(bright music) >> Data by its very nature is distributed and siloed, but most data architectures today are highly centralized. Organizations are increasingly challenged to organize and manage data, and turn that data into insights. This idea of a single monolithic platform for data, it's giving way to new thinking. Where a decentralized approach, with open cloud native principles and federated governance, will become an underpinning of digital transformations. Hi everybody. This is Dave Volante. Welcome back to HPE Discover 2021, the virtual version. You're watching theCube's continuous coverage of the event and we're here with Matt Maccaux, who's a field CTO for Ezmeral Software at HPE. We're going to talk about HPE software strategy, and Ezmeral and specifically how to take AI analytics to scale and ensure the productivity of data teams. Matt, welcome to theCube. Good to see you. >> Good to see you again, Dave. Thanks for having me today. >> You're welcome. So talk a little bit about your role as a CTO. Where do you spend your time? >> I spend about half of my time talking to customers and partners about where they are on their digital transformation journeys and where they struggle with this sort of last phase where we start talking about bringing those cloud principles and practices into the data world. How do I take those data warehouses, those data lakes, those distributed data systems, into the enterprise and deploy them in a cloud-like manner? Then the other half of my time is working with our product teams to feed that information back, so that we can continually innovate to the next generation of our software platform. >> So when I remember, I've been following HP and HPE, for a long, long time, theCube has documented, we go back to sort of when the company was breaking in two parts, and at the time a lot of people were saying, "Oh, HP is getting rid of their software business, they're getting out of software." I said, "No, no, no, hold on. They're really focusing", and the whole focus around hybrid cloud and now as a service, you've really retooling that business and sharpened your focus. So tell us more about Ezmeral, it's a cool name, but what exactly is Ezmeral software? >> I get this question all the time. So what is Ezmeral? Ezmeral is a software platform for modern data and analytics workloads, using open source software components. We came from some inorganic growth. We acquired a company called Cytec, that brought us a zero trust approach to doing security with containers. We bought BlueData who came to us with an orchestrator before Kubernetes even existed in mainstream. They were orchestrating workloads using containers for some of these more difficult workloads. Clustered applications, distributed applications like Hadoop. Then finally we acquired MapR, which gave us this scale out distributed file system and additional analytical capabilities. What we've done is we've taken those components and we've also gone out into the marketplace to see what open source projects exist to allow us to bring those cloud principles and practices to these types of workloads, so that we can take things like Hadoop, and Spark, and Presto, and deploy and orchestrate them using open source Kubernetes. Leveraging GPU's, while providing that zero trust approach to security, that's what Ezmeral is all about is taking those cloud practices and principles, but without locking you in. Again, using those open source components where they exist, and then committing and contributing back to the opensource community where those projects don't exist. >> You know, it's interesting, thank you for that history, and when I go back, I have been there since the early days of Big Data and Hadoop and so forth and MapR always had the best product, but they couldn't get it out. Back then it was like kumbaya, open source, and they had this kind of proprietary system but it worked and that's why it was the best product. So at the same time they participated in open source projects because everybody did, that's where the innovation is going. So you're making that really hard to use stuff easier to use with Kubernetes orchestration, and then obviously, I'm presuming with the open source chops, sort of leaning into the big trends that you're seeing in the marketplace. So my question is, what are those big trends that you're seeing when you speak to technology executives which is a big part of what you do? >> So the trends are, I think, are a couplefold, and it's funny about Hadoop, but I think the final nails in the coffin have been hammered in with the Hadoop space now. So that leading trend, of where organizations are going, we're seeing organizations wanting to go cloud first. But they really struggle with these data-intensive workloads. Do I have to store my data in every cloud? Am I going to pay egress in every cloud? Well, what if my data scientists are most comfortable in AWS, but my data analysts are more comfortable in Azure, how do I provide that multi-cloud experience for these data workloads? That's the number one question I get asked, and that's probably the biggest struggle for these chief data officers, chief digital officers, is how do I allow that innovation but maintaining control over my data compliance especially when we talk international standards, like GDPR, to restrict access to data, the ability to be forgotten, in these multinational organizations how do I sort of square all of those components? Then how do I do that in a way that just doesn't lock me into another appliance or software vendor stack? I want to be able to work within the confines of the ecosystem, use the tools that are out there, but allow my organization to innovate in a very structured compliant way. >> I mean, I love this conversation and you just, to me, you hit on the key word, which is organization. I want to talk about what some of the barriers are. And again, you heard my wrap up front. I really do think that we've created, not only from a technology standpoint, and yes the tooling is important, but so is the organization, and as you said an analyst might want to work in one environment, a data scientist might want to work in another environment. The data may be very distributed. You might have situations where they're supporting the line of business. The line of business is trying to build new products, and if I have to go through this monolithic centralized organization, that's a barrier for me. And so we're seeing that change, that I kind of alluded to it up front, but what do you see as the big barriers that are blocking this vision from becoming a reality? >> It very much is organization, Dave. The technology's actually no longer the inhibitor here. We have enough technology, enough choices out there that technology is no longer the issue. It's the organization's willingness to embrace some of those technologies and put just the right level of control around accessing that data. Because if you don't allow your data scientists and data analysts to innovate, they're going to do one of two things. They're either going to leave, and then you have a huge problem keeping up with your competitors, or they're going to do it anyway. And they're going to do it in a way that probably doesn't comply with the organizational standards. So the more progressive enterprises that I speak with have realized that they need to allow these various analytical users to choose the tools they want, to self provision those as they need to and get access to data in a secure and compliant way. And that means we need to bring the cloud to generally where the data is because it's a heck of a lot easier than trying to bring the data where the cloud is, while conforming to those data principles, and that's HPE's strategy. You've heard it from our CEO for years now. Everything needs to be delivered as a service. It's Ezmeral Software that enables that capability, such as self-service and secure data provisioning, et cetera. >> Again, I love this conversation because if you go back to the early days of Hadoop, that was what was profound about a Hadoop. Bring five megabytes of code to a petabyte of data, and it didn't happen. We shoved it all into a data lake and it became a data swamp. And that's okay, it's a one dot oh, you know, maybe in data as is like data warehouses, data hubs, data lakes, maybe this is now a four dot oh, but we're getting there. But open source, one thing's for sure, it continues to gain momentum, it's where the innovation is. I wonder if you could comment on your thoughts on the role that open-source software plays for large enterprises, maybe some of the hurdles that are there, whether they're legal or licensing, or just fears, how important is open source software today? >> I think the cloud native developments, following the 12 factor applications, microservices based, paved the way over the last decade to make using open source technology tools and libraries mainstream. We have to tip our hats to Red Hat, right? For allowing organizations to embrace something so core as an operating system within the enterprise. But what everyone realized is that it's support that's what has to come with that. So we can allow our data scientists to use open source libraries, packages, and notebooks, but are we going to allow those to run in production? So if the answer is no, well? Then if we can't get support, we're not going to allow that. So where HPE Ezmeral is taking the lead here is, again, embracing those open source capabilities, but, if we deploy it, we're going to support it. Or we're going to work with the organization that has the committers to support it. You call HPE, the same phone number you've been calling for years for tier one 24 by seven support, and we will support your Kubernetes, your Spark your Presto, your Hadoop ecosystem of components. We're that throat to choke and we'll provide, all the way up to break/fix support, for some of these components and packages, giving these large enterprises the confidence to move forward with open source, but knowing that they have a trusted partner in which to do so. >> And that's why we've seen such success with say, for instance, managed services in the cloud, versus throwing out all the animals in the zoo and say, okay, figure it out yourself. But then, of course, what we saw, which was kind of ironic, was people finally said, "Hey, we can do this in the cloud more easily." So that's where you're seeing a lot of data land. However, the definition of cloud or the notion of cloud is changing. No longer is it just this remote set of services, "Somewhere out there in the cloud", some data center somewhere, no, it's moving to on-prem, on-prem is creating hybrid connections. You're seeing co-location facilities very proximate to the cloud. We're talking now about the edge, the near edge, and the far edge, deeply embedded. So that whole notion of cloud is changing. But I want to ask you, there's still a big push to cloud, everybody has a cloud first mantra, how do you see HPE competing in this new landscape? >> I think collaborating is probably a better word, although you could certainly argue if we're just leasing or renting hardware, then it would be competition, but I think again... The workload is going to flow to where the data exists. So if the data's being generated at the edge and being pumped into the cloud, then cloud is prod. That's the production system. If the data is generated via on-premises systems, then that's where it's going to be executed. That's production, and so HPE's approach is very much co-exist. It's a co-exist model of, if you need to do DevTests in the cloud and bring it back on-premises, fine, or vice versa. The key here is not locking our customers and our prospective clients into any sort of proprietary stack, as we were talking about earlier, giving people the flexibility to move those workloads to where the data exists, that is going to allow us to continue to get share of wallet, mind share, continue to deploy those workloads. And yes, there's going to competition that comes along. Do you run this on a GCP or do you run it on a GreenLake on-premises? Sure, we'll have those conversations, but again, if we're using open source software as the foundation for that, then actually where you run it is less relevant. >> So there's a lot of choices out there, when it comes to containers generally and Kubernetes specifically, and you may have answered this, you get the zero trust component, you've got the orchestrator, you've got the scale-out piece, but I'm interested in hearing in your words why an enterprise would or should consider Ezmeral instead of alternatives to Kubernetes solutions? >> It's a fair question, and it comes up in almost every conversation. "Oh, we already do Kubernetes, we have a Kubernetes standard", and that's largely true in most of the enterprises I speak to. They're using one of the many on-premises distributions to their cloud distributions, and they're all fine. They're all fine for what they were built for. Ezmeral was generally built for something a little different. Yes, everybody can run microservices based applications, DevOps based workloads, but where Ezmeral is different is for those data intensive, in clustered applications. Those sorts of applications require a certain degree of network awareness, persistent storage, et cetera, which requires either a significant amount of intelligence. Either you have to write in Golang, or you have to write your own operators, or Ezmeral can be that easy button. We deploy those stateful applications, because we bring a persistent storage layer, that came from MapR. We're really good at deploying those stateful clustered applications, and, in fact, we've opened sourced that as a project, KubeDirector, that came from BlueData, and we're really good at securing these, using SPIFFE and SPIRE, to ensure that there's that zero trust approach, that came from Scytale, and we've wrapped all of that in Kubernetes. So now you can take the most difficult, gnarly complex data intensive applications in your enterprise and deploy them using open source. And if that means we have to co-exist with an existing Kubernetes distribution, that's fine. That's actually the most common scenario that I walk into is, I start asking about, "What about these other applications you haven't done yet?" The answer is usually, "We haven't gotten to them yet", or "We're thinking about it", and that's when we talk about the capabilities of Ezmeral and I usually get the response, "Oh. A, we didn't know you existed and B well, let's talk about how exactly you do that." So again, it's more of a co-exist model rather than a compete with model, Dave. >> Well, that makes sense. I mean, I think again, a lot of people, they go, "Oh yeah, Kubernetes, no big deal. It's everywhere." But you're talking about a solution, kind of taking a platform approach with capabilities. You got to protect the data. A lot of times, these microservices aren't so micro and things are happening really fast. You've got to be secure. You got to be protected. And like you said, you've got a single phone number. You know, people say one throat to choke. Somebody in the media the other day said, "No, no. Single hand to shake." It's more of a partnership. I think that's apropos for HPE, Matt, with your heritage. >> That one's better. >> So, you know, thinking about this whole, we've gone through the pre big data days and the big data was all the hot buzzword. People don't maybe necessarily use that term anymore, although the data is bigger and getting bigger, which is kind of ironic. Where do you see this whole space going? We've talked about that sort of trend toward breaking down the silos, decentralization, maybe these hyper specialized roles that we've created, maybe getting more embedded or aligned with the line of business. How do you see... It feels like the next 10 years are going to be different than the last 10 years. How do you see it, Matt? >> I completely agree. I think we are entering this next era, and I don't know if it's well-defined. I don't know if I would go out on an edge to say exactly what the trend is going to be. But as you said earlier, data lakes really turned into data swamps. We ended up with lots of them in the enterprise, and enterprises had to allow that to happen. They had to let each business unit or each group of users collect the data that they needed and IT sort of had to deal with that down the road. I think that the more progressive organizations are leading the way. They are, again, taking those lessons from cloud and application developments, microservices, and they're allowing a freedom of choice. They're allowing data to move, to where those applications are, and I think this decentralized approach is really going to be king. You're going to see traditional software packages. You're going to see open source. You're going to see a mix of those, but what I think will probably be common throughout all of that is there's going to be this sense of automation, this sense that, we can't just build an algorithm once, release it and then wish it luck. That we've got to treat these analytics, and these data systems, as living things. That there's life cycles that we have to support. Which means we need to have DevOps for our data science. We need a CI/CD for our data analytics. We need to provide engineering at scale, like we do for software engineering. That's going to require automation, and an organizational thinking process, to allow that to actually occur. I think all of those things. The sort of people, process, products. It's all three of those things that are going to have to come into play, but stealing those best ideas from cloud and application developments, I think we're going to end up with probably something new over the next decade or so. >> Again, I'm loving this conversation, so I'm going to stick with it for a sec. It's hard to predict, but some takeaways that I have, Matt, from our conversation, I wonder if you could comment? I think the future is more open source. You mentioned automation, Devs are going to be key. I think governance as code, security designed in at the point of code creation, is going to be critical. It's no longer going be a bolt on. I don't think we're going to throw away the data warehouse or the data hubs or the data lakes. I think they become a node. I like this idea, I don't know if you know Zhamak Dehghani? but she has this idea of a global data mesh where these tools, lakes, whatever, they're a node on the mesh. They're discoverable. They're shareable. They're governed in a way. I think the mistake a lot of people made early on in the big data movement is, "Oh, we got data. We have to monetize our data." As opposed to thinking about what products can I build that are based on data that then can lead to monetization? I think the other thing I would say is the business has gotten way too technical. (Dave chuckles) It's alienated a lot of the business lines. I think we're seeing that change, and I think things like Ezmeral that simplify that, are critical. So I'll give you the final thoughts, based on my rant. >> No, your rant is spot on Dave. I think we are in agreement about a lot of things. Governance is absolutely key. If you don't know where your data is, what it's used for, and can apply policies to it. It doesn't matter what technology you throw at it, you're going to end up in the same state that you're essentially in today, with lots of swamps. I did like that concept of a node or a data mesh. It kind of goes back to the similar thing with a service mesh, or a set of APIs that you can use. I think we're going to have something similar with data. The trick is always, how heavy is it? How easy is it to move about? I think there's always going to be that latency issue, maybe not within the data center, but across the WAN. Latency is still going to be key, which means we need to have really good processes to be able to move data around. As you said, govern it. Determine who has access to what, when, and under what conditions, and then allow it to be free. Allow people to bring their choice of tools, provision them how they need to, while providing that audit, compliance and control. And then again, as you need to provision data across those nodes for those use cases, do so in a well measured and governed way. I think that's sort of where things are going. But we keep using that term governance, I think that's so key, and there's nothing better than using open source software because that provides traceability, auditability and this, frankly, openness that allows you to say, "I don't like where this project's going. I want to go in a different direction." And it gives those enterprises a control over these platforms that they've never had before. >> Matt, thanks so much for the discussion. I really enjoyed it. Awesome perspectives. >> Well thank you for having me, Dave. Excellent conversation as always. Thanks for having me again. >> You're very welcome. And thank you for watching everybody. This is theCube's continuous coverage of HPE Discover 2021. Of course, the virtual version. Next year, we're going to be back live. My name is Dave Volante. Keep it right there. (upbeat music)

