>>Hi, I'm Peter Burress. And welcome to another cube conversation. This one from B M sees Helix Immersion Days and Santa Clara, California The Senate Clara Marriott. Every organization of any scale now has to lean on their suppliers in the technology world in different ways. It used to be you could almost have an antagonistic relationship with whoever was offering you technology. But today, every business is becoming increasingly dependent on technology suppliers that are providing crucial strategic service's. And that relationship is changing the way we think about technology. That is nowhere more obvious than the Manage service's provider space or the MSP space, which is highly dependent upon very complex delivery of very rich service's and a set of analytics that air allow the many service provider in the business to work together to achieve strategic ends. Now have a conversation about how that's working and how that's changing. We've got a great cube conversation got Aldo Romero, who's the cross service is deputy director at Keio Networks Mexico. Alda, Welcome to the Cube. So let's start with what is key networks tell us a little bit about Kiyo Networks. >>Okay, Kyo networks. Personally, he canna from dad and me, you know so much. Probably. Service is a technology. Inform us on the mission critical tenemos court. Enter data centers until Mexico Panorama making quarter political American Guatemala. So if >>we think about this challenge upfront, I said that increasingly, business has to think about treating its suppliers differently in the manage service providers at the vanguard of that, what catalyzed Keogh Network's decision to start thinking about how digital service is and operations management. We're gonna have to start coming together so that you could provide a better set of managed service capabilities to your clients. >>Another 50 are intellectuals For most of the heat does Bella Paralysis. Harvey Seo is a wall of stones in front under the remit Parma coral and triggers. A reason was clean and it's Santy Okay, on a work visa, no Russian grand plataforma CCTV shows was gonna transform our nose and Monroe and Moroccan era Watson was clean. A the city most cake. Alex LaMarca Hello, Obama said a roller. I mean telekinesis. Thomas put up a little emporia. Sorry. C'mon, process. So that's a gimme into control. Purple rules transformer heat element. So as >>you think about using BMC Helix and other classes of technology. You must have a vision in mind of where your relationships and how your service is are gonna be provided. Tell us a little bit about the relationship that you have with BMC Helix and how it's informing and altering and adjusting the promises in the value propositions that you have to your manage. Service customers. >>LaMarca parties with the most. The teleconference. A Norman 10. Vamos a bodyguard Transformer journals. You have a key on networks LLC and technology on the set of issues it processes your lot ago, most in your mutual momentous and those qualities of Israel experience. Check every message from Ministro. Probable servicios is most polio liberal exito process a literal form. A syndicate tile in our mentor mentor now look innocent of all arsonist risk. Leontes, his former meant importante para nosotros a parabola guarantee survey. No Star Service >>manage Service's has been around for a while. We're now talking about Cloud Service isn't as important subset of the manage service of space, but as one that over the course of the next few years might even become more important, especially in countries like Mexico that are growing so fast and introducing increasingly complex capabilities within their economies. As Keogh Networks evolves, do you see yourself being a leader in how cloud service is evolve as well? >>See if you determine, take yours leader in Lapa Improbable level problems servicios the clout that most officials the club go on Amazon. The notary's is the cloak on Microsoft tennis officials that throughout the opening stock include Syria. Infinity tormented knows Romans camellia in America. >>So one last question as you envision moving forward with this increasingly combined digital service, is management and operations management. What kind of leadership are you looking to be? Him? See? He looks for >>Leader Yasuoka. Stumbled booze can do is for their their arms. Trustee in testing facility. Tireless operaciones Sartre Business officials. The mission. Critical contextual. Here's the MTA cameras were Mr Alex Cee Lo Vamos con una Fortaleza, You know, in a para para nuestros revisions in America, he said, mass value. So spar partners >>Aldo Aldo Romero. Thank you very much for being on the Cube. Thank you. All the romero is tthe e Crawl Service is Deputy director Keogh Networks in Mexico, and once again, I'm Peter Burns. This has been another cute conversation until next time

>> What up universe? Welcome to our theCUBE conversation from our fantastic studios in beautiful Palo Alto, California. Today we're going to be talking about what infrastructure can do to accelerate AI. And specifically we're going to use a relationship, a burgeoning relationship between DDN and NVIDIA to describe what we can do to accelerate AI workloads by using higher performance, smarter, and more focused infrastructure for computing. Now to have this conversation, we've got two great guests, here. We've got Kurt Kuckein, who's the senior director of marketing at DDN. And also Darren Johnson, who's the global director of technical marketing for Enterprise and NVIDIA. Kurt, Darren, welcome to theCUBE. >> Thanks For having us. >> Thank you very much. >> So let's get going on this because this is a very, very important topic. And I think it all starts with this notion of that there is a relationship that you guys put forth. Kurt, why don't you describe it. >> So what we're announcing today is the ends A3I architecture, powered by NVIDIA. So it is a full, rack-level solution, a reference to architecture that's been fully integrated and fully tested to deliver an AI infrastructure very simply very completely. >> So if we think about how this or why this is important, AI workloads clearly have a special stress on underlying technology. Darren, talk to us a little bit about the nature of these workloads, and why in particular, things like GPU's and other technologies are so important to make them go fast. >> Absolutely. And as you probably know AI is all about the data. Whether you're doing medical imaging, or whether your doing actual language processing, whatever it is, it's all driven by the data. The more data that you have, the better results that you get. But to drive that data into the GPU's, you need great IO. And that's why we're here today, to talk about DDN and the partnership and how to bring that IO to the GPU's on our DJX platforms. >> So if we think about what you describe, a lot of small files, often randomly distributed, with nonetheless very high profile jobs that just can't stop this dream and start over. >> Absolutely. And if you think about the history of high-performance computing, which is very similar to AI, really IO is just that, lots of files, you have to get it there, low latency, high throughput and that's why DDN's probably nearly 20 years of experience working in that exact same domain is perfect. Because you get the parallel file system which gives you that throughput, gives you that low latency, just helps drive the GPU. >> So you mentioned HPC from twenty years of experience, now, it used to be that HPC you'd have some scientists with a bunch of graduate students, setting up some of these big, honking machines. But now we're moving with commercial domain. You don't have graduate students running around. You don't have very low cost, high quality people here. So, you know, there's a lot of administrators who nonetheless good people, but want to learn. So, how does this relationship actually start making or bringing AI within reach of the commercial world? Kurt, why don't- >> That's exactly where this reference architecture comes in right. So a customer doesn't need to start from scratch. They have a design now that allows them to quickly implement AI, It's something that's really easily deployable. We've fully integrated this solution. DDN has made changes to our parallel file system appliance to integrate directly within the DGX-1 environment. That makes that even easier to deploy from there. And extract the maximum performance out of this without having to run around and tune a bunch of knobs, change a bunch of settings, it's really going to work out of the box. >> And you know it's really done more than just the DGX-1, it's more than hardware. You've done a lot of optimization of different AI toolkits, et cetera et cetera. Talk a little about that Darren. >> Yeah so, I mean, talking about the example used, researchers in the past with HPC, what we have today are data scientists. Data scientists understand pi charts, they understand tenser flow, they understand the frameworks. They don't want to understand the underlying file system, networking, RDMA, InfiniBand, any of that. They just want to be able to come in, run their tenser flow, get the data, get the results. And just churn that, keep churning that, whether it's a single GPU or 90 DJX's or as many DJX's as you want. So this solution helps bring that to customers much easier so those data scientists don't have to be system administrators. >> So, reference architecture that makes things easier. But it's more than just for some of these commercial things. It's also the overall ecosystem, you have application providers, application developers. How is this going to impact the average ecosystem that's growing up around the need to do AI related outcomes? >> Well, I think the one point that Darren was getting to there, and one of the big impacts is also as these ecosystems reach a point where they're going to need a scale. There's somewhere where DDN has tons of experience. So many customers are starting off with smaller data sets, they still need the performance, the parallel file system in that case is going to deliver that performance. But then also, as they grow, going from one GPU to 90 DJX's is going to be an incredible amount of both performance scalability that they're going to need from their IO, as well as probably capacity, scalability. And that's another thing that we've made easy with A3I, is being able to scale that environment seamlessly, within a single name space so that people don't have to deal with a lot of, again, tuning and turning of knobs to make this stuff work really well and drive those outcomes that they need as their successful. In the end, it is the application that's most important to both of us. It's not the end of a structure, it's making the discoveries faster, it's processing the information out in the field faster, it's doing analysis of the MRI faster, and helping the doctor, helping anybody who's using this to really make faster decisions, better decisions. >> Exactly. And just to add to that, in automotive industry, you have data sets that are from 50 to 500 petabytes, and you need access to all that data, all the time, because you're constantly training and retraining to create better models, to create better autonomous vehicles. And you need the performance to do that. DDN helps bring that to bear, and with this reference architecture, simplifies it. So you get the value add of InfiniData GPU's plus its ecosystem is software plus DDN is a match made in Heaven. >> Darren Johnson, NVIDIA, Kurt Kuckein, DDN. Thanks very much for being on theCube. >> Thank you very much. >> Glad I could be here. >> And I'm Peter Burns, and once again I'd like to thank you for watching this Cube Conversation. Until next time.

