comes into play. Docker wants to be cross-cloud, better than any other technology out there. >> On some level, actually, the moat could be, or increasingly is, the training data that drives the refinement of your AI, like Tesla is a perfect example. The self-driving capabilities that they built into the vehicle, they have now a few years' worth of rich test data, training data I should say, that is a core moat in terms of continuing refinement of those algorithms. So that gives you sort of an example of some startup might come along with some very specialized application that takes the consumer world by storm and then they build up some deep well of training data in some very specialized area that becomes their core asset that their next competitor down the pipe doesn't have. >> It has to be a set of data that's unique or proprietary. You're not going to basically out-train your model on cat photos from Google, right? So it has to be a combination of either proprietary data or a combination of data sources that you can stick together. So it's not just one data source, I believe you have to combine multiple data sources together. >> So Jerry, sitting over Jim's shoulder is VMware's booth. I haven't talked about VMware at all this week. You worked at VMware, I've worked with VMware since pretty early days. What advice would you give VMware in the containerized cloud future? How should they be doing more to be part of more conversations? >> I think it's amazing that they have a presence here in the size and scale. The past couple years they're really done a lot to embrace containers and Docker, so I think that's first and foremost. They've done a couple great moves lately. Embracing Amazon last year, with VMware on Amazon, was a big move. Embracing containers with some of their cloud and data technologies I think was an aggressive move too. So I think they're moving in the right direction. I think what they need to understand is, are they going to revolutionize themselves and push these new technologies aggressively, or are they going to keep hanging onto some of their old businesses? For any company of their size and scale, they have multiple motivations, but I think they're making the right steps. So five years ago, or four years ago, I don't think they would have taken this DockerCon seriously. I don't think they were exhibitors at the first DockerCon. But in the past 24 months they've done some amazing moves, so I would say it makes me smile to see them take these great steps forward. >> Jerry, I want to give you the last word. Any cool companies we should be looking at, or things that are exciting to you without giving away trade secrets? >> I can't broadcast the companies I want because everyone else is going to chase those investments. I don't know, I think I'm going to enjoy spending time, actually less with the companies here but a lot with the developers and customers, because I think by the time they have a booth here, everybody knows the company's investment is probably too far along maybe for me to invest, maybe not. But talking to developers to hear what are their friction points? I think when you hear enough friction either in this ecosystem or another ecosystem or at AWS or VWware, then there's something there, you just got to scratch. >> I was talking to some of the people working the booths and they just said the quality of the attendees here, you learn something with every single person you talk to, and there's only a few shows that say that. Amazon reinvented one, the quality of the attendees always real good, this one and a few others. >> I think people who come here by definition are learners, both the companies and the individuals, and you want to surround yourself with learners, people who are open and honest and always learning. >> Jerry, I think that's a perfect note to end it on. We are always learners here and helping to help our audience in trying to understand these technologies, so Jerry Chen, always a pleasure. And we'll be back with the wrap-up here of day one DockerCon 2017. 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