>>data by its very nature is distributed and siloed. But most data architectures today are highly centralized. Organizations are increasingly challenged to organize and manage data and turn that data into insights this idea of a single monolithic platform for data, it's giving way to new thinking. We're a decentralized approach with open cloud native principles and Federated governance will become an underpinning underpinning of digital transformations. Hi everybody, this is Day Volonte. Welcome back to HP discover 2021 the virtual version. You're watching the cubes continuous coverage of the event and we're here with Matt Mako is the field C T O for Israel software at H P E. And we're gonna talk about HP software strategy and esmeralda and specifically how to take a I analytics to scale and ensure the productivity of data teams. Matt, welcome to the cube. Good to see you. >>Good to see you again. Dave thanks for having me today. >>You're welcome. So talk a little bit about your role as CTO. Where do you spend your time? >>Yeah. So I spend about half of my time talking to customers and partners about where they are on their digital transformation journeys and where they struggle with this sort of last phase where we start talking about bringing those cloud principles and practices into the data world. How do I take those data warehouses, those data lakes, those distributed data systems into the enterprise and deploy them in a cloud like manner. And then the other half of my time is working with our product teams to feed that information back so that we can continually innovate to the next generation of our software platform. >>So when I remember I've been following HP and HP for a long, long time, the cube is documented. We go back to sort of when the company was breaking in two parts and at the time a lot of people were saying, oh HP is getting rid of the software business to get out of software. I said no, no, no hold on, they're really focusing and and the whole focus around hybrid cloud and and now as a service and so you're really retooling that business and sharpen your focus. So so tell us more about asthma, it's cool name. But what exactly is as moral software, >>I get this question all the time. So what is Israel? Israel is a software platform for modern data and analytics workloads using open source software components. And we came from some inorganic growth. We acquired a company called citing that brought us a zero trust approach to doing security with containers. We bought blue data who came to us with an orchestrator before kubernetes even existed in mainstream. They were orchestrating workloads using containers for some of these more difficult workloads, clustered applications, distributed applications like Hadoop. And then finally we acquired Map are which gave us this scale out, distributed file system and additional analytical capabilities. And so what we've done is we've taken those components and we've also gone out into the marketplace to see what open source projects exist, to allow us to bring those club principles and practices to these types of workloads so that we can take things like Hadoop and spark and Presto and deploy and orchestrate them using open source kubernetes, leveraging Gpu s while providing that zero trust approaches security. That's what Israel is all about. Is taking those cloud practices and principles but without locking you in again using those open source components where they exist and then committing and contributing back to the open source community where those projects don't exist. >>You know, it's interesting. Thank you for that history. And when I go back, I always been there since the early days of big data and Hadoop and so forth. The map are always had the best product. But but they can't get back then. It was like Kumbaya open source and they had this kind of proprietary system, but it worked and that's why it was the best product. And so at the same time they participated in open source projects because everybody that that's where the innovation is going. So you're making that really hard to use stuff easier to use with kubernetes orchestration. And then obviously I'm presuming with the open source chops, sort of leaning into the big trends that you're seeing in the marketplace. So my question is, what are those big trends that you're seeing when you speak to technology executives, which is a big part of what you do? >>Yeah. So the trends I think are a couple of fold and it's funny about Duke, I think the final nails in the coffin have been hammered in with the Hadoop space now. And so that that leading trend of of where organizations are going. We're seeing organizations wanting to go cloud first, but they really struggle with these data intensive workloads. Do I have to store my data in every cloud? Am I going to pay egress in every cloud? Well, what if my data scientists are most comfortable in AWS? But my data analysts are more comfortable in Azure. How do I provide that multi cloud experience for these data workloads? That's the number one question I get asked. And that's the probably the biggest struggle for these Chief Data Officers. Chief Digital Officer XYZ. How do I allow that innovation but maintaining control over my data compliance especially, we talk international standards like G. D. P. R. To restrict access to data, the ability to be forgotten in these multinational organizations. How do I sort of square all of those components and then how do I do that in a way that just doesn't lock me into another appliance or software vendors stack? I want to be able to work within the confines of the ecosystem. Use the tools that are out there but allow my organization to innovate in a very structured, compliant way. >>I mean I love this conversation. And just to me you hit on the key word which is organization. I want to I want to talk about what some of the barriers are. And again, you heard my wrap up front. I I really do think that we've created not only from a technology standpoint and yes, the tooling is important, but so is the organization. And as you said, you know, an analyst might want to work in one environment, a data scientist might want to work in another environment. The data may be very distributed. They maybe you might have situations where they're supporting the line of business. The line of business is trying to build new products. And if I have to go through this, hi this monolithic centralized organization, that's a barrier uh for me. And so we're seeing that change that kind of alluded to it upfront. But what do you see as the big, you know, barriers that are blocking this vision from becoming a reality? >>It very much is organization dave it's the technology is actually no longer the inhibitor here. We have enough technology, enough choices out there. That technology is no longer the issue. It's the organization's willingness to embrace some of those technologies and put just the right level of control around accessing that data because if you don't allow your data scientists and data analysts to innovate, they're going to do one of two things, they're either going to leave and then you have a huge problem keeping up with your competitors or they're gonna do it anyway, and they're gonna do it in a way that probably doesn't comply with the organizational standards. So the more progressive enterprises that I speak with have realized that they need to allow these various analytical users to choose the tools, they want to self provision those as they need to and get access to data in a secure and compliant way. And that means we need to bring the cloud to generally where the data is because it's a heck of a lot easier than trying to bring the data where the cloud is while conforming to those data principles. And that's, that's Hve strategy, you've heard it from our CEO for years now, everything needs to be delivered as a service. It's essential software that enables that capability, such as self service and secure data provisioning, etcetera. >>Again, I love this conversation because if you go back to the early days of the Duke, that was what was profound about. Do bring bring five megabytes of code, do a petabyte of data and it didn't happen. We shoved it all into a data lake and it became a data swamp. And so it's okay, you know, and that's okay. It's a one dato maybe maybe in data is is like data warehouses, data hubs data lake. So maybe this is now a four dot Oh, but we're getting there. Uh, so an open but open source one thing's for sure. It continues to gain momentum. It's where the innovation is. I wonder if you could comment on your thoughts on the role that open source software plays for large enterprises. Maybe some of the hurdles that are there, whether they're legal or licensing or or or just fears. How important is open source software today? >>I think the cloud native development, you know, following the 12 factor applications microservices based, pave the way over the last decade to make using open source technology tools and libraries mainstream, we have to tip our hats to red hat right for allowing organizations to embrace something. So core is an operating system within the enterprise. But what everyone realizes that its support, that's what has to come with that. So we can allow our data scientists to use open source libraries, packages and notebooks. But are we going to allow those to run in production? And so if the answer is no, then that if we can't get support, we're not going to allow that. So where HP es Merrill is taking the lead here is again embracing those open source capabilities, but if we deploy it, we're going to support it or we're going to work with the organization that has the committees to support it. You call HPD the same phone number you've been calling for years for tier 1 24 by seven support and we will support your kubernetes, your spark your presto your Hadoop ecosystem of components were that throat to choke and we'll provide all the way up to break fix support for some of these components and packages giving these large enterprises the confidence to move forward with open source but knowing that they have a trusted partner in which to do so >>and that's why we've seen such success with, say, for instance, managed services in the cloud or versus throwing out all the animals in the zoo and say, okay, figure it out yourself. But of course what we saw, which was kind of ironic was we, we saw people finally said, hey, we can do this in the cloud more easily. So that's where you're seeing a lot of data. A land. However, the definition of cloud or the notion of cloud is changing no longer. Is it just this remote set of services somewhere out there? In the cloud? Some data center somewhere. No, it's, it's moving on. Prem on prem is creating hybrid connections you're seeing, you know, co location facility is very proximate to the cloud. We're talking now about the edge, the near edge and the far edge deeply embedded, you know? And so that whole notion of cloud is, is changing. But I want to ask you, there's still a big push to cloud, everybody is a cloud first mantra. How do you see HP competing in this new landscape? >>I I think collaborating is probably a better word, although you could certainly argue if we're just leasing or renting hardware than it would be competition. But I think again, the workload is going to flow to where the data exists. So if the data is being generated at the edge and being pumped into the cloud, then cloud is prod, that's the production system. If the data is generated, the on system on premises systems, then that's where it's going to be executed, that's production. And so HBs approach is very much coexist, coexist model of if you need to do deaf tests in the cloud and bring it back on premises, fine or vice versa. The key here is not locking our customers and our prospective clients into any sort of proprietary stack, as we were talking about earlier, giving people the flexibility to move those workloads to where the data exists. That is going to allow us to continue to get share of wallet. Mindshare, continue to deploy those workloads and yes, there's going to be competition that comes along. Do you run this on a G C P or do you run it on a green lake on premises? Sure. We'll have those conversations. But again, if we're using open source software as the foundation for that, then actually where you run it is less relevant. >>So a lot of, there's a lot of choices out there when it comes to containers generally and kubernetes specifically, uh, you may have answered this, you get zero trust component, you've got the orchestrator, you've got the, the scale out, you know, peace. But I'm interested in hearing in your words why an enterprise would or should consider s morale instead of alternatives to kubernetes solutions? >>It's a fair question. And it comes up in almost every conversation. We already do kubernetes, so we have a kubernetes standard and that's largely true. And most of the enterprises I speak to their using one of the many on premises distributions of the cloud distributions and they're all fine. They're all fine for what they were built for. Israel was generally built for something a little different. Yes, everybody can run microservices based applications, devoPS based workloads, but where is Meryl is different is for those data intensive and clustered applications. Those sort of applications require a certain degree of network awareness, persistent storage etcetera, which requires either a significant amount of intelligence. Either you have to write in go lang or you have to write your own operators or Israel can be that easy button. We deploy those state full applications because we bring a persistent storage later that came from that bar we're really good at deploying those stable clustered applications and in fact we've open sourced that as a project cube director that came from Blue data and we're really good at securing these using spiffy inspire to ensure that there is that zero trust approach that came from side tail and we've wrapped all of that in kubernetes so now you can take the most difficult, gnarly, complex data intensive applications in your enterprise and deploy them using open source and if that means we have to coexist with an existing kubernetes distribution, that's fine. That's actually the most common scenario that I walk into is I start asking about what about these other applications you haven't done yet? The answer is usually we haven't gotten to him yet or we're thinking about it and that's when we talk about the capabilities of s role and I usually get the response, oh, a we didn't know you existed and be, well, let's talk about how exactly you do that. So again, it's more of a coexist model rather than a compete with model. Dave >>Well, that makes sense. I mean, I think again, a lot of people think, oh yeah, Kubernetes, no big deal, it's everywhere. But you're talking about a solution, I'm kind of taking a platform approach with capabilities, you've got to protect the data. A lot of times these microservices aren't some micro uh and things are happening really fast, You've got to be secure, you've got to be protected. And like you said, you've got a single phone number, you know, people say one throat to choke, Somebody said the other day said no, no single hand to shake, it's more of a partnership and I think that's a proposed for HPV met with your >>hair better. >>So you know, thinking about this whole, you know, we've gone through the pre big data days and the big data was all, you know, the hot buzz where people don't maybe necessarily use that term anymore, although the data is bigger and getting bigger, which is kind of ironic. Um where do you see this whole space going? We've talked about that sort of trends are breaking down the silos, decentralization. Maybe these hyper specialized roles that we've created maybe getting more embedded are lined with the line of business. How do you see it feels like the last, the next 10 years are going to be different than the last 10 years. How do you see it matt? >>I completely agree. I think we are entering this next era and I don't know if it's well defined, I don't know if I would go out on an edge to say exactly what the trend is going to be. But as you said earlier, data lakes really turned into data swamps. We ended up with lots of them in the enterprise and enterprises had to allow that to happen. They had to let each business unit or each group of users collect the data that they needed and I. T. Sort of had to deal with that down the road. And so I think the more progressive organizations are leading the way they are again taking those lessons from cloud and application developments, microservices and they're allowing a freedom of choice there, allowing data to move to where those applications are. And I think this decentralized approach is really going to be king. And you're gonna see traditional software packages, you're gonna see open source, you're going to see a mix of those. But what I think we'll probably be common throughout all of that is there's going to be this sense of automation, this sense that we can't just build an algorithm once released and then wish it luck that we've got to treat these these analytics and these these data systems as living things that there's life cycles that we have to support, which means we need to have devops for our data science. We need a ci cd for our data analytics. We need to provide engineering at scale like we do for software engineering. That's going to require automation and an organizational thinking process to allow that to actually occur. And so I think all of those things that sort of people process product, but it's all three of those things are going to have to come into play. But stealing those best ideas from cloud and application development, I think we're going to end up with probably something new over the next decade or so >>again, I'm loving this conversation so I'm gonna stick with it for a second. I it's hard to predict, but I'll some takeaways that I have matt from our conversation. I wonder if you could, you could comment. I think, you know, the future is more open source. You mentioned automation deV's are going to be key. I think governance as code, security designed in at the point of code creation is going to be critical. It's not no longer to be a bolt on and I don't think we're gonna throw away the data warehouse or the data hubs or the data lakes. I think they become a node. I like this idea and you know, jim octagon. But she has this idea of a global data mesh where these tools lakes, whatever their their node on the mesh, they're discoverable. They're shareable. They're they're governed uh in a way and that really I think the mistake a lot of people made early on in the big data movement, Oh we have data, we have to monetize our data as opposed to thinking about what products that I can I build that are based on data that then I can, you know, can lead to monetization. And I think and I think the other thing I would say is the business has gotten way too technical. All right. It's an alienated a lot of the business lines and I think we're seeing that change. Um and I think, you know, things like Edinburgh that simplify that are critical. So I'll give you the final thoughts based on my rent. >>I know you're ready to spot on. Dave. I think we we were in agreement about a lot of things. Governance is absolutely key. If you don't know where your data is, what it's used for and can apply policies to it, it doesn't matter what technology throw at it, you're going to end up in the same state that you're essentially in today with lots of swamps. Uh I did like that concept of of a note or a data mesh. It kind of goes back to the similar thing with a service smashed or a set of a P I is that you can use. I think we're going to have something similar with data that the trick is always how heavy is it? How easy is it to move about? And so I think there's always gonna be that latency issue. Maybe not within the data center, but across the land, latency is still going to be key, which means we need to have really good processes to be able to move data around. As you said, government determine who has access to what, when and under what conditions and then allow it to be free, allow people to bring their choice of tools, provision them how they need to while providing that audit compliance and control. And then again, as as you need to provision data across those notes for those use cases do so in a well measured and govern way. I think that's sort of where things are going. But we keep using that term governance. I think that's so key. And there's nothing better than using open source software because that provides traceability, the audit ability and this frankly openness that allows you to say, I don't like where this project is going. I want to go in a different direction and it gives those enterprises that control over these platforms that they've never had before. >>Matt. Thanks so much for the discussion. I really enjoyed it. Awesome perspectives. >>Well, thank you for having me. Dave are excellent conversation as always. Uh, thanks for having me again. >>All right. You're very welcome. And thank you for watching everybody. This is the cubes continuous coverage of HP discover 2021 of course, the virtual version next year. We're gonna be back live. My name is Dave a lot. Keep it right there. >>Yeah.