(vibrant orchestral music) >> Hi, I'm Peter Burns and welcome to another CUBE Conversation from our beautiful studios in Palo Alto, California. I'm actually very excited about today's conversation because we'll be talking about the potential of human beings, of people within organizations, given this tumultuous change in this digital transformation. And to help talk about some of these crucial issues we've got, Emily He from, who is senior vice president of HCM Cloud marketing from Oracle. Emily, welcome to theCUBE. >> Thank you for having me. >> So let's just jump right into it. Let's start by, I mean Oracle's got to interesting approach. Cloud a customer, the idea of bringing the Cloud or forming applications into Cloud services. So why don't we start, what is going on with HCM Cloud at Oracle? >> You said exactly the right thing: which is, we have a very unique approach to the cloud. So we spent the last few years completely rewriting our HCM application for the Cloud. And when I think about 11 years ago when iPhone first came into being, a lot of the HR, HCM vendors rushed to embrace the mobile interface because they think that's the panacea for user adoption. As long as HR software as existed, we've always had issues with user adoptions. The early Cloud vendors really just moved their applications to the Cloud and their focus is to simplify the user interfaces by delivering this modern user experience. The problem is, that didn't really solve the fundamental user adoption problem. There data quality issues, data security issues. The work flow was cumbersome and the user interface wasn't friendly enough, right? So when Oracle started rewriting the Cloud a few years back, we took a very different approach because we already had hundreds of thousands of customers. And they had real business problems. They had complex business problems. So we're asking fundamentally very different questions. The questions we're asking is: How can we use the Cloud, and move our customers data to the Cloud by allowing them to manage the data autonomously? So we can insure data quality, data security. And how can we make the work flow so flexible that they can adjust their business processes to meet the ever changing market conditions. And lastly, how can we push our user experience to the next frontier by embracing Chatbot, voice UI, AI and deliver that really human experience. And that's exactly what we have in Oracle HCM Cloud. We have the Auntie Anne solution, and we're doing really interesting things to push the user experience to the new frontier. >> Well, that's one of the reasons why I'm so fascinated by this topic is 'cause in many respects, as you said, HCM used to be just a set of HR processes: pay roll, hiring, separating. There was just a set of processes you had to do to comply with local employment laws. >> Exactly. >> But now we're talking about using technology to do much more, to actually mediate the activities of human beings in more complex ways, incorporating a different ways of thinking about incentives so that human facing systems, supported by AI, augmented by AI allow this incredible resource, that exist with most organizations to be more productive, more fulfilled, happier and ultimately a better resource to customers. Have I got that right? >> That's such a great point. And that's why I'm so excited about the possibility AI brings to the world of business applications. If you think back on the way we approach applications in the past, we architected business processes and we used technology to deliver to those business processes. So it's an input based system and a predictable output will come out. With AI, now you have all these data from different sources and you can get insight from the data, but more importantly, the system is now suggesting actions, it's suggesting decisions, and human beings can use those insight to create more solutions. And we're also in a situation where potentially robots are working alongside humans. So what is the definition of workforce anymore? Do we include machines in our workforce management solutions and how do we think about that? And I'm personally fascinated by the possibility of having machines augment human tasks and look at the world in a completely different way. >> Well, I think you brought up this interesting point earlier, this essential point earlier that there's been an adoption problem associated with some of these complex people-oriented applications. It might very well be that as we rethink these applications and we focus more on how AI and other types of things can augment the way people work. Because a lot of employees are saying, wait a minute, I'm not process driven. I have a set of responsibilities. I have some agency within this business to serve customers. So how can we bring together those things so that the people can do what they're suppose to do. It might actually increase the likelihood that these HCM applications get adopted. Whaddya think? >> Yah, exactly. If you think about the way we're using enterprise software now, it's actually not very natural, fun or human. Every time you go through the same process, you fill out the form and some outcome will come out. Now I don't think anyone is thrilled to come to work and use enterprise software application. It's almost like you have a coworker and every time you see him, you're having the same conversation. What's your name, what's your address, what's your phone number, right? And in contrast, the way people are engaging with consumer technology is totally different. I use Siri, and I use Google Maps to navigate my traffic. And my kids have hour long conversations with Alexa. Telling jokes and ask science questions. My son is getting Siri to do his homework, math homework, which is very distressing for me. But that's a different conversation all together. And I think that's the way humans want you engaged with enterprise technology. It's already happening, so it's really our collective work, organizations responsibility to bring that type of technology to work, but like you said, there are many open questions we have to answer. >> And not the least of which, it's just not mediate, having an interaction with a machine. But also having conversations and having machinery be able to pick that up. Be able to turn that into subsequent tasks and actions so that human beings are spending more time on the creative side. And I know you have some great examples of this. Companies that are rethinking, so how they go from a human being attended to a customer problem and how that person, perhaps far away from a normal IT process, can actually quickly translate that into something that can scale within the business. >> Yeah, exactly. Yesterday, I think I mentioned this to you before, I was listening to a podcast about how Airbnb is architecting their customer experience and the way they do it is when they think about their ideal customer experience, they have one customer in mind and they really focus on re-imagining how they can deliver this wow experience, but once they nail the experience, then they got good feedback from the customer. They use machines to scale that to millions of customers. And I think that's going to be the way people want to work in the future. Human beings are uniquely good at being creative, problem solving and that's what they enjoy doing. So if we can have them focus on those tasks and have the machines help us scale things that we know will work and use them to get insight to further fine tune the experience, that'll be such a better way to work. >> I totally agree and I think that one of the important derivatives of that is the idea that increasingly we're talking about more collaboration, recognizing and amplifying the strengths of individuals and bringing them into a work force so that everybody is more confident, more comfortable and capable of working together. Certainly that's something HCM wants to do. But it also creates a new question and we spent a lot of time on theCUBE working with executives, like yourself, talking about this. How are we going to incorporate additional diversity into the workforce with an attacking with other worlds, how do you see this whole process coming together? So technology can make it easier, can liberate the potential of a lot of diverse people within a workforce. Yah, I am a huge believer in diversity. I think diversity is good for the workforce and I personally spend a lot of time promoting diversity in the leadership rank. And there are a couple of things: One is, we definitely can use software to foster more diversity in the work place. For example, if we use software to screen resumes, we can eliminate some of the demographic data to reduce bias and the software also has the ability to, for example, help us identify the ideal candidate from looking at our existing employees and come up with the right criteria, so we can get the right candidates on board. But I also think, in this new world we still have more work to do to psychologically set ourselves up for leadership positions. And I talk to a lot of women and this is the advice I usually give them: The first thing is, this applies to both men and women. You need to, really be conscious of the kind of the personal brand you're building and when I talk about personal brand, I don't mean that you go on Twitter and tweet about your personal life and tweet sheer content. It's really about