>> Announcer: From theCUBE studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is a CUBE conversation. >> Hey, welcome back, you're ready. Jeff Frick here with theCUBE. We are still getting through the year of 2020. It's still the year of COVID and there's no end in sight I think until we get to a vaccine. That said, we're really excited to have one of our favorite guests. We haven't had him on for a while. I haven't talked to him for a long time. He used to I think have the record for the most CUBE appearances of probably any CUBE alumni. We're excited to have him joining us from his house in Palo Alto. Bill Schmarzo, you know him as the Dean of Big Data, he's got more titles. He's the chief innovation officer at Hitachi Vantara. He's also, we used to call him the Dean of Big Data, kind of for fun. Well, Bill goes out and writes a bunch of books. And now he teaches at the University of San Francisco, School of Management as an executive fellow. He's an honorary professor at NUI Galway. I think he's just, he likes to go that side of the pond and a many time author now, go check him out. His author profile on Amazon, the "Big Data MBA," "The Art of Thinking Like A Data Scientist" and another Big Data, kind of a workbook. Bill, great to see you. >> Thanks, Jeff, you know, I miss my time on theCUBE. These conversations have always been great. We've always kind of poked around the edges of things. A lot of our conversations have always been I thought, very leading edge and the title Dean of Big Data is courtesy of theCUBE. You guys were the first ones to give me that name out of one of the very first Strata Conferences where you dubbed me the Dean of Big Data, because I taught a class there called the Big Data MBA and look what's happened since then. >> I love it. >> It's all on you guys. >> I love it, and we've outlasted Strata, Strata doesn't exist as a conference anymore. So, you know, part of that I think is because Big Data is now everywhere, right? It's not the standalone thing. But there's a topic, and I'm holding in my hands a paper that you worked on with a colleague, Dr. Sidaoui, talking about what is the value of data? What is the economic value of data? And this is a topic that's been thrown around quite a bit. I think you list a total of 28 reference sources in this document. So it's a well researched piece of material, but it's a really challenging problem. So before we kind of get into the details, you know, from your position, having done this for a long time, and I don't know what you're doing today, you used to travel every single week to go out and visit customers and actually do implementations and really help people think these through. When you think about the value, the economic value, how did you start to kind of frame that to make sense and make it kind of a manageable problem to attack? >> So, Jeff, the research project was eyeopening for me. And one of the advantages of being a professor is, you have access to all these very smart, very motivated, very free research sources. And one of the problems that I've wrestled with as long as I've been in this industry is, how do you figure out what is data worth? And so what I did is I took these research students and I stick them on this problem. I said, "I want you to do some research. Let me understand what is the value of data?" I've seen all these different papers and analysts and consulting firms talk about it, but nobody's really got this thing clicked. And so we launched this research project at USF, professor Mouwafac Sidaoui and I together, and we were bumping along the same old path that everyone else got, which was inched on, how do we get data on our balance sheet? That was always the motivation, because as a company we're worth so much more because our data is so valuable, and how do I get it on the balance sheet? So we're headed down that path and trying to figure out how do you get it on the balance sheet? And then one of my research students, she comes up to me and she says, "Professor Schmarzo," she goes, "Data is kind of an unusual asset." I said, "Well, what do you mean?" She goes, "Well, you think about data as an asset. It never depletes, it never wears out. And the same dataset can be used across an unlimited number of use cases at a marginal cost equal to zero." And when she said that, it's like, "Holy crap." The light bulb went off. It's like, "Wait a second. I've been thinking about this entirely wrong for the last 30 some years of my life in this space. I've had the wrong frame. I keep thinking about this as an act, as an accounting conversation. An accounting determines valuation based on what somebody is willing to pay for." So if you go back to Adam Smith, 1776, "Wealth of Nations," he talks about valuation techniques. And one of the valuation techniques he talks about is valuation and exchange. That is the value of an asset is what someone's willing to pay you for it. So the value of this bottle of water is what someone's willing to pay you for it. So everybody fixates on this asset, valuation in exchange methodology. That's how you put it on balance sheet. That's how you run depreciation schedules, that dictates everything. But Adam Smith also talked about in that book, another valuation methodology, which is valuation in use, which is an economics conversation, not an accounting conversation. And when I realized that my frame was wrong, yeah, I had the right book. I had Adam Smith, I had "Wealth of Nations." I had all that good stuff, but I hadn't read the whole book. I had missed this whole concept about the economic value, where value is determined by not how much someone's willing to pay you for it, but the value you can drive by using it. So, Jeff, when that person made that comment, the entire research project, and I got to tell you, my entire life did a total 180, right? Just total of 180 degree change of how I was thinking about data as an asset. >> Right, well, Bill, it's funny though, that's kind of captured, I always think of kind of finance versus accounting, right? And then you're right on accounting. And we learn a lot of things in accounting. Basically we learn more that we don't know, but it's really hard to put it in an accounting framework, because as you said, it's not like a regular asset. You can use it a lot of times, you can use it across lots of use cases, it doesn't degradate over time. In fact, it used to be a liability. 'cause you had to buy all this hardware and software to maintain it. But if you look at the finance side, if you look at the pure play internet companies like Google, like Facebook, like Amazon, and you look at their valuation, right? We used to have this thing, we still have this thing called Goodwill, which was kind of this capture between what the market established the value of the company to be. But wasn't reflected when you summed up all the assets on the balance sheet and you had this leftover thing, you could just plug in goodwill. And I would hypothesize that for these big giant tech companies, the market has baked in the value of the data, has kind of put in that present value on that for a long period of time over multiple projects. And we see it captured probably in goodwill, versus being kind of called out as an individual balance sheet item. >> So I don't think it's, I don't know accounting. I'm not an accountant, thank God, right? And I know that goodwill is one of those things if I remember from my MBA program is something that when you buy a company and you look at the value you paid versus what it was worth, it stuck into this category called goodwill, because no one knew how to figure it out. So the company at book value was a billion dollars, but you paid five billion for it. Well, you're not an idiot, so that four billion extra you paid must be in goodwill and they'd stick it in goodwill. And I think there's actually a way that goodwill gets depreciated as well. So it could be that, but I'm totally away from the accounting framework. I think that's distracting, trying to work within the gap rules is more of an inhibitor. And we talk about the Googles of the world and the Facebooks of the world and the Netflix of the world and the Amazons and companies that are great at monetizing data. Well, they're great at monetizing it because they're not selling it, they're using it. Google is using their data to dominate search, right? Netflix is using it to be the leader in on-demand videos. And it's how they use all the data, how they use the insights about their customers, their products, and their operations to really drive new sources of value. So to me, it's this, when you start thinking about from an economics perspective, for example, why is the same car that I buy and an Uber driver buys, why is that car more valuable to an Uber driver than it is to me? Well, the bottom line is, Uber drivers are going to use that car to generate value, right? That $40,000, that car they bought is worth a lot more, because they're going to use that to generate value. For me it sits in the driveway and the birds poop on it. So, right, so it's this value in use concept. And when organizations can make that, by the way, most organizations really struggle with this. They struggle with this value in use concept. They want to, when you talk to them about data monetization and say, "Well, I'm thinking about the chief data officer, try not to trying to sell data, knocking on doors, shaking their tin cup, saying, 'Buy my data.'" No, no one wants your data. Your data is more valuable for how you use it to drive your operations then it's a sell to somebody else. >> Right, right. Well, on of the other things that's really important from an economics concept is scarcity, right? And a whole lot of economics is driven around scarcity. And how do you price for scarcity so that the market evens out and the price matches up to the supply? What's interesting about the data concept is, there is no scarcity anymore. And you know, you've outlined and everyone has giant numbers going up into the right, in terms of the quantity of the data and how much data there is and is going to be. But what you point out very eloquently in this paper is the scarcity is around the resources to actually do the work on the data to get the value out of the data. And I think there's just this interesting step function between just raw data, which has really no value in and of itself, right? Until you start to apply some concepts to it, you start to analyze it. And most importantly, that you have some context by which you're doing all this analysis to then drive that value. And I thought it was really an interesting part of this paper, which is get beyond the arguing that we're kind of discussing here and get into some specifics where you can measure value around a specific business objective. And not only that, but then now the investment of the resources on top of the data to be able to extract the value to then drive your business process for it. So it's a really different way to think about scarcity, not on the data per se, but on the ability to do something with it. >> You're spot on, Jeff, because organizations don't fail because of a lack of use cases. They fail because they have too many. So how do you prioritize? Now that scarcity is not an issue on the data side, but it is this issue on the people resources side, you don't have unlimited data scientists, right? So how do you prioritize and focus on those opportunities that are most important? I'll tell you, that's not a data science conversation, that's a business conversation, right? And figuring out how you align organizations to identify and focus on those use cases that are most important. Like in the paper we go through several different use cases using Chipotle as an example. The reason why I picked Chipotle is because, well, I like Chipotle. So I could go there and I could write it off as research. But there's a, think about the number of use cases where a company like Chipotle or any other company can leverage your data to drive their key business initiatives and their key operational use cases. It's almost unbounded, which by the way, is a huge challenge. In fact, I think part of the problem we see with a lot of organizations is because they do such a poor job of prioritizing and focusing, they try to solve the entire problem with one big fell swoop, right? It's slightly the old ERP big bang projects. Well, I'm just going to spend $20 million to buy this analytic capability from company X and I'm going to install it and then magic is going to happen. And then magic is going to happen, right? And then magic is going to happen, right? And magic never happens. We get crickets instead, because the biggest challenge isn't around how do I leverage the data, it's about where do I start? What problems do I go after? And how do I make sure the organization is bought in to basically use case by use case, build out your data and analytics architecture and capabilities. >> Yeah, and you start backwards from really specific business objectives in the use cases that you outline here, right? I want to increase my average ticket by X. I want to increase my frequency of visits by X. I want to increase the amount of items per order from X to 1.2 X, or 1.3 X. So from there you get a nice kind of big revenue hit that you can plan around and then work backwards into the amount of effort that it takes and then you can come up, "Is this a good investment or not?" So it's a really different way to get back to the value of the data. And more importantly, the analytics and the work to actually call out the information. >> The technologies, the data and analytic technologies available to us. The very composable nature of these allow us to take this use case by use case approach. I can build out my data lake one use case at a time. I don't need to stuff 25 data sources into my data lake and hope there's someone more valuable. I can use the first use case to say, "Oh, I need these three data sources to solve that use case. I'm going to put those three data sources in the data lake. I'm going to go through the entire curation process of making sure the data has been transformed and cleansed and aligned and enriched and met of, all the other governance, all that kind of stuff this goes on. But I'm going to do that use case by use case, 'cause a use case can tell me which data sources are most important for that given situation. And I can build up my data lake and I can build up my analytics then one use case at a time. And there is a huge impact then, huge impact when I build out use case by use case. That does not happen. Let me throw something that's not really covered in the paper, but it is very much covered in my new book that I'm working on, which is, in knowledge-based industries, the economies of learning are more powerful than the economies of scale. Now think about that for a second. >> Say that again, say that again. >> Yeah, the economies of learning are more powerful than the economies of scale. And what that means is what I learned on the first use case that I build out, I can apply that learning to the second use case, to the third use case, to the fourth use case. So when I put my data into my data lake for my first use case, and the paper covers this, well, once it's in my data lake, the cost of reusing that data in a second, third and fourth use cases is basically, you know marginal cost is zero. So I get this ability to learn about what data sets are most important and to reapply that across the organization. So this learning concept, I learn use case by use case, I don't have to do a big economies of scale approach and start with 25 datasets of which only three or four might be useful. But I'm incurring the overhead for all those other non-important data sets because I didn't take the time to go through and figure out what are my most important use cases and what data do I need to support those use cases. >> I mean, should people even think of the data per se or should they really readjust their thinking around the application of the data? Because the data in and of itself means nothing, right? 55, is that fast or slow? Is that old or young? Well, it depends on a whole lot of things. Am I walking or am I in a brand new Corvette? So it just, it's funny to me that the data in and of itself really doesn't have any value and doesn't really provide any direction into a decision or a higher order, predictive analytics until you start to manipulate the data. So is it even the wrong discussion? Is data the right discussion? Or should we really be talking about the capabilities to do stuff within and really get people focused on that? >> So Jeff, there's so many points to hit on there. So the application of data is what's the value, and the queue of you guys used to be famous for saying, "Separating noise from the signal." >> Signal from the noise. Signal from a noise, right. Well, how do you know in your dataset what's signal and what's noise? Well, the use case will tell you. If you don't know the use case and you have no way of figuring out what's important. One of the things I use, I still rail against, and it happens still. Somebody will walk up my data science team and say, "Here's some data, tell me what's interesting in it." Well, how do you separate signal from noise if I don't know the use case? So I think you're spot on, Jeff. The way to think about this is, don't become data-driven, become value-driven and value is driven from the use case or the application or the use of the data to solve that particular use case. So organizations that get fixated on being data-driven, I hate the term data-driven. It's like as if there's some sort of frigging magic from having data. No, data has no value. It's how you use it to derive customer product and operational insights that drive value,. >> Right, so there's an interesting step function, and we talk about it all the time. You're out in the weeds, working with Chipotle lately, and increase their average ticket by 1.2 X. We talk more here, kind of conceptually. And one of the great kind of conceptual holy grails within a data-driven economy is kind of working up this step function. And you've talked about it here. It's from descriptive, to diagnostic, to predictive. And then the Holy grail prescriptive, we're way ahead of the curve. This comes into tons of stuff around unscheduled maintenance. And you know, there's a lot of specific applications, but do you think we spend too much time kind of shooting for the fourth order of greatness impact, instead of kind of focusing on the small wins? >> Well, you certainly have to build your way there. I don't think you can get to prescriptive without doing predictive, and you can't do predictive without doing descriptive and such. But let me throw a really one at you, Jeff, I think there's even one beyond prescriptive. One we're talking more and more about, autonomous, a ton of analytics, right? And one of the things that paper talked about that didn't click with me at the time was this idea of orphaned analytics. You and I kind of talked about this before the call here. And one thing we noticed in the research was that a lot of these very mature organizations who had advanced from the retrospective analytics of BI to the descriptive, to the predicted, to the prescriptive, they were building one off analytics to solve a problem and getting value from it, but never reusing this analytics over and over again. They were done one off and then they were thrown away and these organizations were so good at data science and analytics, that it was easier for them to just build from scratch than to try to dig around and try to find something that was never actually ever built to be reused. And so I have this whole idea of orphaned analytics, right? It didn't really occur to me. It didn't make any sense into me until I read this quote from Elon Musk, and Elon Musk made this statement. He says, " I believe that when you buy a Tesla, you're buying an asset that appreciates in value, not depreciates through usage." I was thinking, "Wait a second, what does that mean?" He didn't actually say it, "Through usage." He said, "He believes you're buying an asset that appreciates not depreciates in value." And of course the first response I had was, "Oh, it's like a 1964 and a half Mustang. It's rare, so everybody is going to want these things. So buy one, stick it in your garage. And 20 years later, you're bringing it out and it's worth more money." No, no, there's 600,000 of these things roaming around the streets, they're not rare. What he meant is that he is building an autonomous asset. That the more that it's used, the more valuable it's getting, the more reliable, the more efficient, the more predictive, the more safe this asset's getting. So there is this level beyond prescriptive where we can think about, "How do we leverage artificial intelligence, reinforcement, learning, deep learning, to build these assets that the more that they are used, the smarter they get." That's beyond prescriptive. That's an environment where these things are learning. In many cases, they're learning with minimal or no human intervention. That's the real aha moment. That's what I miss with orphaned analytics and why it's important to build analytics that can be reused over and over again. Because every time you use these analytics in a different use case, they get smarter, they get more valuable, they get more predictive. To me that's the aha moment that blew my mind. I realized I had missed that in the paper entirely. And it took me basically two years later to realize, dough, I missed the most important part of the paper. >> Right, well, it's an interesting take really on why the valuation I would argue is reflected in Tesla, which is a function of the data. And there's a phenomenal video if you've never seen it, where they have autonomous vehicle day, it might be a year or so old. And he's got his number one engineer from, I think the Microprocessor Group, The Computer Vision Group, as well as the autonomous driving group. And there's a couple of really great concepts I want to follow up on what you said. One is that they have this thing called The Fleet. To your point, there's hundreds of thousands of these things, if they haven't hit a million, that are calling home reporting home every day as to exactly how everyone took the Northbound 101 on-ramp off of University Avenue. How fast did they go? What line did they take? What G-forces did they take? And every one of those cars feeds into the system, so that when they do the autonomous update, not only are they using all their regular things that they would use to map out that 101 Northbound entry, but they've got all the data from all the cars that have been doing it. And you know, when that other car, the autonomous car couple years ago hit the pedestrian, I think in Phoenix, which is not good, sad, killed a person, dark tough situation. But you know, we are doing an autonomous vehicle show and the guy who made a really interesting point, right? That when something like that happens, typically if I was in a car wreck or you're in a car wreck, hopefully not, I learned the person that we hit learns and maybe a couple of witnesses learn, maybe the inspector. >> But nobody else learns. >> But nobody else learns. But now with the autonomy, every single person can learn from every single experience with every vehicle contributing data within that fleet. To your point, it's just an order of magnitude, different way to think about things. >> Think about a 1% improvement compounded 365 times, equals I think 38 X improvement. The power of 1% improvements over these 600,000 plus cars that are learning. By the way, even when the autonomous FSD, the full self-driving mode module isn't turned on, even when it's not turned on, it runs in shadow mode. So it's learning from the human drivers, the human overlords, it's constantly learning. And by the way, not only they're collecting all this data, I did a little research, I pulled out some of their job search ads and they've built a giant simulator, right? And they're there basically every night, simulating billions and billions of more driven miles because of the simulator. They are building, he's going to have a simulator, not only for driving, but think about all the data he's capturing as these cars are riding down the road. By the way, they don't use Lidar, they use video, right? So he's driving by malls. He knows how many cars are in the mall. He's driving down roads, he knows how old the cars are and which ones should be replaced. I mean, he has this, he's sitting on this incredible wealth of data. If anybody could simulate what's going on in the world and figure out how to get out of this COVID problem, it's probably Elon Musk and the data he's captured, be courtesy of all those cars. >> Yeah, yeah, it's really interesting, and we're seeing it now. There's a new autonomous drone out, the Skydio, and they just announced their commercial product. And again, it completely changes the way you think about how you use that tool, because you've just eliminated the complexity of driving. I don't want to drive that, I want to tell it what to do. And so you're saying, this whole application of air force and companies around things like measuring piles of coal and measuring these huge assets that are volume metric measured, that these things can go and map out and farming, et cetera, et cetera. So the autonomy piece, that's really insightful. I want to shift gears a little bit, Bill, and talk about, you had some theories in here about thinking of data as an asset, data as a currency, data as monetization. I mean, how should people think of it? 