being conscious about the value you are trying to exhibit at work and use your day to day actions to demonstrate those values, and that will help you create a reputation that will have a stronger impact on your career than anything else. The other thing I notice about women is, the strength for women is, women are naturally empathetic so we're very collaborative, we want to help each other, but at the same time, sometimes that can hold us back because you don't want to hurt other people's feelings by stepping forward and taking on leadership position. And men are usually much better at raising their hands and saying, "I'm ready for this position." So I think women can learn from men, and the way to do it is something I call Micro Bravery. And that is, I believe courage is a muscle you can exercise. The more you use it, the better you'll be at it and if everyday you can push yourself to do something that you're uncomfortable with, maybe it's giving someone performance feedback or maybe it's standing up and presenting, maybe it's coming here and having a conversation with you on tv. The more you do that, the more you are going to take risks and the more comfortable you will be in stepping into those leadership positions. The other thing that I noticed about a lot of women is when they have a family, they hesitate to take leadership positions. Because they think they're part is now the family and they can't do both. I firmly believe we can do both. As a matter of fact, I think being a parent makes you a really good leader because there's so many lessons you can learn from being a parent. One of the things I find helpful is, now that I have children, every time I make a tough decision I always ask myself: If I make this decision and I tell my kids, would they proud of me? If they told me, they make this decision would I be proud of them. So it kind of help you bring humanity to work and really strengthen your moral compass. So those are things I usually tell women to be more effective at work place and hopefully they, more women will assume the leadership roles. >> I love hearing that in theCUBE. So just to quickly summarize. We've talked about how women in particular, but overall, we're going to get an increasingly diverse workforce that's going to be applied to increasingly complex problems and the powerful role that software can play if it's set up right to facilitate collaboration, facilitate interaction, augment the human experience, so we can do more, do more productively, make everyone more happy. >> Exactly, I couldn't have said it better. >> Emily He, the senior vice president of marketing at Oracle HCM. Thank you very much for being on theCube. >> Thank you so much for having me. (dramatic music)

[Music] hi I'm Peter burns and welcome to another cube conversation we are here in our Palo Alto studios with Chen C Wang Chen C is the founder and General Partner of rain capital and an old colleague Chen see you've been around for a long time we're very happy to have you here in a cube at least in my opinion one of the leading thinkers and security what's happening in ite data security digital business security we were colleagues at four store many years ago what are you doing now well I'm doing this new fund I just started rain capital it's an early-stage venture fund focusing on cyber security innovation so very excited about that and very specific yes I was security and AI as well but the core focus is cyber security so let's talk about what that actually means because there's a lot of new practices new processes new groups with NIT that are being spawned as a consequence of this memoriae agile devops one of the groups or one of the practices or expertise centers it's especially underrepresented in the security world somewhat surprisingly is DevOps what do we need to do to bring more security into DevOps right that's a really good question and that's one of the areas that I've been focusing on in the last two three years is looking at the impact of DevOps practices to IT or including security and it's a huge impact because initially or originally what we have is security is a practice that that is gatekeeper right so you got applications being developed and then you you test them and then you go through security tests at the end and before you could deploy DevOps practices disrupt all of that what DevOps set says is I'm a developer I can deploy my application directly onto a production productionserver without going through all those gates because business agility demands it right once you have developers or testers touching your production servers directly some of the old security practices go away right you cannot do that anymore because too heavy-handed there's also the notion of portability so today it's very common for companies to want move their workloads from the internal data center to AWS or maybe next month I want to move to Google Cloud or Azure and I don't want to go through all the testing and pre-implementation practices I want to do it right away and the portability also disrupts existing security practices right so if your security policy depends on you instrumenting the server to put some kind of module on there tomorrow the server is not not there anymore right so what do you do so it engenders hosts loads of issues and also is a catalyst for innovation so that's why I'm very very excited about that so when you talk to customers users because I know you still have work with a lot of relationships I'm sure that's going to be one of the distinctions that you bring to bear when you think about what rain capital does what are say the three things that you tell them you've already mentioned you got a you got to ensure that the practices are in place that portability is at least made more obvious and that you don't bind security down to a particular device because it device may not be there that's three what are some of the kind of organizational institutional things that DevOps folks have to do to make sure that everybody gets the security profile with the need right so what we're seeing in terms organizationally or culturally the change is that in a in a DevOps led organization the the boundaries are going away right so some of the companies that I'm seeing cloud native to start with they may not have an ops team right what they do is that IT there their infrastructure team is embedded with the applications team it really the application demand and knowing what the application wants to do and then works with the developers to establish policies and deployment practices as opposed to being arms and lands from the the developers which you know creates all kinds of tension so it's an organizational shift as well and mindset shift right so the mindset shift is that you're no longer somebody who enforces policies you actually enable business versus the policy enforcer and it's easy to say but they actually requires a very deep shift in thinking so I got another question on security and I won't move to something else but really quickly as IT organizations or as businesses source their IT capabilities more from public cloud or service providers that means that they also have to have a new approach to how they to institutionalizing the work the practice the process the certainty associated with good security in in your experience what are just a couple of things that businesses have to worry about as they negotiate and monitor and manage relationships with third parties as it pertains to security right that's a good question there possibly a long list of things right but I don't use the the top things is don't get locked in right all the platform providers want to give you all these enriched capabilities as long as you buy into our services right so what you want to do is I want to stay at the level that I can easily move right so then this may mean I have to do a little bit more things or have to compensate with third party technologies as opposed to buying into this vertically integrated notion of the platform providers and that's where you need to stay at because if otherwise you get locked it so that's one second is the the ability to do monitoring has to be real-time has and the the thing about DevOps is real-time visible of the real-time closed loop response right so you cannot like secure the analytics in the past has to usually is you get tons of logs put there and somebody chewing through logs and look for anomalies it's not fast enough it's not good enough anymore so what we want is monitoring capabilities that are able to do it platform in the platform independent way but able to give you real-time visibility and response capability right there and that's where the innovation comes from you know one thing I've noticed we've been talking I've been talking a couple customers and they're starting to discover that some of the cloud service providers are using security as a way to lock in right so so and security I think should be built in right it should be by default secure by default is the way we want to be you always know how to do yeah yeah and and you should be able to get out if if security is the differentiation then maybe it's not the right market rights group yeah all right so ring capital has in addition to looking at the whole DevOps world you bring in your security expertise to bear on potential investments it's got another distinction what's the other distinction about rain capital