'Cause I don't think currency is very good. It's really not kind of an exchange of value that we're doing this kind of classic asset. I think the data as oil is horrible, right? To your point, it doesn't get burned up once and can't be used again. It can be used over and over and over. It's basically like feedstock for all kinds of stuff, but the feedstock never goes away. So again, or is it that even the right way to think about, do we really need to shift our conversation and get past the idea of data and get much more into the idea of information and actionable information and useful information that, oh, by the way, happens to be powered by data under the covers? >> Yeah, good question, Jeff. Data is an asset in the same way that a human is an asset. But just having humans in your company doesn't drive value, it's how you use those humans. And so it's really again the application of the data around the use cases. So I still think data is an asset, but I don't want to, I'm not fixated on, put it on my balance sheet. That nice talk about put it on a balance sheet, I immediately put the blinders on. It inhibits what I can do. I want to think about this as an asset that I can use to drive value, value to my customers. So I'm trying to learn more about my customer's tendencies and propensities and interests and passions, and try to learn the same thing about my car's behaviors and tendencies and my operations have tendencies. And so I do think data is an asset, but it's a latent asset in the sense that it has potential value, but it actually has no value per se, inputting it into a balance sheet. So I think it's an asset. I worry about the accounting concept medially hijacking what we can do with it. To me the value of data becomes and how it interacts with, maybe with other assets. So maybe data itself is not so much an asset as it's fuel for driving the value of assets. So, you know, it fuels my use cases. It fuels my ability to retain and get more out of my customers. It fuels ability to predict what my products are going to break down and even have products who self-monitor, self-diagnosis and self-heal. So, data is an asset, but it's only a latent asset in the sense that it sits there and it doesn't have any value until you actually put something to it and shock it into action. >> So let's shift gears a little bit and start talking about the data and talk about the human factors. 'Cause you said, one of the challenges is people trying to bite off more than they can chew. And we have the role of chief data officer now. And to your point, maybe that mucks things up more than it helps. But in all the customer cases that you've worked on, is there a consistent kind of pattern of behavior, personality, types of projects that enables some people to grab those resources to apply to their data to have successful projects, because to your point there's too much data and there's too many projects and you talk a lot about prioritization. But there's a lot of assumptions in the prioritization model that you can, that you know a whole lot of things, especially if you're comparing project A over in group A with project B, with group B and the two may not really know the economics across that. But from an individual person who sees the potential, what advice do you give them? What kind of characteristics do you see, either in the type of the project, the type of the boss, the type of the individual that really lends itself to a higher probability of a successful outcome? >> So first off you need to find somebody who has a vision for how they want to use the data, and not just collect it. But how they're going to try to change the fortunes of the organization. So it always takes a visionary, may not be the CEO, might be somebody who's a head of marketing or the head of logistics, or it could be a CIO, it could be a chief data officer as well. But you've got to find somebody who says, "We have this latent asset we could be doing more with, and we have a series of organizational problem challenges against which I could apply this asset. And I need to be the matchmaker that brings these together." Now the tool that I think is the most powerful tool in marrying the latent capabilities of data with all the revenue generating opportunities in the application side, because there's a countless number, the most important tool that I found doing that is design thinking. Now, the reason why I think design thinking is so important, because one of the things that design thinking does a great job is it gives everybody a voice in the process of identifying, validating, valuing, and prioritizing use cases you're going to go after. Let me say that again. The challenge organizations have is identifying, validating, valuing, and prioritizing the use cases they want to go after. Design thinking is a marvelous tool for driving organizational alignment around where we're going to start and what's going to be next and why we're going to start there and how we're going to bring everybody together. Big data and data science projects don't die because of technology failure. Most of them die because of passive aggressive behaviors in the organization that you didn't bring everybody into the process. Everybody's voice didn't get a chance to be heard. And that one person who's voice didn't get a chance to get heard, they're going to get you. They may own a certain piece of data. They may own something, but they're just waiting and lay, they're just laying there waiting for their chance to come up and snag it. So what you got to do is you got to proactively bring these people together. We call this, this is part of our value engineering process. We have a value engineering process around envisioning where we bring all these people together. We help them to understand how data in itself is a latent asset, but how it can be used from an economics perspective, drive all those value. We get them all fired up on how these can solve any one of these use cases. But you got to start with one, and you've got to embrace this idea that I can build out my data and analytic capabilities, one use case at a time. And the first use case I go after and solve, makes my second one easier, makes my third one easier, right? It has this ability that when you start going use case by use case two really magical things happen. Number one, your marginal cost flatten. That is because you're building out your data lake one use case at a time, and you're bringing all the important data lake, that data lake one use case at a time. At some point in time, you've got most of the important data you need, and the ability that you don't need to add another data source. You got what you need, so your marginal costs start to flatten. And by the way, if you build your analytics as composable, reusable, continuous learning analytic assets, not as orphaned analytics, pretty soon you have all the analytics you need as well. So your marginal cost flatten, but effect number two is that you've, because you've have the data and the analytics, I can accelerate time to value, and I can de-risked projects as I go use case by use case. And so then the biggest challenge becomes not in the data and the analytics, it's getting the all the business stakeholders to agree on, here's a roadmap we're going to go after. This one's first, and this one is going first because it helps to drive the value of the second and third one. And then this one drives this, and you create a whole roadmap of rippling through of how the data and analytics are driving this value to across all these use cases at a marginal cost approaching zero. >> So should we have chief design thinking officers instead of chief data officers that really actually move the data process along? I mean, I first heard about design thinking years ago, actually interviewing Dan Gordon from Gordon Biersch, and they were, he had just hired a couple of Stanford grads, I think is where they pioneered it, and they were doing some work about introducing, I think it was a a new apple-based alcoholic beverage, apple cider, and they talked a lot about it. And it's pretty interesting, but I mean, are you seeing design thinking proliferate into the organizations that you work with? Either formally as design thinking or as some derivation of it that pulls some of those attributes that you highlighted that are so key to success? >> So I think we're seeing the birth of this new role that's marrying capabilities of design thinking with the capabilities of data and analytics. And they're calling this dude or dudette the chief innovation officer. Surprise. >> Title for someone we know. >> And I got to tell a little story. So I have a very experienced design thinker on my team. All of our data science projects have a design thinker on them. Every one of our data science projects has a design thinker, because the nature of how you build and successfully execute a data science project, models almost exactly how design thinking works. I've written several papers on it, and it's a marvelous way. Design thinking and data science are different sides of the same coin. But my respect for data science or for design thinking took a major shot in the arm, major boost when my design thinking person on my team, whose name is John Morley introduced me to a senior data scientist at Google. And I was bottom coffee. I said, "No," this is back in, before I even joined Hitachi Vantara, and I said, "So tell me the secret to Google's data science success? You guys are marvelous, you're doing things that no one else was even contemplating, and what's your key to success?" And he giggles and laughs and he goes, "Design thinking." I go, "What the hell is that? Design thinking, I've never even heard of the stupid thing before." He goes, "I'd make a deal with you, Friday afternoon let's pop over to Stanford's B school and I'll teach you about design thinking." So I went with him on a Friday to the d.school, Design School over at Stanford and I was blown away, not just in how design thinking was used to ideate and bring and to explore. But I was blown away about how powerful that concept is when you marry it with data science. What is data science in its simplest sense? Data science is about identifying the variables and metrics that might be better predictors of performance. It's that might phrase that's the real key. And who are the people who have the best insights into what values or metrics or KPIs you might want to test? It ain't the data scientists, it's the subject matter experts on the business side. And when you use design thinking to bring this subject matter experts with the data scientists together, all kinds of magic stuff happens. It's unbelievable how well it works. And all of our projects leverage design thinking. Our whole value engineering process is built around marrying design thinking with data science, around this prioritization, around these concepts of, all ideas are worthy of consideration and all voices need to be heard. And the idea how you embrace ambiguity and diversity of perspectives to drive innovation, it's marvelous. But I feel like I'm a lone voice out in the wilderness, crying out, "Yeah, Tesla gets it, Google gets it, Apple gets it, Facebook gets it." But you know, most other organizations in the world, they don't think like that. They think design thinking is this Wufoo thing. Oh yeah, you're going to bring people together and sing Kumbaya. It's like, "No, I'm not singing Kumbaya. I'm picking their brains because they're going to help make their data science team much more effective and knowing what problems we're going to go after and how I'm going to measure success and progress. >> Maybe that's the next Dean for the next 10 years, the Dean of design thinking instead of data science, and who knew they're one and the same? Well, Bill, that's a super insightful, I mean, it's so, is validated and supported by the trends that we see all over the place, just in terms of democratization, right? Democratization of the tools, more people having access to data, more opinions, more perspective, more people that have the ability to manipulate the data and basically experiment, does drive better business outcomes. And it's so consistent. >> If I could add one thing, Jeff, I think that what's really powerful about design thinking is when I think about what's happening with artificial intelligence or AI, there's all these conversations about, "Oh, AI is going to wipe out all these jobs. Is going to take all these jobs away." And what we're actually finding is that if we think about machine learning, driven by AI and human empowerment, driven by design thinking, we're seeing the opportunity to exploit these economies of learning at the front lines where every customer engagement, every operational execution is an opportunity to gather not only more data, but to gather more learnings, to empower the humans at the front lines of the organization to constantly be seeking, to try different things, to explore and to learn from each of these engagements. I think it's, AI to me is incredibly powerful. And I think about it as a source of driving more learning, a continuous learning and continuously adapting an organization where it's not just the machines that are doing this, but it's the humans who've been empowered to do that. And my chapter nine in my new book, Jeff, is all about team empowerment, because nothing you do with AI is going to matter of squat if you don't have empowered teams who know how to take and leverage that continuous learning opportunity at the front lines of customer and operational engagement. >> Bill, I couldn't set a better, I think we'll leave it there. That's a great close, when is the next book coming out? >> So today I do my second to last final review. Then it goes back to the editor and he does a review and we start looking at formatting. So I think we're probably four to six weeks out. >> Okay, well, thank you so much, congratulations on all the success. I just love how the Dean is really the Dean now, teaching all over the world, sharing the knowledge and attacking some of these big problems. And like all great economics problems, often the answer is not economics at all. It's completely really twist the lens and don't think of it in that, all that construct. >> Exactly. >> All right, Bill. Thanks again and have a great week. >> Thanks, Jeff. >> All right. He's Bill Schmarzo, I'm Jeff Frick. You're watching theCUBE. Thanks for watching, we'll see you next time. (gentle music)

>> Live, from Barcelona, Spain, it's theCUBE, covering KubeCon and CloudNativeCon Europe, 2019. Brought to you by Red Hat, the Cloud Native Computing Foundation and ecosystem partners. >> Welcome back. This is theCUBE at KubeCon CloudNativeCon 2019 here in Barcelona, Spain. I'm Stu Miniman, my co-host is Corey Quinn and welcoming back to the program, friend of the program, Brian Gracely who is the Director of Product Strategy at Red Hat. Brian, great to see you again. >> I've been, I feel like I've been in the desert. It's three years, I'm finally back, it's good to be back on theCUBE. >> Yeah well, I feel like we've been traveling parallel paths a lot. TheCUBE goes to a lot of events. We do a lot of interviews but I think when you go to shows, you actually have more back-to-back meetings than we even do, so we feel you in the jet lag and a little bit of exhaustion. Thanks for making time. >> Yeah, it's great. I had dinner with you two weeks ago, I did a podcast with Corey a week ago, and now, due to the magic of the internet, we're all here together in one place. It's good. >> Absolutely. Well Brian, as we know at a show like this we all want to hold hands and sing Kubernetes Kumbaya. It's wonderful to see that all of the old fights of the past have all been solved by software in the cloud. >> They're all good, it's all good. Yeah, somebody said it's a cult. I think I heard Owen Rodgers said it's now officially a cult. Corey, you called it the Greek word for spending lots of money. >> Uh yeah, it was named after the Kubernetes, the Greek god of spending money on cloud services. >> So, Brian, you talk to a lot of customers here. As they look at this space, how do they look at it? There's still times that I hear them, "I'm using this technology and I'm using this technology, "and gosh darn it vendor, "you better get together and make this work." So, open-source, we'd love to say is the panacea, but maybe not yet. >> I don't think we hear that as much anymore because there is no more barrier to getting the technology. It's no longer I get this technology from vendor A and I wish somebody else would support the standard. It's like, I can get it if I want it. I think the conversations we typically have aren't about features anymore, they're simply, my business is driven by software, that's the way I interact with my customer, that's the way I collect data from my customers, whatever that is. I need to do that faster and I need to teach my people to do that stuff. So the technology becomes secondary. I have this saying and it frustrates people sometimes, but I'm like, there's not a CEO, a CIO, a CTO that you would talk to that wakes up and says, "I have a Kubernetes problem." They all go, "I have a, I have this business problem, "I have that problem, it happens to be software." Kubernetes is a detail. >> Yeah Brian, those are the same people 10 years ago had a convergent problem, I never ran across them. >> If you screw up a Kubernetes roll-out, then you have a Kubernetes problem. But it's entertaining though. I mean, you are the Director of Product Strategy, which is usually a very hard job with the notable exception of one very large cloud company, where that role is filled by a post-it note that says simply, yes. So as you talk to the community and you look at what's going on, how are you having these conversations inform what you're building in terms of Openshift? >> Yeah, I mean, strategy you can be one of two things. You can either be really good at listening, or you can have a great crystal ball. I think Red Hat has essentially said, we're not going to be in the crystal ball business. Our business model is there's a lot of options, we will go get actively involved with them, we will go scratch our knees and get scars and stuff. Our biggest thing is, I have to spend a lot of time talking to customers going, what do you want to do? Usually there's some menu that you can offer them right now and it's really a matter of, do you want it sort of half-baked? Are you willing to sort of go through the learning process? Do you need something that's a little more finalized? We can help you do that. And our big thing is, we want to put as many of those things kind of together in one stew, so that you're not having-- Not you Stu, but other stews, thinking about like, I don't want to really think about them, I just want it to be monitored, I want the network to just work, I want scalability built in. So for us it's not so much a matter of making big, strategic bets, it's a matter of going, are we listening enough and piecing things together so they go, yeah, it's pretty close and it's the right level of baked for what I want to do right now. >> Yeah, so Brian, an interesting thing there. There's still quite a bit of complexity in this ecosystem. Red Hat does a good job of giving adult supervision to the environment, but, you know, when I used to think when row came out, it was like, okay, great. Back in the day, I get a CD and I know I can run this. Today here, if I talk to every Kubernetes customer that I run across and say okay, tell me your stack and tell me what service measure you're using, tell me which one of these projects you're doing and how you put them together. There's a lot of variation, so how do you manage that, the scale and growth with the individual configurations that everybody still can do, even if they're starting to do public clouds and all those other things? >> So, it's always interesting to me. I watch the different Keynotes and people will talk about all the things in their stack and why they had problems and this, that, and the other, and I kind of look at it and I'm like, we've solved that problem for you. Our thing is always, and I don't mean that sort of boastfully, but like, we put things together in what we think are pretty good defaults. It's the one probably big difference between Openshift and a lot of these other ones that are here is that we've put all those things together as sort of what we think are pretty good defaults. We allow some flexibility. So, you don't like the monitoring, you don't like Prometheus plugin splunk, that's fine. But we don't make you stand on your head. So for us, a lot of these problems that, our customers don't go, well, we can't figure out the stack, we can't do these things, they're kind of built in. And then their problem becomes okay, can I highly automate that? Did I try and make too many choices where you let me plug things in? And for us, what we've done, is I think if we went back a few years, people could say you guys are too modular, you're too plugable. We had to do that to kind of adapt to the market. Now we've sort of learned over time, you want to be immutable, you want to give them a little less choice. You want to really, no, if you're going to deploy an AWS, you got to know AWS really well. And that's, you know, not to make this a commercial, but that's basically what Openshift four became, was much more opinions about what we think are best practices based on about a thousand customers having done this. So we don't run into as many of pick your stack things, we run into that next level thing. Are we automating it enough? Do we scale it? How do we do statefulness? Stuff like that. >> Yeah, I'm curious in the Keynote this morning they called, you know, Kubernetes is a platform of platforms. Did that messaging resonate with you and your customers? >> Yeah, I think so, I mean, Kubernetes by itself doesn't really do anything, you need all this other stuff. So when I hear people say we deployed Kubernetes, I'm like, no you don't. You know, it's the engine of what you do, but you do a bunch of other stuff. So yeah, we like to think of it as like, we're platform builders, you should be a platform consumer, just like you're a consumer of Salesforce. They're a platform, you consume that. >> Yeah, one of the points made in the Keynote was how one provider, I believe it was IBM, please yell at me if I got that one wrong, talks about using Kubernetes to deploy Kubernetes. Which on the one hand, is super cool and a testament to the flexibility of how this is really working. On the other, it's-- and thus the serpent devours itself, and it becomes a very strange question of, okay, then we're starting to see some weird things. Where do we start, where do we look? Indeed.com for a better job. And it's one of those problems that at some point you just can't manage a head around complexities inside of complexities, but we've been dealing with that for 40 years. >> Yeah, Kubernetes managing Kubernetes is kind of one of those weird words like serverless, you're like what does that mean? I don't, it doesn't seem to, I don't think you mean what you want it to mean. The simplest way we explain that stuff, so... A couple of years ago there was a guy named Brandon Philips who had started a company called CoreOS. He stood up at Kube-- >> I believe you'll find it's pronounce CoreOS, but please, continue. >> CoreOS, exactly. Um, he stood up in the Seattle one when there was a thousand people at this event or 700, and he said, "I've created this pattern, "or we think there's a pattern that's going to be useful." The simplest way to think of it is, there's stuff that you just want to run, and I want essentially something monitoring it and keep it in a loop, if you will. Kubernetes just has that built in. I mean, it's kind of built in to the concept because originally Google said, "I can't manage it all myself." So that thing that he originally came up with or codified became what's now called operators. Operators is that thing now that's like okay, I have a stateful application. It needs to do certain things all the time, that's the best practice. Why don't we just build that around it? And so I think you heard in a lot of the Keynotes, if you're going to run storage, run it as an operator. If you're going to run a database, run it as an operator. It sounds like inception, Kubernetes running-- It's really just, it's a health loop that's going on all the time with a little bit of smarts that say hey, if you fail, fail this way. I always use the example like if I go to Amazon and get RDS, I don't get a DVA, there's no guy that shows up and says, "Hey, I'm your DVA." You just get some software that runs it for you. That's all this stuff is, it just never existed in Kubernetes before. Kubernetes has now matured enough to where they go, oh, I can play in that world, I can make that part of what I do. So it's less scary, it sounds sort of weird, inception-y. It's really just kind of what you've already gotten out of the public cloud now brought to wherever you want it. >> Well, one of the concerns that I'm starting to see as well is there's a level of hype around this. We've had a lot of conversations around Kubernetes today and yesterday, to the point where you can almost call this Kubernetes and friends instead of CloudNativeCon. And everyone has described it slightly differently. You see people describing it as systemd, as a kernel, sometimes as the way and the light, and someone on stage yesterday said that we all are familiar with the value that Kubernetes has brought to our jobs and our lives, is I think was the follow-up to that, which is a little strange. And I got to thinking about that. I don't deny that it has brought value, but what's interesting to me about this is I don't think I've heard two people define its value in the same terminology at all, and we've had kind of a lot of these conversations. >> So obviously not a cult because they would all be on message if it was a cult. >> Yeah, yeah yeah yeah. >> It's a cult with very crappy brand control, maybe. We don't know. >> I always just explain it that like, you know, if I went back 10 years or something, people... Any enterprise said hey, I would love to run like Google or like Amazon. Apparently for every one admin, I can manage a thousand servers and in their own data centers it's like well, I have one guy and he manages five, so I have cloud envy. >> We tried to add a sixth and he was crushed to death. Turns out those racks have size and weight limits. >> That's right, that's right. And so, people, they wanted this thing, they would've paid an arm and a leg for it. You move forward five years from that and it's like oh, Google just gave you their software, it's now available for free. Now what are you going to do with it? I gave you a bunch of power. So yeah, depending on how much you want to drink the Kool-Aid you're like, this is awesome, but at the end of the day you're just like, I just want the stuff that is available to, that's freely, publicly available, but for whatever reason, I can't be all in on one cloud, or I can't be all in on a public cloud, which, you believe in that there's tons of economic value about it, there's just some companies that can't do that. >> And I fully accept that. My argument has always been that it is, I think it's a poor best practice. When you have a constraint that forces you to be in multiple cloud providers, yes, do it! That makes absolute perfect sense. >> Right, if it makes sense, do it. And that's kind of what we've always said look, we're agnostic to that. If you want to run it, if you want to run it in a disconnected mode on a cruise ship, great, if it makes sense for you. If you need to run, you know, like... The other thing that we see-- >> That cruise ship becomes a container ship. >> Becomes a container ship. I had an interesting conversation with the bank last night. I had dinner with the bank. We were talking, they said, look, I run some stuff locally where I'm at, 'cause I have to, and then, we put a ton of stuff in AWS. He told me this story about a batch processing job that cost him like $4 or $5 million today. He does a variant of it in Lambda, and it cost him like $50 a month. So we had this conversation and it's going like, I love AWS, I want to be all in at AWS. And he said, here's my problem. I wake up every morning worried that I'm going to open the newspaper and Amazon, not AWS, Amazon is going to have moved closer into the banking industry than they are today. And so I have to have this kind of backup plan if you will. Backup's the wrong word, but sort of contingency plan of if they stop being my technology partner and they start becoming my competitor, which, there's arguments-- >> And for most of us I'd say that's not a matter of if, but when. >> Right, right. And some people live with it great. Like, Netflix lives with it, right? Others struggle. That guy's not doing multi-cloud in the future, he's just going, I would like to have the technology that allows me if that comes along. I'm not doing it to do it, I'd like the bag built in. >> So Brian, just want to shift a little bit off of kind of the mutli-cloud discussion. The thing that's interest me a lot, especially I've talked to a number of the Openshift customers, it is historically, infrastructure was the thing that slowed me down. We understand, oh, I want to modernize that. No, no wait. The back in thing or you know, provisioning, these kind of things take forever. The lever of this platform has been, I can move faster, I can really modernize my environment, and, whether that's in my data center or in one public cloud and a couple of others, it is that you know, great lever to help me be able to do that. Is that the right way to think about this? You've talked to a lot of customers. Is that a commonality between them? >> I think we see, I hate to give you a vendor answer, but we tend to see different entry points. So for the infrastructure people, I mean the infrastructure people realize in some cases they're slow, and a lot of cases the ones that are still slow, it's 'cause of some compliance thing. I can give you a VM in an hour, but I got to go through a process. They're the ones that are saying, look, my developers are putting stuff in containers or we're downloading, I just need to be able to support that. The developers obviously are the ones who are saying, look, business need, business problem, have budget to do something, That's usually the more important lever. Just faster infrastructure doesn't do a whole lot. But we find more and more where those two people have to be in the room. They're not making choices independently. But the ones that are successful, the ones that you hear case studies about, none of them are like, we're great at building containers. They're great at building software. Development drives it, infrastructure still tends to have a lot of the budget so they play a role in it, but they're not dictating where it goes or what it does. >> Yeah, any patterns you're seeing or things that customers can do to kind of move further along that spectrum? >> I think, I mean there's a couple of things, and whether you fit in this or not, number one, nobody has a container problem. Start with a business problem. That's always good for technology in general, but this isn't a refresh thing, this is some business problem. That business problem typically should be, I have to build software faster. We always say... I've seen enough of these go well and I've seen enough go poorly. There's, these events are great. They're great in the sense of people see that there's progress, there's innovation. They're also terrible because if you walk into this new, you feel like, man, everybody understands this, it must be pretty simple. And what'll happen is they start working on it and they realize, I don't know what I'm doing. Even if they're using Openshift and we made it easy, they don't know what they're doing. And then they go, I'm embarrassed to ask for help. Which is crazy because if you get into open source the community's all there to help. So it's always like, business problem, ask for help early and often, even if it embarrasses you. Don't go after low-hanging fruit, especially if you're trying to get further investment. Spinning up a bunch of web clusters or hello worlds doesn't, nobody cares anymore. Go after something big. It basically forces your organization to be all in. And then the other thing, and this is the thing that's never intuitive to IT teams, is you, at the point where you actually made something work, you have to look more like my organization than yours, which is basically you have to look like a software marketing company, because internally, you're trying to convince developers to come use your platform or to build faster or whatever, you actually have to have internal evangelist and for a lot of them, they're like, dude, marketing, eh, I don't want anything to do with that. But it's like, that's the way you're going to get people to come to your new way of doing things. >> Great points, Brian. I remember 15 years ago, it was the first time I was like wait, the CIO has a marketing person under him to help with some of those transformations? Some of the software roles to do. >> Yeah, it's the reason they all want to come and speak at Keynotes and they get at the end and they go, we're hiring. It's like, I got to make what I'm doing sound cool and attract 8,000 people to it. >> Well absolutely it's cool here. We really appreciate Brian, you sharing all the updates here. >> Great to see you guys again. It's good to be back. >> Definitely don't be a stranger. So for Corey Quinn, I'm Stu Miniman. Getting towards the end. Two days live, wall-to-wall coverage here at KubeCon, CloudNativeCon 2019. Thanks for watching theCUBE. (rhythmic music)

>> Live from Barcelona, Spain it's theCUBE, covering KubeCon, CloudNativeCon, Europe 2019. Brought to you by Red Hat, the Cloud Native Computing Foundation and Ecosystem Partners. >> Welcome back. This is theCUBE's coverage of KubeCon, CloudNativeCon 2019. I'm Stu Miniman, my co-host for two days wall-to-wall coverage is Corey Quinn. Happy to welcome back to the program first Ben Sigelman, who is the co-founder and CEO of LightStep. And welcome to the program a first time Morgan McLean, who's a product manager at Google Cloud Platform. Gentlemen, thanks so much for joining us. >> Thanks for having us. >> Yeah. >> All right so, this was a last minute ad for us because you guys had some interesting news in the keynote. I think the feedback everybody's heard is there's too many projects and everything's overlapping, and how do I make a decision, but interesting piece is OpenCensus, which Morgan was doing, and OpenTracing, which Ben and LightStep were doing are now moving together for OpenTelemetry if I got it right. >> Yup. >> So, is it just everybody's holding hands and singing Kumbaya around the Kubernetes campfire, or is there something more to this? >> Well I mean, it started when the CNCF locked us in a room and told us there were too many projects. (Stu and Ben laughing) Really wouldn't let us leave. No, to be fair they did actually take us to a room and really start the ball rolling, but conversations have picked up for the last few months and personally I'm just really excited that it's gone so well. Initially if you told me six or nine months ago that this would happen, I would've been, given just the way the projects were going, both were growing very quickly, I would've been a little skeptical. But seriously, this merger's gone beyond my wildest dreams. It's awesome, both to unite the communities, it's awesome to unite the projects together. >> What has the response been from the communities on this merger? >> Very positive. >> Yeah. >> Very positive. I mean OpenTracing and OpenCensus are both projects with healthy user bases that are growing quickly and all that, but the reason people adopt them is to future-proof their own software. Because they want to adopt something that's going to be here to stay. And by having these two things out in the world that are both successful, and were overlapping in terms of their goals, I think the presence of two projects was actually really problematic for people. So, the fact that they're merging is net positive, absolutely for the end user community, also for the vendor community, it's a similar, it's almost exactly the same parallel thought process. When we met, the CNCF did broker an in-person meeting where they gave us some space and we all got together and, I don't know how many people were there, like 20 or 30 people in that room. >> They did let us leave the room though, yesterday, yeah that was nice. >> They did let us leave the room, that's true. We were not locked in there, (Morgan laughing) but they asked us in the beginning, essentially they asked everyone to state what their goals were. And almost all of us really had the same goal, which is just to try and make it easy for end users to adopt a telemetry project that they can stick with for the long haul. And so when you think of it in that respect, the merger seems completely obvious. It is true that it doesn't happen very often, and we could speculate about why that is. But I think in this case it was enabled by the fact that we had pretty good social relationships with OpenCensus people. I think Twitter tends to amplify negativity in the world in general, as I'm sure people, not a controversial statement. >> News alert, wait, absolutely the negatives are, it's something in the algorithm I think. >> Yeah, yeah. >> Maybe they should fix that. >> Yeah, yeah (laughs) exactly. And it was funny, there was a lot of perceived animosity between OpenTracing and OpenCensus a year ago, nine months ago, but when you actually talk to the principals in the projects and even just the general purpose developers who are doing a huge amount of work for both projects, that wasn't a sentiment that was widely held or widely felt I think. So, it has been a very kind of happy, it's a huge relief frankly, this whole thing has been a huge relief for all of us I think. >> Yeah it feels like the general ask has always been that, for tracing that doesn't suck. And that tends to be a bit of a tall order. The way that they have seemed to have responded to it is a credit to the maturity of the community. And I think it also speaks to a growing realization that no one wants to have a monoculture of just one option, any color you want so long as it's black. (Ben laughing) Versus there's 500 different things you can pick that all stand in that same spot, and at that point analysis paralysis kicks in. So this feels like it's a net positive for, absolutely everyone involved. >> Definitely. Yeah, one of the anecdotes that Ben and I have shared throughout a lot of these interviews is there were a lot of projects that wanted to include distributed tracing in them. So various web frameworks, I think, was it Hadoop or HBase was-- >> HBase and HDFS were jointly deciding what to do about instrumentation. >> Yeah, and so they would publish an issue on GitHub and someone from OpenTracing would respond saying hey, OpenTracing does this. And they'd be like oh, that's interesting, we can go build an implementation file and issue, someone from OpenCensus would respond and say, no wait, you should use OpenCensus. And with these being very similar yet incompatible APIs, these groups like HBase would sit it and be like, this isn't mature enough, I don't want to deal with this, I've got more important things to focus on right now. And rather than even picking one and ignoring the other, they just ignored tracing, right? With things moving to microservices with Kubernetes being so popular, I mean just look at this conference. Distributed tracing is no longer this kind of nice to have when you're a big company, you need it to understand how your app works and understand the cause of an outage, the cause of a problem. And when you had organizations like this that were looking at tracing instrumentation saying this is a bit of joke with two competing projects, no one was being served well. >> All right, so you talked about there were incompatible APIs, so how do we get from where we were to where we're going? >> So I can talk about that a little bit. The APIs are conceptually incredibly similar. And the part of the criteria for any new language, for OpenTelemetry, are that we are able to build a software bridge to both OpenTracing and OpenCensus that will translate existing instrumentation alongside OpenTelemetry instrumentation, and omit the correct data at the end. And we've built that out in Java already and then starting working a few other languages. It's not a tremendously difficult thing to do if that's your goal. I've worked on this stuff, I started working on Dapper in 2004, so it's been 15 years that I've been working in this space, and I have a lot of regrets about what we did to OpenTracing. And I had this unbelievably tempting thing to start Greenfield like, let's do it right this time, and I'm suppressing every last impulse to do that. And the only goal for this project technically is backwards compatibility. >> Yeah. >> 100% backwards compatibility. There's the famous XKCD comic where you have 14 standards and someone says, we need to create a new standard that will unify across all 14 standards, and now you have 15 standards. So, we don't want to follow that pattern. And by having the leadership from OpenTracing and OpenCensus involved wholesale in this new effort, as well as having these compatibility bridges, we can avoid the fate of IPv6, of Python 3 and things like that. Where the new thing is very appealing but it's so far from the old thing that you literally can't get there incrementally. So that's, our entire design constraint is make sure that backwards compatibility works, get to one project and then we can think about the grand unifying theory of a provability-- >> Ben you are ruining the best thing about standards is that there is so many of them to choose from. (everyone laughing) >> There's still plenty more growing in other areas (laughs) just in this particular space it's smaller. >> One could argue that your approach is nonstandard in its own right. (Ben laughing) And in my own experiments with distributed tracing it seems like step one is, first you have to go back and instrument everything you've built. And step two, hey come back here, because that's a lot of work. The idea of an organization going back and reinstrumenting everything they've already instrumented the first time. >> It's unlikely. >> Unless they build things very modularly and very portably to do exactly that, it's a bit of a heavy lift. >> I agree, yeah, yeah. >> So going forward, are people who have deployed one or the other of your projects going to have to go back and do a reinstrumentation, or will they unify and continue to work as they are? >> So, I would pause at the, I don't know, I would be making up the statistic, so I shouldn't. But let's say a vast majority, I'm thinking like 95, 98% of instrumentation is actually embedded in frameworks and libraries that people depend on. So you need to get Dropwizard, and Spring, and Django, and Flask, and Kafka, things like that need to be instrumented. The application code, the instrumentation, that burden is a bit lower. We announced something called SpecialAgent at LightStep last week, separate to all of this. It's kind of a funny combination, a typical APM agent will interpose on individual function calls, which is a very complicated and heavyweight thing. This doesn't do any of that, but it takes, it basically surveys what you have in your process, it looks for OpenTracing, and in the future OpenTelemetry instrumentation that matches that, and then installs it for you. So you don't have to do any manual work, just basically gluing tab A into slot B or whatever, you don't have to do any of that stuff which is what most OpenTracing instrumentation actually looks like these days. And you can get off the ground without doing any code modifications. So, I think that direction, which is totally portable and vendor neutral as well, as a layer on top of telemetry makes a ton of sense. There are also data translation efforts that are part of OpenCensus that are being ported in to OpenTelemetry that also serve to repurpose existing sources of correlated data. So, all these things are ways to take existing software and get it into the new world without requiring any code changes or