um it's a woman led venture fund which is a rarity in Silicon Valley right so oh is it I don't know you tell me yeah no it is a rarity there are not a lot of funds that one would associate as being a broadly representative of or very inclusive so as you are moving forward with recap we'll talk a bit about the evolving role of women in technology so so I would say that even though it's a woman led venture fund I don't think of ourselves as different just because woman led I think we are different because we have a deep deep understanding of the market and deep understanding of the technology and also very extensive relationship with end-users but in terms of women in technology and women in security I'm a big advocate um so for the past two years I was the program co-chair for the Grace Hopper conference I put together the security and privacy content for the conference and the the need for a an ecosystem that is inclusive that is enabling for underrepresented either gender or race it is huge right so people go to Grace Hopper conference and they come back and they so inspired because they see all these women representing them and I think in Silicon Valley we need that insecurity we need that even more because if you look at some statistics I think women in general IT is about 24% representative army representation in security is about 11% so we have a long way to go now I'm going to avoid making comments about that because it's smart not to but those are those are numbers that are distressing that's obvious clearly there's a lot of talent you're not the only one there's a lot of talent out there there's clearly got be brought to bear and so you might not be differentiated by the fact that as women you do things differently but it might nonetheless be a more comfortable home for a woman like yourself and have a great idea and want to turn it into a business failure that's one thing all right so Chauncey and by the way I got to say just a quick advertisement for the cube the cube has been a major supporter for for women in tech for a couple years now we've been at a number of these different conferences Jeff Frick who's the general manager john john fourier co co dave one co co put a lot of time and energy so we look forward and give us a point oh absolutely we look forward to more fruitful relationships like that in the future so once again I'm Peter Burris with Wicky bonds looking angle and we've been talking to Chen Zi Wang of rain Capital founder General Partner about a number of different topics Chauncey once again thank you for every much for being on the cube thank you for having me

>> Announcer: Live from San Francisco, it's theCUBE! Covering Pure Accelerate 2017. Brought to you by Pure Storage. >> Welcome to Pier 70 in San Francisco, everybody. I'm Dave Vellante with my cohost Stu Miniman, and this is Pure Accelerate 2017. Pure Storage in 2009 started a big wave of flash migrations, and the company's strategy was to specifically go after the large EMC Install base of older Symmetrix, mainframe class storage, and even to a certain extent VNX and Clariion, if anyone remembers those terms, the Install base. Pure's ascendancy was really a function of shifting from spinning disk to flash. Fast forward seven, eight, nine years later, and Pure is talking about big data and AI and machine learning and IoT, and is really trying to completely transform not only the storage industry but itself as a leading player. The last time an independent storage company hit a billion dollars is about 20 years ago, a company called NetApp. Pure is trying to be the next to be a billion dollar company. Stu Miniman, lot of action goin' on here, used to be back in the day, I bought EMC for block, NetApp for file. Pure is trying to change that. >> Yeah, and Dave, you know storage, we've talked about it when Dell bought EMC. What did that mean to the whole storage industry? I wrote an article when it happened and said it's the end of the storage industry as we know it. When I came in here, it was like, oh, we're going to be talking about storage. You mentioned NetApp; I was at a NetApp event last week, and they said, "Storing is boring." It's really about the data, it's about the new applications. I really liked in the keynote they were talking about new use cases, new applications, how do they fit into that multi-cloud world, really interesting to hear Scott Dietzen, who we've known since this company was in stealth, laying out where the company is. They've got over 33 hundred customers, lot of SaaS applications, they're talking a lot about the machine learning and the AI pieces that are in here, but at the end of the day, I mean Dave, this is their primary business is a storage array replaces, as you said, the traditional EMC boxes that used to be sold. So how much of this is still kind of an update on what the legacy is doing, how much are they ready for the future? I'm excited to dig in with some real customers here. Pure has a good movement, good customer base, I've always had some good smart people with good tech, the Puritans as they call them, all wearing orange here. So, a cool venue and excited to dig in. >> Well, it's one of the fastest-growing companies in the storage business and in the IT business, and the way that Pure has gotten there isn't, you know, in its early days it never really talked much about so-called software-defined, it just did it. One of the problems that Pure attacks is the problem of migration. David Floyer and Wikibon have written extensively about the cost of migration, the pain of migration. It was almost just assumed, well you know, if I'm buying storage I'm going to have to migrate, and I'm going to spend 50, a hundred, sometimes many hundreds of thousands of dollars migrating my workloads from older arrays to newer arrays. Pure Storage has this Evergreen concept, where through the use of software and software-defined technologies, it's able to upgrade new customers quote-unquote seamlessly, there's that overused word again, but it's able to deliver essentially storage as a service even though you're putting an appliance on their site. So it's a radically different model. They've announced some things today, for instance like three site data replication, which is very very complicated. Trying to simplify that, so a lot of really novel ideas. Again I come back to their ascendancy. It was really based, Stu, upon attacking the slow, expensive spinning disk using its data reduction technology to create parity between the cost of spinning disk and the cost of flash, something David Floyer predicted back in 2009 would happen by 2014 for the high-spin speed. Now with FlashBlade, which is essentially the file-based system that Pure has, they're going after that same mantra with higher-capacity spinning disks, really going after the NetApp base. >> Yeah, and Dave, you mentioned that Pure could be the most recent billion dollar storage company. The company that might actually beat them to that is Nutanix. Now of course, Nutanix sells more than just storage. They're hyper-converged infrastructure, which means the compute that they're also selling, that's being used there, so it's not quite apples to apples, but the last quoter Nutanix had, about 10 million dollars more in revenue than Pure did; they also had IPOed. In that hyperconverge trend, one of the things that I saw early on on that, Dave, was attacking that migration cost. Hyperconverge, like what Pure does, a software layer, you create a pool of architectures, I can add in nodes, I can change configurations, I can update without the traditional way that we used to do it in storage, which was buy that box, take months to get it in there, load it up, transfer it over, retest it, you know all of those things that really kept your time-to-value on storage down, and that's something that Pure and all the hyper-converged players have been attacking, that kind of legacy mindset that we had in storage for so long. >> Yeah, and of course Pure's approach to converged is in partnership with Cisco and presumably others, I'm not actually sure about that, but Cisco's the main partner there with FlashStack, that's their converge play. They kind of do a knock on hyper-converged, kind of de-positioning it as sort of low-end, sort of contained, within small remote offices, whereas they're positioning FlashStack as the scalable internet infrastructure. Pure does very well with SaaS companies, they do, they're increasingly doing better with Fortune 500, they've still got a long way to go there. About 80% of their business is U.S., so there's a lot of upside internationally. We're talking about a company that'll be a billion dollars in their fiscal 2018, which is fundamentally the year we're in now, they've got about a 2.4 billion dollar market cap, they're growing at about 30% a year. And very interestingly, they had mid-60% gross margins at one point last year, they had like 69.6% gross margin, which is unheard of, you know, we haven't really seen this since back in the heydays of NetApp and EMC. The question is, is that sustainable? And of course the big question that we have today, and we're going to talk to Scott Dietzen, nickname Dietz, lot of nicknames here at Pure Storage, about is the concept of a large independent storage company. That concept is going away, it's like extinct except for one company really, NetApp is the only billion-dollar storage company left. It's