redeploys. >> The long-term goal of this has always been that because web framework and client library providers will go and build the instrumentation into those, that when you're writing your own service that you're deploying in Kubernetes or somewhere else, that by linking one of the OpenTelemetry implementations that you get all of that tracing and context propagation, everything out of the box. You as a sort of individual developer are only using the APIs to define custom metrics, custom spans, things that are specific to your business. >> So Ben, you didn't name LightStep the same as your project. But that being said, a major piece of your business is going through a change here, what does this mean for LightStep? >> That's actually not the way I see it for what it's worth. LightStep as a product, since you're giving me an opportunity to talk about it, (laughs) foolish move on your part. No, I'm just kidding. But LightStep as a product is totally omnivorous, we don't really care where the data comes from. And translating any source of data that has a correlation ID and a timestamp is a pretty trivial exercise for us. So we do support OpenTracing, we also support OpenCensus for what it's worth. We'll support OpenTelemetry, we support a bunch of weird in-house things people have already built. We don't care about that at all. The reason that we're pursuing OpenTelemetry is two-fold, one is that we do want to see high quality data coming out of projects. We said at the keynote this morning, but observability literally cannot be better than your telemetry. If your telemetry sucks, your observability will also suck. It's just definitionally true, if you go back to the definition of observability from the '60s. And so we want high quality telemetry so our product can be awesome. Also, just as an individual, I'm a nerd about this stuff and I just like it. I mean a lot of my motivation for working on this is that I personally find it gratifying. It's not really a commercial thing, I just like it. >> Do you find that, as you start talking about this more and more with companies that are becoming cloud-native rapidly, either through digital transformation or from springing fully formed from the forehead of some God, however these born in the cloud companies tend to be, that they intuitively are starting to grasp the value of tracing? Or does this wind up being a much heavier lift as you start, showing them the golden path as it were? >> It's definitely grown like I-- >> Well I think the value of tracing, you see that after you see the negative value of a really catastrophic outage. >> Yes. >> I mean I was just talking to a bank, I won't name the bank but a bank at this conference, and they were talking about their own adoption of tracing, which was pretty slow, until they had a really bad outage where they couldn't transact for an hour and they didn't know which of the 200 services was responsible for the issue. And that really put some muscle behind their tracing initiative. So, typically it's inspired by an incident like that, and then, it's a bit reactive. Sometimes it's not but either way you end up in that place eventually. >> I'm a strong proponent of distributed tracing and I feel very seen by your last answer. (Ben laughing) >> But it's definitely made a big impact. If you came to conferences like this two years ago you'd have Adrian, or Yuri or someone doing a talk on distributed tracing. And they would always start by asking the 100 to 200 person audience, who here knows what distributed tracing is? And like five people would raise their hand and everyone else would be like no, that's why I'm here at the talk, I want to find out about it. And you go to ones now, or even last year, and now they have 400 people at the talk and you ask, who knows what distributed tracing is? And last year over half the people would raise their hand, now it's going to be even higher. And I think just beyond even anecdotes, clearly businesses are finding the value because they're implementing it. And you can see that through the number of companies that have an interest in OpenTracing, OpenTelemetry, OpenCensus. You can see that in the growth of startups in this space, LightStep and others. >> The other thing I like about OpenTelemetry as a name, it's a bit of a mouthful but that's, it's important for people to understand the distinction between telemetry and tracing data and actual solutions. I mean OpenTelemetry stops when the correct data is being omitted. And then what you do with that data is your own business. And I also think that people are realizing that tracing is more than just visualizing a single distributed trace. >> Yeah. >> The traces have an enormous amount of information in there about resource usage, security patterns, access patterns, large-scale performance patterns that are embedded in thousands of traces, that sort of data is making its way into products as well. And I really like that OpenTelemetry has clearly delineated that it stops with the telemetry. OpenTracing was confusing for people, where they'd want tracing and they'd adopt OpenTracing, and then be like, where's my UI? And it's like well no, it's not that kind of project. With OpenTelemetry I think we've been very clear, this is about getting >> The name is more clear yeah. >> very high quality data in a portable way with minimal effort. And then you can use that in any number of ways, and I like that distinction, I think it's important. >> Okay so, how do we make sure that the combination of these two doesn't just get watered-down to the least common denominator, or that Ben just doesn't get upset and say, forget it, I'm going to start from scratch and do it right this time? (Ben laughing) >> I'm not sure I see either of those two happening. To your comment about the least common denominator, we're starting from what I was just commenting about like two years ago, from very little prior art. Like yeah, you had projects like Zipkin, and Zipkin had its own instrumentation, but it was just for tracing, it was just for Zipkin. And you had Jaeger with its own. And so, I think we're so far away, in a few years the least common denominator will be dramatically better than what we have today. (laughs) And so at this stage, I'm not even remotely worried about that. And secondly to some vendor, I know, because Ben had just exampled this, >> Some vendor, some vendor. >> that's probably not, probably not the best one. But for vendor interference in this projects, I really don't see it. Both because of what we talked about earlier where the vendors right now want more telemetry. I meet with them, Ben meets with 'em, we all meet with 'em all the time, we work with them. And the biggest challenge we have is just the data we get is bad, right? Either we don't support certain platforms, we'll get traces that dead end at certain places, we don't get metrics with the same name for certain types of telemetry. And so this project is going to fix that and it's going to solve this problem for a lot of vendors who have this, frankly, a really strong economic incentive to play ball, and to contribute to it. >> Do you see that this, I guess merging of the two projects, is offering an opportunity to either of you to fix some, or revisit if not fix, some of the mistakes, as they were, of the past? I know every time I build something I look back and it was frankly terrible because that's the kind of developer I am. But are you seeing this, as someone who's probably, presumably much better at developing than I've ever been, as the opportunity to unwind some of the decisions you made earlier on, out of either ignorance or it didn't work out as well as you hoped? >> There are a couple of things about each project that we see an opportunity to correct here without doing any damage to the compatibility story. For OpenTracing it was just a bit too narrow. I mean I would talk a lot about how we want to describe the software, not the tracing system. But we kind of made a mistake in that we called it OpenTracing. Really people want, if a request comes in, they want to describe that request and then have it go to their tracing system, but also to their metric system, and to their logging stack, and to anywhere else, their security system. You should only have to instrument that once. So, OpenTracing was a bit too narrow. OpenCensus, we've talked about this a lot, built a really high quality reference implementation into the product, if OpenCensus, the product I mean. And that coupling created problems for vendors to adopt and it was a bit thick for some end users as well. So we are still keeping the reference implementation, but it's now cleanly decoupled. >> Yeah. >> So we have loose coupling, a la OpenTracing, but wider scope a la OpenCensus. And in that aspect, I think philosophically, this OpenTelemetry effort has taken the best of both worlds from these two projects that it started with. >> All right well, Ben and Morgan thank you so much for sharing. Best of luck and let us know if CNCF needs to pull you guys in a room a little bit more to help work through any of the issues. (Ben laughing) But thanks again for joining us. >> Thank you so much. >> Thanks for having us, it's been a pleasure. >> Yeah. >> All right for Corey Quinn, I'm Stu Miniman we'll be back to wrap up our day one of two days live coverage here from KubeCon, CloudNativeCon 2019, Barcelona, Spain. Thanks for watching theCUBE. (soft instrumental music)

>> live from Boston, Massachusetts. It's the you covering your red hat. Some twenty nineteen brought to you by bread hat. >> Well, good morning. And welcome to Day three of our coverage here, Right? Had some twenty nineteen. We're live here on the Cube, were in Boston, Massachusetts, and was soon Merriman. I'm John Wall's. Glad to have you with us for our last day of coverage. We're now joined by the SPP. Adele Technologies. Pete. Myka, Pete. Good to see you this morning. And Pete, by the way, is coming with I'm sure song in this heart of smile on his face two and a half hours to get in today. >> It was a long drive in, but I'm here now. I'm excited to be here. This is a great show. And here with great partners. >> Yeah, the tough part's over, right? >> We're in Boston, not in Vegas, so that you gotta be a little >> bit there some consolation. Let's just first off, let's paint the umbrella here a little bit about the overall partnership between Delhi, um state right and red hat and how that's evolved. And currently, word stands with all the new releases I've heard about this week. >> Yeah, it's been a great partnership for almost two decades now, right? Della and red hat of working together on a lot of different products from ready stack are ready architectures and ready nodes to software sales. Support customer engagements has been a tremendous partnership for twenty years, and I expect to be going for another twenty years. >> All right, that's digging a little bit walking through the stacks, if you Well, so we understand. You know, Red Hat is an operating system, you know, long history working on, you know, all the del platforms. You've got the converge environment. Where where does red hat fit in? What pieces of there ever broadening portfolio fit in? >> Right. So really, on the ready solution side of the world, which is another part of the products I managed for Del. So within the ready solutions environment, we worked with red hat on open stack way. Deliver hardened, supported open stack products to both. Tell Cohen Enterprise Markets on that. We also deliver open shift and already noted ready solution environment so we can deliver that container men's container environment for those same enterprise and serves customers. >> Yeah, so if you know, the Cubans at, you know Del Technologies World last week and at that show in here, I >> saw a sizable >> break out for telecommunications. You know, we could talk a lot about Enterprise, but, you know, telcos got some certain special requirements needed to make sure it's certified for certain things and, you know, gotta be tested out. Maybe we talk a little bit about what those customers are looking for and why that match you red hat makes sense. >> Sure mean Telco really wants to have control over their environment they wantto have. Open source is a great technology for Tell Cole, right, and they love taking the technology customizing for their environment, reselling components to their end users in open stack from Red Hat is a perfect fit for that market. And so again, we deliver that and the hardened solution on top Adele Technologies on Del Partridge servers deliver that to the telco market and provide them the tools and the capabilities they need to deliver the solutions to their customers. >> What what is it? Let's go dive in just a little bit. Then about those specific traits or attributes, you think in terms of the telecom market goes, you know what is specifically about you think there needs that they find so attractive about open source and what makes them stand apart from other industry sectors. Yet to me, it's controlling >> customization. So rather than taking a packaged app that shrink wrapped in running it like everybody else, they want to get a customized control for their markets. They have certain as to mention they have certain standards and compliance you don't have to deal with. They also want to differentiate within that telecom market. So it's hard to do without having control around the underlying stack. I think those are the big attractiveness around. And then, um, you know that the solution from Red Hat combined with Dellis is such a enterprise quality product for the telecom market, which I think has certain advantages. >> Okay, so you mentioned you know, the ready solutions and open stack piece, and then on top of that, there could be open ships. So that's right, a news, you know, talk to you know, many of the customers, the executive team on the team here, open shift for showing good momentum over thousand customers. So how does that fit in with the solutions you're >> offering well, so we offer a ready solution for open shift this wall, right? And we see that as the container solution for the the market that really wants those open source type products and has a line themselves with red hat in Lenox. And so it's a perfect solution for that. And, you know, we really see Oprah shift as the ability to create a managed environment for containers as we saw from Polish Kino with Over shit for now provides a tremendous hybrid cloud experience for customers at one of my great workloads, both on premises to cloud and back. And so we think that's tremendous technology that we'll add value. And with our hardware technology underneath that we could provide a stack that we think services the market quite well. >> Yeah, it's funny, Pete, you know, you've got a lot of history and I've worked with you for many years on this the ultimate A lot of these technologies, you go back to server virtual ization. You look a container ization in Cuba. Netease. They're like, Well, we want to extract upto, allow the applications to be able to be modernized and do these wonderful things. And I shouldn't have to think about the infrastructure. Right. But we know what the end of the day It lives on something, and it needs to be good talk a little bit of things, like Corinne, eh? Tease. And you know where Del thinks they fit from an infrastructure standpoint compared to communities. >> Yeah. What we want to do is provide the infrastructure that makes it easy to four workloads and applications to preside on, including open shifting cabernets environments. Right? And so, really, what you want to do? And for years, as you say, we've got a lot of history in this. We've been trying to push that complexity and management up the stack. So the hardware and even the virtual ization layer and the container layer becoming afterthought, right? And you know, what I saw from open ship for is that really puts the power back into the application developers and makes it easier to manage and control your underlying harder environment. So, with tight integrations into the open ship community with our del technology Zach, we can provide that sort seamless infrastructure layer that allows the application developers to go do what they need to do not be worried about infrastructure management. >> Do you have any customer examples that might help highlight the partnership? >> Um, no, I >> don't have any good. I >> didn't I'm sorry. I didn't >> know the customer. Well, let's hope out for a little bit. And you talk about hybrid and what that's going to enable there, is that the, uh Oh, here we go for you on this in terms of what's new, What's the latest? I mean, what about the capabilities? You're going to get nowt for what's going to be offered and what is that? That's kind of jumping off the page to you. This is Yeah, this was worth the wait. Well, >> to me, it was all about the management in the automation, the underlying infrastructure just again taking that complexity away from the developers and putting it, um, allowing the application developers tools they need to do to very quickly developed applications, but also migrate them to the proper landing spot and maybe cloud one day and maybe on premises the next. You know, one of the beauties of cloud is is there are classes of applications that may not necessarily fit on a public cloud. You may not know that. Do you? Get there and you want to have the flexibility to push them out, see how they work and bring them back in and open Shift gives you all this capability open shit for yeah, >> eso Absolutely what we hear from customers. It it's not. The future is hybrid and multi cloud. It's today, and the future are voting hybrid and multi class today. To that point, I wonder if you could help us. Just It's not Dell specific, but VM wear made an announcement today that they're supporting open shift for on top of'Em. Where can you maybe t explain where that fits into the overall discussion? >> Yeah, So look, Dell's always writing choices, the customers and we want it we want to be. And we are the essential infrastructure company to the enterprise and commercial environments. And so open shift on VM were just another example of choice and customers. They're gonna have different location environments out there. They're going to run some containers. They're going to run. Some of'em are going to run some native way. Want to be the infrastructure provided for that. We want to work with partners like you had a choice to our customers. >> You know, we've heard a lot this week about flexibility, right on a scale and options and all. And I understand providing choice is a great thing, you know, the customers. But what does that do for you in terms of having to answer to all of that desire? The flexibility? Well, it's it's >> opportunity in this challenge, right? Supporting all these different environments, of course, is a challenge for engineering teams. But it's also opportunity if we want to be. And we are the essential, you know, hardware technology, player in the industry. We have to support all these leading platforms and open shifts. Just example of that. The >> challenge on that side of it. I get opportunity, but you have to develop that expertise We do know throughout your force, and that probably has its own challenges. >> It doesn't mean we have to have expertise only and our own technologies like VM wear, but also open shift and other technologies or red hat technologies. We have to higher and cultivate, um, open source engineers, you know, which is not always easy to find on DH. We have to develop those expertise that know how to integrate those components together. Rights, not just a matter of taking the software and laying on top of the next eighty six architecture and saying it's done way, want Toby to integrate that. So we provide the best experience to the customers. So having that capability to understand what's happening at the hardware infrastructure layer also, what's happening at the virtual ization and container layer is a critical piece of knowledge that we have to. We have to grow and continue to work with >> you. But what about, I mean, as far as the competitive nature of the work force, then I kind of thinking about It's almost like ways. The more people who use that, the tougher it is to get around right, Because so the more people who are moving toward open source, the more which is great. But it also the more competitive the hiring becomes, the training becomes that it does bring with it. Certainly I would say barriers by any means, but a different factor. >> It's a challenge across the entire industry right now, hiring good technical people, and it's not just on open source space. It's an all space is open source is a particular challenge because it takes a certain set of skills to work in that environment. Dell has a philosophy where we are continually looking at university hires and growing from within. We try to hire a CZ. Many new hires, new grads as we can, But the reality is we have to look everywhere in order to try to find those. Resource is very hard to come by, and it's very competitive to get these employees are these candidates. Once you find them, it's hard to get him in the head of environment. >> So it it's interesting. Just step back for a second here last week at your show, it was I opening to see such a nadella, you know, up on stage with Pak else, right? While Microsoft Environments have lived on V EMS for a long time, you know, far as I know the first time the two CEOs have been public scene together fast word to here. And once again we saw touching Adela up on stage with, you know, red hat. It's, you know, for years we think about the industry as to the competitive nature and what's going on and Who's fighting who. Multi cloud. It's not like it's everybody's holding hands and singing, you know, Cooper Netease, Kumbaya. But it is a slightly different dynamic today than it might have been >> is very different in the past. When there are maur infrastructure players, Mohr software players, you could pick your swim lanes. You can compete now, the lines are blurred, and cloud definitely has a lot to do with that. Right and hybrid Multi cloud has everything to do with that, because if your applications going run on eight of us one day on premises the next day in azure the next day you better have tools, processes and procedures that allow those applications the migrate across that multi cloud experience. And so what if forces vendors to do is get together and participate in a cooperative in whatever your favorite word is for competitors working together. But that's really what it is, is we've realized you look a Del Technologies UVM. Where is part of our family? But we're working with Red Hat. What, working with Microsoft and Red Hat, as you see, is doing the same thing. It's necessary in today's market in today's environment that you just have to do that. >> Well, Paul, you mentioned swim lanes. I hope the Express lane is open for you on the ride home. So good luck with that. Thanks for the time this morning, too. Good to see you. It's a home game for you. So it's not all bad. It's not all >> bad. No, this is a great place to be and a great event. I'm glad I could be part of the >> burger. Thanks for being with us. Thank you. Back with more live coverage here. You're watching the Cube. Our coverage, right. Had summat twenty nineteen.