been 20-plus years, maybe even 25 years since that's occurred. What are your thoughts on that, Stu? You know, I wrote a piece maybe eight years ago, Can EMC Remain Independent, recognizing that most of EMC's value was coming from Vmware and of course EMC could not remain independent. Do you think a company like Pure can unseat the leaders of Dell, EMC, HPE, IBM, and remain an independent storage company? >> Well, one of the things I always look at is what is, where are they going to hit their plateau? They're reaching towards billion dollars and they do continue to grow. I think that Pure still has plenty of headroom, but how long does it take them, Dave, to get to three or five billion dollars? The reason I throw out that number is that's probably how much storage Amazon's doing today. You know, look at Amazon, it's a 15 billion dollar company, somewhere between 15 and 30% of Amazon's business, and nobody in the storage business talks about that because it just ties to my applications. So I want to follow the applications, follow the data. It's good to hear that Pure is getting in with a lot of SaaS providers. From Wikibon data, 2/3 of the public cloud data, I'm sorry, of the public cloud revenue, is SaaS providers, so absolutely here come these like Pure, SolidFire sold, before when they were an independent company, sold to lots of service providers as well as SaaS providers. Kaminario, a Massachusetts-based flash company, sells to I believe it's about half of their business, is selling to the SaaS providers because these are companies that look at, okay I need to own how I scale my environment, own those economics, and need to grow that. And just one more piece on that economics, Dave. Look at that kind of multi- or hybrid cloud world. I bristle a little bit when I hear Scott Dietzen kind of almost say, public cloud, it's in the corner. about 20% of the use cases fit in that environment, yeah we'll do snaps to Amazon, we'll do some other things. But you don't put the public cloud in the corner and just say, oh, 20% of the market's there. 'Cause that's today, and it is still growing 50, 75, 100% depending on which public cloud you're talking about. We think that there's still plenty of upside, and when does that become a headwind that will slow the growth of what Pure's doing? You see a lot of the other software storage companies out there say how do they become software? When we were at the Veeam show, Dave, how did, they really were, we're going to live in Azure. We're going to partner with AWS, and they don't really care. Pure very much, their growth, their revenue, and their margins today are all built that they're going to be selling gear with that, yes they have the Purity 1 software and they have some cloud plays, but very much seems to be saying that public cloud's not the direction. I'm sure Scott will probably give us a little more nuance there, but you know, that legacy change to new distributed architectures has been a tailwind for Pure, and when will cloud be something that will push against their growth? >> Well, we're going to ask Scott Dietzen about that, and you're right on, I mean public cloud clearly is growing, I mean it's growing like crazy, particularly the SaaS component of that. Now of course, that can be a tailwind for Pure because they do sell to SaaS companies. They even, Scott even had a slide up there today showing Google, Uber, Facebook, AWS. Did you infer like I did that they were implying that they were selling to those companies, or? >> No, no no, I saw because in the last quarterly report they talked about basically the number four through a thousand. >> Dave: Four to a thousand. >> Dave: Right. >> So they're not selling to the top three, that they're clear on. >> So, okay, so the top three would be Amazon, Google, and Microsoft-- >> Right. >> But then there's Facebook, and Uber, possibly they could sell to those companies, Spotify is a SaaS company, so that SaaS part of the market is growing like crazy. Now the other point is, Wikibon released a study. We've been talking about it for the last couple of weeks in theCUBE around the true private cloud market forecast. True private cloud is an on-prem infrastructure that substantially mimics the public cloud at a much lower cost. We came up with this notion of true private cloud because there was so much cloudwashing going on, which really was virtualization. Now, the true private cloud is growing actually faster than any other cloud segment, now from a smaller base, granted. But we see about a 230 billion dollar TAM over the next 10 years evolving. Now, the most important part of this, and Scott Dietzen touched upon this in the morning, as did Hat, using some nicknames again, that companies are really focused on lowering their IT labor costs, and we see 150 billion dollars, approximately, of IT labor moving out of nondifferentiated heavy lifting, into what we sometimes call vendor R&D. In the form of cloud, or on-prem products, appliances, and other software frameworks that can automate and eliminate this low-value provisioning and patching and LUN management. So, Stu, you were very much involved in that true private cloud report, that market's exploding. I mean, to me, it's all about TAM expansion for Pure. They're a billion dollar company, roughly, they're participating in a 30 or 40 billion dollar market, so they have a long way to go. >> Yeah, absolutely. Because really, Dave, it's about the application. It is not a winner-takes-all environment. When you look at multicloud, it's what applications, and even we start teasing apart pieces of my applications and where they live. So, I look at, there was a nice logo slide that Pure put up, and you say okay, Hulu is a customer. Well, is Pure helping with their CDN? I really doubt it. You know, you look at Workday. Workday, up on stage at Amazon Reinvent talking about how they partnered with Amazon. So what applications is Pure winning, which ones are their customers using the public cloud for, and how does all of that sort out? Absolutely, true private cloud is really that reinvention of the data center, that flipping, if you will, of I mean Dave, you probably know better than me, that saying that IT spends 80 or 90 percent of their budget on keeping the lights on. How do we flip that so we can spend money on innovating, driving the business forward, stop spending on one of our favorite terms, undifferentiated heavy lifting and move to innovate and drive the business, and have IT serving those applications and serving the things that help me differentiate from the competition and move faster. Because, absolutely I'm sure something we'll hear this show, is it's that agility and that speed is what companies need, and Pure with their six nines of availability and that if you buy it today you're future-proof, if you will, is going to help customers say that they can have a platform that they buy today and know it's going to serve them well in the future. >> Well, Mark Benioff I think was the first that I heard said it, or it might've been Peter Burns, I can't remember, but basically there're going to be many more SaaS companies coming out of non-tech companies than tech companies. That to me, Stu, is a big, big tailwind for a company like Pure who's software first, software-defined, knows how to sell to SaaS companies. The other thing is, Pure's the latest company. They didn't say this but they certainly, one could infer it, the latest company to basically say tape is dead. So it used to be offsite backup the tape, now they're talking the flash to flash to cloud as the long-term retention. So a lot of really interesting things going on here. The venue is actually quite amazing, it's at Pier 70, this place is going to get torn down right after this show, it's a place that used to be an old steel mill that used to make battleships here, about two battleships a year during World War II. >> Yeah, the new Warriors facility is going to be here in Dogpatch soon, and I know everybody's super excited about that. >> Yeah, well, yeah, a lot of purple hats here, a lot of excited Warriors fans. >> All right, we'll be back, we've got day-to-day all day, wall-to-wall coverage of Pure Accelerate, #PureAccelerate. This is theCUBE, I'm Dave Vellante with Stu Miniman, we'll be right back with Scott Dietzen right after this short break. (upbeat electronic chords)

>> Announcer: Live from San Jose, California, it's the Cube, covering Big Data Silicon Valley 2017. (upbeat music) >> Hello and welcome to the Cube's special coverage of Big Data SV, Big Data in Silicon Valley in conjunction with Strata + Hadoop. I'm John Furrier with George Gilbert, with Mickey Bonn and Peter Burns as well. We'll be doing interviews all day today and tomorrow, here in Silicon Valley in San Jose. Our next guest is Amit Walia who's the Executive Vice President and Chief Product Officer of Informatica. Kicking of the day one of our coverage. Great to see you. Thanks for joining us on our kick off. >> Good to be here with you, John. >> So obviously big data. this is like the eighth year of us covering, what was once Hadoop World, now it's Strata + Hadoop, Big Data SV. We also do Big Data NYC with the Cube and it's been an interesting transformation over the past eight years. This year has been really really hot with you're starting to see Big Data starting to get a clear line of sight of where it's going. So I want to get your thoughts, Amit, on where the view of the marketplace is from your standpoint. Obviously Informatica's got a big place in the enterprise. And the real trends on how the enterprises are taking analytics and specifically with the cloud. You got the AI looming, all buzzed up on AI. That really seized, people had to get their arms around that. And you see IoT. Intel announced an acquisition, $15 billion for autonomous vehicles, which is essentially data. What's your views? >> Amit: Well I think it's a great question. 10 years have happened since Hadoop started right? I think what has happened as we see is that today what enterprises are trying to encapsulate is what they call digital transformation. What does it mean? I mean think about it, digital transformation for enterprises, it means three unique things. They're transforming their business models to serve their customers better, they're transforming their operational models for their own execution internally, if I'm a manufacturing or an execution-oriented company. The third one is basically making sure that their offerings are also tailored to their customers. And in that context, if you think about it, it's all a data-driven world. Because it's data that helps customers be more insightful, be more actionable, and be a lot more prepared for the future. And that covers the things that you said. Look, that's where Hadoop came into play with big data. But today the three things that organizations are catered around big data is just a lot of data right? How do I bring actionable insights out of it? So in that context, ML and AI are going to play a meaningful role. Because to me as you talk about IoT, IoT is the big game changer of big data becoming big or huge data if I may for a minute. So machine learning, AI, self-service analytics is a part of that, and the third one would be big data and Hadoop going to cloud. That's going to be very fast. >> John: And so the enterprises now are also transforming, so this digital transformation, as you point out, is absolutely real, it's happening. And you start to see a lot more focus on the business models of companies where it's not just analytics as a IT function, it's been talked about for a while, but now it's really more relevant because you're starting to see impactful applications. >> Exactly. >> So with cloud and (chuckles) the new IoT stuff you start to say okay apps matter. And so the data becomes super important. How is that changing the enterprises' readiness in terms of how they're consuming cloud and data and what not? What's you're view on that? Because you guys are deep in this. >> Amit: Yep. >> What's the enterprises' orientation these days? >> So slight nuance to that, as an answer. I think what organizations have realized is that today two things happened that never happened in the last 20 years. Massive fragmentation of the persistence layer, you see Hadoop itself fragmented the whole database layer. And a massive fragmentation of the app layer. So there are 3,000 enterprise size apps today. So just think about it, you're not restricted to one app. So what customers and enterprises are realizing is that, the data layer is where you need to organize yourself. So you need to own the data layer, you cannot just be in the app layer and the database layer because you got to be understanding your data. Because you could be anywhere and everywhere. And the best example I give in the world of cloud is, you don't own anything, you rent it. So what do you own? You own the darn data. So in that context, enterprise readiness as you came to, becomes very important. So understanding and owning your data is the critical secret sauce. And that's where companies are getting disrupted. So the new guys are leveraging data, which by the way the legacy companies had, but they couldn't figure it out. >> What is that? This is important. I want to just double-click on that. Because you mentioned the data layer, what's the playbook? Because that's like the number one question that I get. >> Mm-hmm. >> On Cube interviews or off camera is that okay, I want to have a data strategy. Now that's empty in its statement, but what is the playbook? I mean, is it architecture? Because the data is the strategic advantage. >> Amit: Yes. >> What are they doing? What's the architecture? What are some of the things that enterprises do? Now obviously they care about service level agreements and having potentially multicloud, for instance, as a key thing. But what is that playbook for this data layer? >> That's a very good question, sir. Enterprise readiness has a couple of dimensions. One you said is that there will be hybrid doesn't mean a ground cloud multicloud. I mean you're going to be in multi SAS apps, multi platform apps, multi databases in the cloud. So there is a hybrid world over there. Second is that organizations need to figure out a data platform of their own. Because ultimately what they care for is that, do I have a full view of my customer? Do I have a full view of the products that I'm selling and how they are servicing my customers? That can only happen if you have what I call a meta-data driven data platform. Third one is, boy oh boy, you talked about self-service analytics, you need to know answers today. Having analytics be more self-serving for the business user, not necessarily the IT user, and then leveraging AI to make all these things a lot more powerful. Otherwise, you're going to be spending, what? Hours and hours doing statistical analysis, and you won't be able to get to it given the scale and size of data models. And SLAs will play a big role in the world of cloud. >> Just to follow up on that, so it sounds like you've got the self-service analytics to help essentially explore and visualize. >> Amit: Mm-hmm. >> You've got the data governance and cataloging and lineage to make sure it is high quality and navigable, and then you want to operationalize it once you've built the models. But there's this tension between I want what made the data lake great, which was just dump it all in there so we have this one central place, but all the governance stuff on top of that is sort of just well, we got to organize it anyway. >> Yeah. >> How do you resolve that tension? >> That is a very good question. And that's where enterprises kind of woke up to. So a good example I'll give you, what everybody wanted to make a data lake. I mean if you remember two years ago, 80% of the data lakes fell apart and the reason was for the fact that you just said is that people made the data lake a data swamp if I may. Just dump a lot of data into my loop cluster, and life will be great. But the thing is that, and what customers of large enterprises realized is they became system integrators of their own. I got to bring data, catalog it, prepare it, surface it. So the belief of customers now is that, I need a place to go where basically it can easily bring in all the data, meta-data driven catalog, so I can use AI and ML to surface that data. So it's very easy at the preparation layer for my analysts to go around and play with data and then I can visualize anything. But it's all integrated out of the box, then each layer, each component being self-integrated, then it falls apart very quickly when you want to, to your question, at an enterprise level operationalize it. Large enterprises care about two things. Is it operationalizable? And is it scalable? That's where this could fall apart. And that's what our belief is. And that's where governance happens behind the scenes. You're not doing anything. Security of your data, governance of their data is driven through the catalog. You don't even feel it. It's there. >> I never liked the data lakes term. Dave Vellante knows I've always been kind of against, even from day one, 'cause data's more fluid, I call it a data ocean, but to your point, I want to get on that point because I think data lakes is one dimension, right? >> Yeah. >> And we talked about this at Informatica World, last year I think. And this year it's May 15th. >> Yes. >> I think your event is coming up, but you guys introduced meta-data intelligence. >> Yep. >> So there was, the old model was throw it centralized, do some data governance, data management, fence it out, call, make some queries, get some reports. I'm over simplifying but it was like, it was like a side function. You're getting at now is making that data valuable. >> Amit: Yep. >> So if it's in a lake or it's stored, you never know when the data's going to be relevant, so you have to have it addressable. Could you just talk about where this meta-data intelligence is going? Because you mentioned machine learning and AI. 