(loud upbeat music) >> Narrator: Live from Boston, Massachusetts. It's the Cube. Covering Red Hat Summit 2019. Brought to you by Red Hat >> Well good morning and welcome to day two of Red Hat Summit 2019. We're in Boston. Beautiful Boston, Mass. again. Second day of just gorgeous sunshine as I'm looking outside but we're inside the Boston Convention and Exposition Center BCEC. Stu Miniman John Walls here on the Cube. Stu good morning to ya. >> Good good morning John. Yeah lovely spring day here in Boston. >> John: Yeah >> Crowd's all excited. >> John: Yes >> Lots of things to geek out on. >> John: Let's go back uh lets go back to last night for the sake of it if you don't mind. We just got done with keynotes this morning We'll touch on that in a second. Last night though, what an array of of CEO keynote you might as well call it. We have IBM. we have Microsoft. We have Red Hat. We have you know the boss of each. And first lets lets just jump in first with IBM Ginni Rometty on the stage last night. And settling maybe a few concerns with some of her comments. I don't have a death wish. Independent. All that. So that your she said all of the good things >> Look first of all, love the tone. It's we hear what your saying and we're kind of laughing with you. You know when they joked and said You know IBM's been working for a long time on Linux. You know we spent a billion dollars that was you know big dollar uh dollar Jim Whitter was like 34 billion dollars is a really big number too. Everybody laughed >> Right >> You know the the commentary notes and joking is look we want this to succeed. We're spending 34 billion dollars on Red Hat. We don't have a death with for it you know. We're not trying to kill it. And what she said specifically and they've said it before but it bears repeating you know more often is Red Hat will stay separate. They're not going to "blue wash" the company which is the term for when they normally integrate and take over. They're going to stay separate. The brand is going to stay separate. That's why they didn't stop something like the new rebranding you know uh you know new new >> Logo >> Hat same soul >> Right right >> You know same hat but new logo same soul All of those things are in place you know and when I talk to lots of people in Red Hat they expect that you know day after this closes they'll be doing the same job. They understand that you know things like IBM's scale should be able to enable them and there will be more collaberation there but you know they're under the umbrella but you know are managed separately. Uh and that's something what the other thing Ginni pointed out which I thought was important that she say it and that is something we're all be watching is the culture that they have built is super super important. She said Red Hat's built a wonderful company and maybe more importantly culture and Jim goes Oh and our eco system you know don't forget our eco system She's like of course but that culture should actually slowly infuse into IBM not the reverse. We don't want you know IBM look great culture, great innovations, strong history but IBM is not looking to take IBM's culture and put it on Red Hat. They want to learn from you know the younger you know you know company and you know moving and growing fast So help accelerate. Work together and you know absolutely important and as Jim said on stage you know pretty impressive here at the Red Hat show you start out with the CEO of IBM you end with the CEO of microsoft. Those are two pretty impressive tech companies >> John: Sure >> With your CEOs coming to talk to this community. >> Yeah tell me about on the culture standpoint though you you do have some very definite differences right just in terms of history you know IBM been around forever Red Hat new kid on the block relatively speaking. How hard do you think it really will be? I mean you've been around this space for a long time that's there just that I think an institutional resistance that is is almost inevitable >> Stu: Yeah >> You have (groan) it's gonna take a lot of open mindedness and bending on the IBM side. >> Look yes and no because look Red Hat has facilities. If they're not living in the same place as if they're you know the the tower down Raleigh where Red Hat is if that stays Red Hat people and they stay separate sure they might have some calls where they collaberate but its a you know Conway's law I like to go to is the way software is designed matches the organizational structure. If the organizational structure gets mixed between them, >> Mmhmm >> Expect that IBM culture just 'cause the size of it you know will likely overpower and it's really easy for it to leak that way. Going the other way you know Red Hat's got you know about twelve thirteen thousand employees you know IBM's got well over a hundred thousand employees. So can Red Hat inflitrate it? In pieces and places and start doing it, sure. But it would be very easy for IBM just to total have a blue wave wash over and make Red Hat lose you know what makes them so special and they are special in this industry. But one of the things that I actually really loved in the keynote we'll talk to is some of that what they called their innovation labs what they helped teach some of that culture to some pretty impressive companies and help them along that technical journey to you know not just do the technology but the cultural changes so that you know they can live in that multi cloud world. They can live you know work with the open source even more. >> I think we got the impression or at least I did you know listening to Ginni too there's a recognition there that we being IBM you know we need them. We need you know we we have we're at a somewhat of a competitive disadvantage right now. This gets us in the game on a whole new level. So I'm I'm would imagine that message is being communicated throughout the ranks at IBM. You know there's a reason why we're spending this kind of money and making this kind of a commitment because their ways worked. And it's in a space that we have to be more present >> Hey look I'm excited. Our first two guests of the day we've got Jim Whitehearst the CEO of Red Hat and then we've got Arvind Krishna who is you know the SVP of cloud and heavily involved in that decision to move IBM to do the acquisition and talking about that hybrid multi cloud world. We will dig in there because that you know is the product space it's the area where Red Hat and IBM intersect the most. Because you know I don't expect that IBM is going to mess up you know rhel >> John: right >> you know from a core linux standpoint they've been partnered for a decade on this. It's not competitive with what IBM does. They we you know IBM does not have a huge team doing it but some of the other spaces some of the tooling some of the you know orchestration and that multi cloud world is an area that IBM has a lot of bodies and a lot of resources and we'll see. But you know an area they want to have help is you know IBM absolutely needs to partner in the multi cloud world with more of the cloud environments so maybe we can talk a little bit about Microsoft. >> Yeah lets go Microsoft here um you know again um kind of a nice kumbaya moment last night where there's a handshaking backslapping five years ago they they both readily admitted it. We're talking about you know Satya Nadella and uh Jim Whitehearst last night wouldn've been like that! We weren't on the best of terms not too long ago and to think that we'd be sharing a stage and not only talking about working together but being partners and truly partners um many people would have imagined that to be just totally unfathomable but it happened. We saw it last night! >> Yeah so um and there's a lot more not just to Sataya being here but the relationship uh that I've been learning more about-walking the show floor, talking to some of the people, uh reading some of the articles online there so you know you know big announcement they talked about is open shift on Azure and that you know fully managed you know common operating platform, across the clouds, manage it yourself, consume it as a service, um you know deep integration there uh between Azure and Open Shift. So uh as I mentioned yesterday in our open Red Hat's working with all the clouds you know talk to them at Google at this show two years ago they announced the AWS piece uh but more than that even is you know some of the applications you know where is microsoft doing great? They have business productivity applications so sequel on rhel is something that you know fully supported and is something that you know Red Hat's been seeing a lot of growth there. And it's something that you know you think Microsoft usually you think Windows and today in the technology world you know Satya's goal is when you think Microsoft he wants you thinking you know Azure and AI and not that they don't have a strong Windows business or that it's not going uh you know not going away. See things like in the demo this morning their like oh hey you want to you know manage your all your linux environments and logins? Oh they pulled up a windows desktop. I mean you know it's it's I think it's it's interesting to see that Linux. It's like oh my gosh that's blasphemy. How dare you you know pull up you know a windows gooey and you see like minecraft and all these other stuff there. It's like that's that's not what a linux used to using. >> John: Right right >> But I can go to those environments so that blending of worlds uh is is what we see and uh yeah you know Microsoft and Red Hat uh living together uh you know in a lot of these customer environments is uh impressive. And I heard Satya spending a bunch of time with customers here. He didn't just fly in and do the keynote and then you know out on the jet off to his next environment You know working with the customers. Strong commitment uh to the partnership and as Satya said inter operate and commit to open source which if you haven't been watching the last five years has been a big push of Microsoft uh and uh is not the Microsoft that we grew up off of you know in the '90s and like um with proprietary software, proprietary operating systems, um committing to all of these environments. >> Yeah I mean so lets follow up a little bit on on the commitment angle or you know that discussion because I think you raised an interesting point that this was just not a fly by. It wasn't just a dropping kind of thing. This was a apparently from what you're uh sources have been telling you a very much more committed uh direction for the company for Microsoft we're talking about here. That's a strong statement. That this is not just for show. That our commitment is going to be the long term success. >> Yeah Yeah um you know we go to a lot of shows and when I've been at a lot of the open source shows especially uh really in the container and Kuraneti's space so we've got the Cube two weeks from now in uh Barcelona for the Cube con and Cloud native Con. Uh.. Microsoft and Red Hat are both really big players in that environment and it's not you know shooting arrows and throwing stones. It's everybody's committing to the growth of these environments and the reality for customers is going to be multi cloud. Uh you know Paul Cormier this morning said you know hybrid is the direction. I'm like well no no, it is where they are today. I think what he means to say is if you look in the future, it's not going away. It's not what a few years ago it was the public cloud was the enemy to some and it's taking over and beware. It's well no the reality is is customer's using a ton of SAS. Microsoft to their credit pushed a ton of customers into that environment. They moved Office 365. Wasn't a oh hey it'd be nice if you do it, it's like you were being pushed by you know into this environment and if Micrsoft is pushing you that way and you know I was used to you know getting my discs and downloading things and doing that. Well this is the new world. It's you know SAS first, public cloud, absolutely an environment. We have Azure you know strong growth you know really strong growth. Uh you know for for many years. Um and the data centers, so you're going to have all of these environments and to manage them and make multi cloud better than its parts? Uh... The partnerships need to be deeper than they were in the past. We can't have the old world of saying oh yeah we've signed some cooperative support agreement but if something goes wrong, we're all going to be pointing fingers as to who's fault it is. The customer doesn't care. They need to run their business. >> John: Right >> Uh you know it needs to be able to go. My data and my applications are the lifeblood of my business so partnerships like Microsoft and Red Hat just make all the sense in the world today. >> Yeah we saw some uh some demos today of uh well I saw Open Shift 4 on the stage. Uh you talked about what uh Microsoft and opening up in Windows and all. Um but pretty impressive in terms of upgrading capabilities and automation capabilities just in general that's kinda what the the impression that I left with was. It's pretty cool. This is pretty good. You're allowing a lot of jobs to be done simultaneously without interference without concerns where as you know a year or two back you couldn't have these dual operations going on because you're too worried about interfering or disrupting instead. You're giving great confidence to the application side and to the dev side. So like Dev Ops is you know you're uh taking a lot of the worry out of the equation. >> Yeah it's really interesting time 'cause I you know there are many of the solutions that will just really abstract away or manage away anything that I need to worry about. I just wanna consume it as a service. It's really simple um. I might just have something that I'll you know automatically does most of the stuff for me and I don't need get underneath but still a lot of these demos its okay here's my terminal and you know let me run through these environments uh and I want to have visibility. So um we're in a little bit of a transition period here as the you know where we are. You know what my teams, what the skill set they need to have, how much depth they need to be able to do um because you know these sins of IT in the past was you know how much am I reinventing the wheel or doing undifferentiated heavy lifting where the vendors of the platforms could really make this easier so that what I need to do as the IT is respond to the needs of the business. I need to be agile. I need to be flexible and if I need to you know build this you know build the temple every time they need something uh I'm not going to be able to be fast enough >> John: Right >> And so I need to be at cloud speed. Uh I need to you know be able to you know respond when uh the business says I need something or I need to make a change. It is uh no longer acceptable to say months or years. It's it's now usually measured you know days or weeks if not in certain things are like no no instantly >> Like now. >> You need to now (john laughs) >> Exactly. >> Ready for a big day? >> Stu: Yeah absolutely. >> All right Jim Whitehearst coming up in just a little bit, a moment or two, but we'll continue our coverage here live from Boston. We're at Red Hat Summit 2019 and you are watching the Cube (loud upbeat music) (music fades away)

>> Announcer: Live from Chicago, Illinois, it's theCUBE! Covering VeeamON 2018. Brought to you by Veeam. >> Welcome back to Day Two of VeeamON 2018 in Chicago. My name is Dave Vellante, and I'm here with Stu Miniman. You're watching theCUBE, the leader in live tech coverage. Dante Orsini is here. He's the Senior Vice President of Biz Dev at iland. CUBE alum. Good friend of theCUBE. Great to see you again. >> Great to see ya. >> Thanks for coming on. >> Yeah, thanks for having me. >> What's happening with iland these days, in the world of cloud service providers? >> Well Dave, it's been insane for us. Obviously Veeam's a huge partner of ours. We've been working together for what, seven years now I think. And it's just amazing to see the growth of this company. Right? We've integrated Veeam -- our relationship. We started off basically providing managed backup many, many moons ago. But six years ago we started to build our own platform, on top of Veeam, on top of Cisco, on top of HPE. Customers really wanted to see more control. They wanted greater levels of security. They really wanted a true enterprise cloud. To do that we had to enhance the VMware stack. We had chose to take Veeam and integrate them via their API. Today if somebody deploys anything in the world with iland, it's automatically backed up by Veeam. If you fast forward a bit, as you see what Veeam's done to innovate with cloud and multi cloud, they've really helped build our business. >> Dante, if you go and look back before the whole cloud wave, the typical service provider. They would have one of everything. You'd walk down the aisles and there'd be whatever it was. An EMC box. A digital box. Whatever it was. Did virtualization change that? Were you able to consolidate? Create a platform. Create a simpler environment to manage. Or is there still a lot of bespoke infrastructure lying around? >> Yeah, that's a great question. For us, I'd love to tell you we hit it right the first time twelve years ago. But no. Just like you said. There's all sorts of different technologies right? But I think what we've done is we quickly standardized. We leverage Cisco UCS from a compute perspective. We leverage some of their storage platforms for the things that we do with Veeam Cloud Connect Backup. We actually help them drive the validation of that product before it came to market. We operate at scale with them. Same thing with Veeam. We're their the largest cloud provider in the world right now. As far as leveraging Veeam technologies. In addition to that on the storage front, we also because of the demands of the environment, we really want to deliver a secure cloud service. Encryption is table stakes, and has been for years. HPE Nimble plays a critical role for us there. That's really our stack. Cisco from a network and a compute perspective, VMware with the hypervisor, and HPE from a storage perspective. >> It's sounds like you've taken some very cost effective platforms. Nimble, Veeam, etc. And then architected an enterprise class solution. You guys are adding value around that as an integrator and obviously a service provider. >> Yup, correct. And I think the market is demanding more and more from a cloud provider. People want true transparency. They want control over the infrastructure. For us it's like, how can we develop an API? So we can make this platform extensible. And then still work with the customers that are struggling with the promise of cloud. And Stu, you see this all the time, right? >> Yeah, and Dante, one of the things we're discussing here is it's a very hybrid world. As Veeam said, customers are doing lots of SAAS. They're using service providers. They have their own data centers. They're using a few public clouds. One of the things I've been watching real closely is companies like iland and the other cloud service providers Amazon and Microsoft aren't the enemy anymore. It's, well we actually have to partner with them on some services. We do some things locally. Maybe give us your viewpoint on how that's changed in the last couple of years. >> Yeah, great question. I would tell you that we're not quite there yet, Stu. From my perspective. You guys know, we're known best for providing disaster recovery as a service. That's where we've made a name in the space. But the irony is we've really focused on building this cloud infrastructure. So an I as platform. And ironically that's the majority of our revenue. When we look at public, clearly it is a hybrid world. Where we spend a lot of time, is investing in how can we highly automate the integration? Because we know that people are going to have workloads everywhere. The idea is, think about it from a recovery perspective. If I'm protecting your traditional workloads. And you've got a dev team that's using various different services that are proprietary to a public cloud, that stuff's got to talk to each other in a true resiliency capacity. We wanted to make sure that people could actually highly automate and orchestrate a failover to us, a test to us. But also integrate the connectivity portion of that. Right? Making sure that all these things can talk together is important. You understand as well as I do, as these cloud architectures change, become more modern, and they're more service driven. The traditional, I'm going to move from point A to point B is no longer in play. It's how can I have more diversity amongst my vendor base? If I'm using containers. You've got a globally distributed architecture. If I can deploy some of that with iland, and some of that maybe using Kubernetes, that gives me diversity for recovery. >> Dante, you've hit one of the key things we've been as an industry struggling with. That pace of change is just so rapid. How do you internally deal with that pace of change? As to I architected something today, and tomorrow there's something new. Tell us what you're hearing from your customers as to how they make their decisions and sort through this constantly changing Rubrik? >> Well it's definitely insane. We see all sorts of various different use cases, depending on the industry. And that pressure to innovate at the speed of light is, really people struggle with it. I think from our perspective, there's a couple things that we're doing. One, we actually wrote our own assessment application. We call it iland Catalyst. This was really designed to help both our customers as well as our partners. Cause we go to market through a lot of partners as well, to help streamline this pre-sales process for a customer. Again, we focus squarely on the VMware infrastructure stack. Being able to pull an inventory of what somebody has in their environment. And then go through and select resource pools and VM's, for whatever the purpose. Whether they're looking to work and shift workloads. Or whether they're looking to protect them from a backup or DR perspective, we're able to mitigate all the challenges associated with that. To your point. As people are looking at cloud, it's like okay. Is this cloud thing real? And how's it apply to my business? What can I really do with this? And by the way, I got to deal with my budget also. What's this stuff cost? We've got some really smart people. But you can't scale our smartest people globally. We wanted to really drive that into an application. It's really helped get people to outcomes much quicker. So do it right first. >> Dante, if you reverse back a few years ago, VMware was calling Amazon a book seller. Amazon was calling guys like VMware the old guard. The old way. They kissed and hugged last year. You must've loved that first of all. Because it was like, great, VMware specialist. We'll just drive truck through that opportunity, because we get service provision, cloud, VMware stack, boom. Now fast forward. They've got this little kumbaya thing going on. How do you now differentiate from that? >> Yeah, that's a great question. First of all, VMware, obviously a very strategic partner. I think they've got a long road ahead of them. On some of the things that they're doing. I think the promise of where they're going is great. But I still think there's a lot of folks that struggle with the idea. Think about co-mingling my traditional workloads. And then trying to integrate cloud native services on top of it. I think it's a tall order. We'll see where it goes. We're keeping a close eye on it. But in the interim for us, we continue to see folks that are saying, look I want to get out of the data center business. I've built my data center on VMware. I need to have much greater levels of control and visibility. And you need to make this easy on me. From that perspective, we've been able to do really, really well. We work with a lot of service providers that are looking for that level of a consultative approach. But also want to realize the benefits of a cloud. The point being is, I want a great cloud but it needs to be enterprise class. And I also need to know that I might need help architecting that migration. >> Well that's the key, right? You're not going to get that from an Amazon. They're not going to come into your shop. They're not going to hold your hand through it. They're not going to help you build the architecture route. And help you manage it on an ongoing basis. >> Dante, it's May 2018, so I'd be remiss if I didn't ask about GDPR. >> Hey Stu, I love you man! This is great. You guys know we operate globally, and have for over a decade. GDPR we were way out in front of this. I'm not sure if you follow, The BSI just came out with a new standard. 10012, I believe. I think our Compliance and DPO Officer would be pretty proud of me for remembering that one. >> Dave: I'm proud of ya. >> It's tailor made for GDPR. We've been pre-certified, one of four companies that did it. We do a ton in the security side and the compliance side. And I know they go hand in hand. We went through a global audit last year. On the back of some of the ISO work we do with the CSA, the Cloud Security Alliance. And actually came out with a gold star certification. Sounds juvenile, right? A gold star, woo hoo! But it's a big deal. Only iland and Microsoft have actually achieved that level of certification. Yeah. On the compliance side we're way out in front of GDPR. We're doing a lot from a thought leadership perspective in educating both the partners and the marketplace. I think it's going to see what happens with Brexit also. I think you'll see the rest of the world kind of find their way to their own type of regulation. >> What do all those acronyms mean for your customers in terms of GDPR compliance? How does that turn into value for them, and make their life easier? Can you explain? >> I think right now the whole market's been in my opinion has been ill prepared for this. You see a lot of people scrambling. Being able to identify what data is going to fall under that regulation. How you treat the data. How you're able to account for the data. And also destroy the data. And validate that. Is frankly I see some of the biggest sweeping change in marketing. I see marketing people really scrambling. Because they have to make sure that they double-opt in. Cause the fines for breaching this are unbelievable. I think you're going to see the regulators make an example out of certain people. >> No doubt. >> Quickly. >> There's going to be some examples. They're going to go after the guys with deep pockets first. But the fines are... What are the fines? Four, is it 10% of the turnover? No, 4% of turnover. >> 4% of your previous year's turnover. >> Which is insane. >> Yep, yep. >> That's going to hurt. >> Or something like 20 million pounds, something like that. >> Which ever is greater. >> Which ever is greater. Yes! Yes, exactly. Yup. >> It's pretty onerous. Dante, VeeamON 2018, we'll give you closing thoughts. >> Fantastic event, right. Just super appreciative for our relationship with Veeam. They've been behind us. They've been behind this whole cloud provider community. I mean guys, you know this. Raat Mere and team had the ability to go take this stuff to a public cloud many moons ago. They chose to enable a managed cloud provider market first. We are very grateful for that. >> Awesome. Hey thanks so much for coming on theCUBE. Great to see you. >> My pleasure. >> As always. >> Yup, go Yankees! >> Oh whoa, time out. >> Go Yankees. >> While we're on the topic. Listen, you can't beat the Red Sox in April. Okay, you know that, right? >> Yeah, here we go. >> So it's going to be interesting to see. I mean I have predicted the Yankees take the east, and they go to the World Series. But you got to be excited as a Yankees fan. >> Could be a good year. >> I've always liked Brian Cashman. I think he's one of the best GM's in the business. Watch his moves at the trading deadline. He's going to beef up the bullpen. I hope the Sox can hang tough with him because anything can happen. >> It's true, anything can happen. >> Hey, great to see ya. >> Great to see you guys, thank you. >> Go Sox. >> Dig it. >> Keep it right there everybody. We'll be back with our next guest right after this short break.