'Cause this seems to be what everyone is talking about. In real time, how do I make the data really valuable when I need it? And what's the secret sauce that you guys have, specifically, to make that happen? >> So that, to contextualize that question, think about it. So if you. What you don't want to do is keep make everything manual. Our belief is that the intelligence around data has to be at the meta-data level, right? Across the enterprise, which is why, when we invested in the catalog, I used the word, "It's the google of data for the enterprise." No place in an enterprise you can go search for all your data, and given that the fast, rapid-changing sources of data, think about IoT, as you talked about, John. Or think about your customer data, for you and me may come from a new source tomorrow. Do you want the analyst to figure out where the data is coming from? Or the machine learning or AI to contextualize and tell you, you know what, I just discovered a great new source for where John is going to go shop. Do you want to put that as a part of analytics to give him an offer? That's where the organizing principle for data sits. The catalog and all the meta-data, which is where ML and AI will converge to give the analyst self-discovery of data sets, recommendations like in Amazon environment, recommendations like Facebook, find other people or other common data that's like a Facebook or a LinkedIn, that is where everything is going, and that's why we are putting all our efforts on AI. >> So you're saying, you want to abstract the way the complexity of where the data sits? So that the analyst or app can interface with that? >> That's exactly right. Because to me, those are the areas that are changing so rapidly, let that be. You can pick whatever data sets based on what you want, you can pick whichever app you want to use, wherever you want to go, or wherever your business wants to go. You can pick whichever analytical tool you like, but you want to be able to take all of those tools but be able to figure out what data is there, and that should change all the time. >> I'm trying to ask you a lot while you're here. What's going to be the theme this year at Informatica World? How do you take it to the next level? Can you just give us a teaser of what we might expect this year? 'Cause this seems to be the hottest trend. >> This is, so first, at Informatica World this year, we will be unveiling our whole new strategy, branding, and messaging, there's a whole amount of push on that one. But the two things that will be focused a lot on is, one is around that intelligent data platform. Which is basically what I'm talking about. The organizing principle of every enterprise for the next decade, and within that, where AI is going to play a meaningful role for people to spring forward, discover things, self-service, and be able to create sense from this mountains of data that's going to sit around us. But we won't even know what to do. >> All right, so what do you guys have in the product, just want to drill into this dynamic you just mentioned, which is new data sources. With IoT, this is going to completely make it more complex. You never know what data's going to be coming off the cars, the wearables, the smart cities. You have all these new killer use-cases that are going to be transformational. How do you guys handle that, and what's the secret sauce of? 'Cause that seems to be the big challenge, okay, I'm used to dealing with data, its structure, whether it's schemas, now we got unstructured. So okay, now I got new data coming in very fast, I don't even know when or where it's going to come in, so I have to be ready for these new data. What is the Informatica solution there? >> So in terms of taking data from any source, that's never been a challenge for us, because Informatica, one of the bread and butter for us is that we connect and bring data from any potential source on the planet, that's what we do. >> John: And you automate that? >> We automate that process, so any potential new source of data, whether it's IoT, unstructured, semi-structured, log, we connect to that. What I think the key is, where we are heavily invested, once you've brought all that. By the way, you can use Kafka Cues for that, you can use back-streaming, all of that stuff you could do. Question is, how do you make sense out of it? I can get all the data, dump it in a Kafka Cue, and then I take it to do some processing on Spark. But the intelligence is where all the Informatica secret sauce is, right? The meta-data, the transformations, that's what we are invested in, but in terms of connecting anything to everything? That we do for a living, we have done that for one quarter of a century, and we keep doing it. >> I mean, I love having a chat with you, Amit, you're a product guy, and we love product guys, 'cause they can give us a little teaser on the roadmap, but I got to ask you the question, with all this automation, you know, the big buzz out in the world is, "Oh machine learning and AI is replacing jobs." So where is the shift going to be, because you can almost connect the dots and say, "Okay, you're going to put some people out of work, "some developer, some automation, "maybe the systems management layer or wherever." Where are those jobs shifting to? Because you could almost say, "Okay, if you're going to abstract away and automate, "who loses their job?" Who gets shifted and what are those new opportunities, because you could almost say that if you automate in, that should create a new developer class. So one gets replaced, one gets created possibly. Your thoughts on this personnel transformation? >> Yeah, I think, I think what we see is that value creation will change. So the jobs will go to the new value. New areas where value is created. A great example of that is, look at developers today, right. Absolutely, I think they did a terrific job in making sure that the Hadoop ecosystem got legitimized, right? But in my opinion, where enterprise scalability comes, enterprises don't want lots of different things to be integrated and just plumbed together. They want things to work out of the box, which is why, you know, software works for them. But what happens is that they want that development community to go work on what I call value-added areas of the stack. So think about it, in connected car, they're working with lots of customers on the connected car issue, right? They don't want developers to work on the plumbing. They want us to kind of give that out of the box, because SLA is operational scale, and enterprise scalability matters, but in terms of the top-layer analytics, to make sure we can make sense out of it, that's what they're, that's where they want innovation. So what you will see is that, I don't think the jobs will go in vapor, but I do think the jobs will get migrated to a different part of the stack, which today it has not been, but that's, you know, we live in Silicon Valley, that's a natural evolution we see, so I think that will happen. In general in the larger industry, again I'd say, look, driverless cars, I don't think they've driven away jobs. What they've done is created a new class of people who work. So I do think that will be a big change. >> Yeah there's a fallacy there. I mean with the ATM argument was ATM's are going to replace tellers, yet more branches opened up. >> That's exactly it. >> So therefore creating new jobs. I want to get to the quick question, I know George has a question, but I want to get on the cost of ownership, because one of the things that's been criticized in some of these emerging areas, like Hadoop and Open Stack, for instance, just to pick two random examples. It's great, looks good, you know, all peace and love. An industry's being created, legitimized, but the cost of ownership has been critical to get that done, it's been expensive, talent, to find talent and deploying it was hard. We heard that on the Cube many times. How does the cost of ownership equation change? As you go after these more value, as developers and businesses go after these more value-creating activities in the Stack? >> See look, I always say, there is no free lunch. Nothing is free. And customers realize that, that open source, if you completely wanted to, to your point, as enterprises wanted to completely scale out and create an end-to-end operational infrastructure, open source ends up being pretty expensive. For all the reasons, right, because you throw in a lot of developers, and it's not necessarily scalable, so what we're seeing right now is that enterprises, as they have figured that this works for me, but when they want to go scale it out, they want to go back to what I call a software provider, who has the scale, who has the supportability, who also has the ability to react to changes and also for them to make sure that they get the comfort that it will work. So to me, that's where they find it cheaper. Just building it, experimenting with that, it's cheaper here, but scaling it out is cheaper with a software provider, so we see a lot of our customers when we start a little bit experimenting to developers, downloading something, works great, but would I really want to take it across Nordstrom or a JP Morgan or a Morgan Stanley. I need security, I need scalability, I need somebody to call to, at that point on those equations become very important. >> And that's where the out of box experience comes in, where you have the automation, that kind of. >> Exactly. >> Does that ease up some of the cost of ownership? >> Exactly, and the talent is a big issue, right? See we live in Silicon Valley, so we. By the way, Silicon Valley hiring talent is hard. Just think about it, if you go to Kansas City, hiring a scholar developer, that's a rare breed. So just, when I go around the globe and talk to customers, they don't see that talent at all that we here just somehow take for granted. They don't, so it's hard for them to kind of put their energy behind it. >> Let me ask. More on the meta-data layer. There's an analogy that's come up from the IIoT world where they're building these digital twins, and it's not just GE. IBM's talking about it, and actually, we've seen more and more vendors where the digital twin is this, it's a digital representation now of some physical object. But you could think of it as meta-data, you know, for a physical object, and it gets richer over time. So my question is, meta-data in the old data warehouse world, was we want one representation of the customer. But now it's, there's a customer representation for a prospect, and one for an account, and one for, you know, in warranty, and one for field service. Is that, how does that change what you offer? >> That's a very very good question. Because that's where the meta-data becomes so much more important because its manifestation is changing. I'll give you a great example, take Transamerica, Transamerica is a customer of ours leveraging big data at scale, and what they're doing is that, to your question, they have existing customers who have insurance through them. But they're looking for white space analysis, who could be potential opportunities? Two distinct ones, and within that, they're looking at relationships. I know you, John, you have Transamerica, could you be an influencer with me? Or within your family, extended family. I'm a friend, but what about a family member that you've declared out there on social media? So they are doing all that stuff in the context of a data lake. How are they doing it? So in that context, think about that complexity of the job, pumping data into a lake won't solve it for them, but that's a necessary first step. The second step is where all of that meta-data through ML and AI, starts giving them that relationship graph. To say, you know what, John in itself has this white space opportunity for you, but John is related to me in one way, him and me are connected on Facebook. John's related to you a little bit more differently, he has a stronger bond with you, and within his family, he has different strong bonds. So that's John's relationship graph. Leverage him, if he has been a good customer of yours. All of that stuff is now at the meta-data level, not just the monolithic meta-data, relationship graph. His relationship graph of what he has bought from you, so that you can just see that discovery becomes a very important element. Do you want to do that in different places? You want to do that in one place. I may be in a cloud environment, I may be on prem, so that's where when I say that meta-data becomes the organized principle, that's where it becomes real. >> Just a quick follow-up on that, then. It doesn't seem obvious that every end customer of yours, not the consumer but the buyer of the software, would have enough data to start building that graph. >> I don't think, to me, what happened was, the word big data, I thought got massively abused. A lot of Hadoop customers are not necessarily big data customers. I know a lot of banking customers, enterprise banking, whose data volumes will surprise you, but they're using Hadoop. What they want is intelligence. That's why I keep saying that the meta-data part, they are more interested in a deeper understanding of the data. A great example is, if John. I had a customer, who basically had a big bank. Rich net worth customer. In their will, the daughter was listed. When the daughter went to school, by the way, went to the bank branch in that city, she had no idea, she walked up, she basically wanted to open an account. Three more friends in the line. Manager comes out because at that point, the teller said, "This is somebody you should take special care of." Boom, she goes in a special cabin, the other friends are standing in a line. Think of the customer service perception, you just created a new millennia right? That's important. >> Well this brings up the interesting comment. The whole graph thing, we love, but this brings back the neural network trend. Which is a concept that's been around for a long long time, but now it's front and center. I remember talking to Diane Green who runs Google Cloud, she was saying that you couldn't hire neural network, they couldn't get jobs 15 years ago. Now you can't hire enough of them. So that brings up the ML conversation. So, I want to take that to a question and ask about the data lake, 'cause you guys have announced a new cloud data lake. >> Yes. >> So it sounds like, from what you're saying, is you're going beyond the data lake. So talk about what that is. Because data lake, people get, you throw stuff into a lake. And hopefully it doesn't become a swamp. How are you guys going beyond just the basic concept of a data lake with your new cloud data lake? >> Yeah, so, data lake. If you remember last year, actually at Strata San Jose we chatted, and we had announced the data lake because we realized customers, to your point John, as you said, were struggling on how to even build a data lake, and they were all over the place, and they were failing. And we announced the first data lake there, and then in Strata New York, basically we brought the meta-data ML part to the data lake. And then obviously right now we're taking it to the cloud, and what we see in the world of data lake is that customers ask for three things. First, they want the prebuilt integrated solution. Data can come in, but I want the intelligence of meta-data and I want data preparation baked in. I don't want to have three different tools that I will go around, so out of the box. But we also saw, as they become successful with our customers, they want to scale up, scale down. Cloud is just a great place to go. You can basically put a data lake out there, by the way in the context of data, a lot of new data sources are in the cloud, so it's easy for them to scale in and out in the cloud, experiment there and all that stuff. Also you know Amazon, we supported Amazon Kinesis, all of these new sources and technologies in the world of cloud are allowing experimentation in the data lake, so that allowed our customers to basically get ahead of the curve very quickly. So in some ways, cloud allowed customers to do things a lot faster, better, and cheaper. So that's what we basically put in the hands of our customers. Now that they are feeling comfortable, they can do a secured and governed data lake without feeling that it's still not self-served. They want to put it in the cloud and be a lot more faster and cheaper about it. >> John: And more analytics on it. >> More analytics. And now, because our ML, our AI, the meta-data part, connects cloud, ground, everything. So they have an organizing principle, whatever they put wherever, they can still get intelligence out of it. >> Amit, we got to break, but I want to get one final comment for you to kind of end the segment, and it's been fun watching you guys work over the past couple years. And I want to get your perspective because the product decisions always have kind of a time table to them, it's not like you made this up last night because it's trendy, but you guys have made some good product choices. It seems like the wind's at your back right now at Informatica. What, specifically, are bets that you guys made a couple years ago that are now bearing fruit? Can you just take a minute to end the segment, share some of those product bets. Because it's not always that obvious to make those product bets years earlier, seems to be a tail wind for you. You agree, and can you share some of those bets? >> I think you said it rightly, product bets are hard, right? Because you got to see three, four years ahead. The one big bet that we made is that we saw, as I said to you, the decoupling of the data layer. So we realized that, look, the app layer's getting fragmented. The cloud platforms are getting fragmented. Databases are getting fragmented. That that whole old monolithic architecture is getting fundamentally blown up, and the customers will be in a multi, multi, multi spread out hybrid world. Data is the organizing principle, so three years ago, we bet on the intelligent data platform. And we said that the intelligent data platform will be intelligent because of the meta-data driven layer, and at that point, AI was nowhere in sight. We put ML in that picture, and obviously, AI has moved, so the bet on the data platform. Second bet that, in that data platform, it'll all be AI, ML driven meta-data intelligence. And the third one is, we bet big on cloud. Big data we had already bet big on, by the way. >> John: You were already there. >> We knew the cloud. Big data will move to the cloud far more rapidly than the old technology moved to the cloud. So we saw that coming. We saw the (mumbles) wave coming. We worked so closely with AWS and the Azure team. With Google now, as well. So we saw three things, and that's what we bet. And you can see the rich offerings we have, the rich partnerships we have, and the rich customers that are live in those platforms. >> And the market's right on your doorstep. I mean, AI is hot, ML, you're seeing all this stuff converge with IoT. >> So those were, I think, forward-looking bets that paid out for us. (chuckles) And but there's so much more to do, and so much more upside for all of us right now. >> A lot more work to do. Amit, thank you for coming on, sharing your insight. Again, you guys got in good pole position in the market, and again it's right on your doorstep, so congratulations. This is the Cube, I'm John Furrier with George Gilbert. With more coverage in Silicon Valley for Big Data SV and Strata + Hadoop after this short break.