>> Announcer: Live! From Las Vegas. It's theCUBE. Covering HPE Discover 2017. Brought to you by Hewlett Packard Enterprise. >> Welcome back everyone. We are here live in Las Vegas for SiliconANGLE's Cube exclusive coverage of three days of wall to wall interviews here at HPE Discover 2017. I'm John Furrier, your host with Dave Vellante, cohost. And our next two guest is Rod Bagg, VP of Analytics, Customer Support, Data Center, Infrastructure, HPE, formerly Nimble now HPE. and Paul Sabin, Senior Network and Infrastructure Manager at Baker Botts LLP. Guys, thanks for joining on theCUBE. >> Male Voices: Thanks for having us. >> So we talked before we came on camera about all the great stories Nimble obviously part of the fold here at HP Enterprise. Your customer stories. Let's get right into it. Tell your story about how Nimble put you out of a job. That's my favorite one. Go. >> Okay, so when I started or when we bought Nimble Storage, I was the senior storage engineer. So we purchased it, we brought it in-house. It was up within, within an hour, I was already starting carve out LUNs. At that point, I'm using the restful APIs to carve out the rest of the 200 LUNS that we needed. Presenting it to the hosts. And by the end of it, it ran itself. Between InfoSight and the fact that the product just is so easily automated, I kid you not, true story, at the end of the year when we were doing our self evaluations, my evaluation said, and congratulations, you don't need me anymore. My position is obsolete. And the management came back and said, Paul, you're absolutely right. We agree that we don't need this position anymore so we're going to promote you to the senior network infrastructure team. (John laughs) So I manage that now. >> So you got promoted. But this is a trend in automation. This is the DevOps, this is the programmable infrastructure world we're moving into with hybrid. >> Exactly. Rod, this is big deal. >> Yeah, yeah exactly. InfoSight as we see it plays a big role in that. Really the product is simple and being able to automate that. But InfoSight giving our customers sort of visibility at a very deep level into how the systems are performing. And what we do on the backend to drive availability really takes a lot of pain off of our customers. Not sure that we put everybody out of work but we certainly make life easier. So that they can focus on the business aspect. >> And you automate those tasks the way that really should be automated and that's a cool thing. >> Yup. >> Take a minute. I'll like you to take a minute just to explain what the product is and what you guys are doing. Just so we can get that out there as context. And then jump into some more stories. >> Yeah so from an InfoSight perspective? >> John: Yeah. >> So InfoSight is our predictive cloud analytics platform that uses machine learning to predict and prevent problems from occurring to our customers. So we're not disrupting their business. And so we collect somewhere in the order of, about maybe 25 million pieces of information from every array and the virtual environment. Everyday from every single array. All of that gets into a galactic database, where we have a team of data scientists working with our support engineers and our product engineers to build wellness rules. We have about eight hundred health checks that are really looking out at every part of the infrastructure for our customers and really avoiding issues for them. >> So you take the data across your entire install base. >> Rod: Yup. >> I'm sure you take care of the data so it's not all-- >> Rod: Oh yeah, it's all secure. >> Secure and nanomized. And then use that as predictive to prescribe or both or how are you-- >> Yeah both. So our real goal there is that if we know of an issue, that's either we found in our labs or maybe one customer has experienced it. Really, we're doing everything we possibly can to analyze that issue across the entire install base. So we're learning from peers. >> Male Voice: Yup. >> And applying those learnings across the install base and preventing other customers from hitting that issue. >> The system is autodidactic in this sense. It learns and then applies, is that right? >> Yeah. So we do machine learning. Semi-supervised in a lot of cases. So where we've seen and issue and we can train the models. And then it will look out for those sort of issue across the entire install. >> John: I like the notion of wellness. >> Yup. >> Brings some of the people we relate to. We also heard terms like self-driving storage. >> Yup. >> Layoff testers. >> Yeah. >> But this is again, the trend that really is needed. Share other stories that you have because this is really where IT is going as it moves to a different kind of application and consumption model for you guys. >> Right so, well, kind of touching about what he was talking about, when you're as a storage guy, what's the number one thing that us storage guys have to do, is we have to prove that it's not the storage that's the problem. So usually, what happened was, in the old world, I would produce some statistics of, okay, and here's the IOPS that we're producing and here's the latency during this time. So based on this, it wasn't me, I don't know who it was. I'm just going to tell you it's not me. In the new world-- [John] That was the finger pointing world. >> Yes it was! >> The other guy got it. >> But with InfoSight, it's like hey, I can tell you but you're also welcome to go here as well. But let me show you VMM site where it's going to show you, not only what was happening at the storage. But let me take you all the way down to the host and then the VM and we're going to find this problem. And yeah, turns out sometimes it's going to be the VM that's all of a sudden taking whatever reason adding a huge amount of latency. And that, is something that, there's no more finger pointing in it anymore. All of a sudden, we're in the same team, it's like this kumbaya thing. >> That's awesome. It's good for the cohesiveness as a team. But also it's time savers too. When you reduce the steps to do things, you get your weekends back as you guys say before you came on camera. Tell the story about how you had to do all this work on the provisioning on the replication side, >> Sure. When we deployed the arrays, we decided it was business decision to go ahead and put the production arrays into our production data center and then we would do the DR at a later time. So I've got all of my data live, on production. And they say, okay, we're adding our Nimble storage at our DR site. Paul, how much replication bandwidth do we need? And so, same story. In the old world, you go and you pull your statistics from your replication technology, you put it in excel spreadsheet, you figure out, okay, here's my peaks and I just want to say, if we fall behind just a little bit, this is what we can do. And so usually what happens is, I say, guys, in my best guess, based on what I can see from my limited scope because my eyes are bleeding at this point. >> From the spreadsheet. You're in a spreadsheet right now. >> Paul: Yes, exactly. >> You're in spreadsheet hell. >> I'm in spreadsheet hell. And so what I do is, after about a weekend's worth of work, I put in this recommendation and I usually fluff it because I could be wrong in my statistics and so this is what I end up creating. >> You don't want to be under. You want to be over. >> Exactly, I'm always trying to do that. So the firm, I'm, hopefully this is, nobody's watching at the office, but sometimes they maybe overpaying for something because I just don't want to make that chance. In the new world, this is actually the coolest thing ever. So I'm on InfoSight and I go to this little dropdown, it's like the tool planner, okay, what's that? Where it's going to tell you what you need for bandwidth based on your actual real data. So then I'm pulling, like okay, based on this time, what is the replication if I want to do it every hour. And what if I want to do it every two hours? So then I just take that and I turn it into this report that I got to present to the executive team and they're like, oh my goodness, you have certainly stepped up. How many weekends did you use on this one? And you know, I'm not going to tell them it took me five minutes in InfoSight (John laughs) to be able to create this report. >> Now that they. >> But now they know. >> Cat's out, but you already got promoted. >> Oh that's true. >> Hey Rod, can you talk about the decision to acquire Nimble. What was the genesis. Obviously there's a portfolio component, tuck-ins, fill in some gaps. But there's this other sort of IP piece. Maybe take us back. >> Yeah, so certainly, there was the portfolio fit with the storage platform. So that was obviously a big part of it. I think the other obviously big part was InfoSight. So the idea that what we're doing there with our customers and approving the availability of the systems and the operational performance of the system and keeping a close eye on that to make sure it's optimized. So all that value prop around InfoSight was a big part of the decision I think. We are working on extending InfoSight into the HP product line. Starting with 3PAR so we are working already with that engineering team. To be able to bring some of these features out as quickly as we can into the 3PAR world as well. >> So what is that, from an engineering standpoint, is that sort of the requirement there is to point InfoSight at the data, the 3PAR data? >> Yeah exactly. So 3PAR does collect a lot of data already. >> Yeah sure do. >> So really, we're just pulling that data into our pipelines and so on within InfoSight and taking advantage of some of the machine learning and algorithms and so on that we already do. Things like DMVision, would be possible and so on in that environment as well if you're a 3PAR customer. >> It's interesting. Back in, maybe 10 years ago, 3PAR was sort of the gold standard of what we used to call the hero report. >> Rod: That's right, yup, yeah. People love that. >> Thin provisioning. What impact it was. >> Rod: Yup. How much you save, et cetera. And then that predated the whole big data analytics years right? >> Rod: Yeah, exactly. >> So when Nimble started, they could have started with that premise. Right around that time. >> Yeah, yup. >> I remember when I first saw it, I was like wow this is magic. >> Yeah exactly. That was the premise, was to really apply data science to all of that data that was coming in. Really transform the support experience for Nimble. And I think that's the other big element for HP as well. There's lots of that we do in our support organization that, to be honest, it's quite enviable, by a lot of storage and high tech vendors. >> You guys took a different approach. I think what's really notable for me, which I'm impressed with is, everyone talks about this but very few put into action, is making the user experience center, >> Rod: Yeah exactly. >> Of the value. I mean all of the things you talk about, the benefits, is really centered around your experience right. Saving you time, making your life easier, shifting the automation, that could be automated with the right things. And moving into higher value things. So Paul, what's your thoughts on this as it goes forward. This world is evolving. We're hearing the message here, simplifying, hybrid IT, you got cloud right on the doorstep, multiple clouds are going to be the endgame, we'll know all this, so all said and done. Whole new infrastructure is going to be out there. What's your view of how that user experience for the practitioners will evolve. What's your vision. How do you see it playing out. >> Rod: Be out of a job again. (Paul laughs) >> No, true story. The firm decided that they were going to bring us some people to help us look into what cloud we should, or how we should utilize the cloud because even from us, we're trying to keep ourselves agile as a law firm. Because if we can provide our services in a better, more meaningful and faster way, that gives us a competitive edge. So we brought in this team and they went over all of our IOPS and at the time it was under the different storage system so it took at least 20, 30 hours of my time to get all these numbers that they wanted. And then they created this report for us. Which I thought was really meaningful and valuable. The last line was, you should do cloud work, cloud makes sense. So that was it. Solid advice you know. Money well spent. (laughs) >> And that's what Meg's basically saying in the key note. The right mix of cloud versus on-prem. Certainly law firms have proprietary information and they want it secure. I guess my question really is, fundamentally is, a provocative one, I'd love to get your thoughts on. Serious question, you can laugh at at it a little bit but with AI bots coming, you can almost see these kinds of legal tasks being automated away. So, you might be, next promotion is taking over the firm. That's where big data can in. So how are you guys looking at that as a firm because I'm sure the lawyers are saying, hey you know what, I can shift my value to higher yield activities >> Paul: Exactly. >> Where that makes sense. You guys talk about that at all? >> We do. And I actually use the example of NASA. I really love NASA, I'm a huge fan. And NASA decide, they declared, we're going to go to Mars. We're going to do this. How are we going to do this? We have to let go of our operational stuff. We have to let go, I mean we can launch the shuttle all day long, we're comfortable with that. We can go into the space station, we're comfortable with that. But now, we've got to go new. And the way we have to do that is, we have to drop this stuff. Let's let other people do this. Let's let the InfoSight team start handling a lot of that work for me. And now, I'm asking my team, guys, I want you to start dreaming. Get out of the operational work. Start dreaming out loud. Let's figure out ways we can deliver value to our attorneys. >> Exactly. >> To free them. And let's let them just, again, take that same freedom, with the business intelligence and the machine learning, you're right that they're document management, which is their bread and butter, is their document production. Even that's getting scrutinized or transformed through this machine learning. And so, you could take this as a, as a way of saying no, there goes my job. Or you can say no, now I've got the opportunity to do something even better and cooler and really bring the value. >> And stretching. That's the whole stretch goal. Having that moonshot, in this case Mars. >> Paul: Mars right. >> It's the stretch and leverage right. >> Paul: Yes. >> That's the concept. How do you apply that to storage because now HP's got the composability, they got synergy. >> Paul: Yeah, yup. >> They have all kinds of. Now glue layer's kind of developing. We heard Antonio Neri in the press and analyst queue. We heard Meg Whitman talk about, you know, most her acquisitions have been in software, except for maybe one or two, over the past couple years, have been software. >> Paul: Yup. >> So, hardware, software kind of blending. >> Yeah. I think so, from the storage perspective certainly, I think that's happening. I think from the InfoSight perspective, where we see that going, is again, today when we put a lot of effort into our recommendation models. And that's an area that's very much in the deep data sciences realm. So when we come up with those recommendations, >> John: Umhmm. >> you know, we do things where we can prevent people from hitting issues and not just sort of happen automatically but some of these things are, something needs changing in their environment. So maybe, maybe there's a QoS policy that should be applied on the array to optimize performance because of some peak workload during Christmas, something of that nature. So that's still a last mile problem for us because you've got a human at the other end that's got to go in there and fix it and hopefully do it right and not ignore it and everything else. >> I can see the headline now, storage wellness coming to HP. >> Rod: Yeah exactly. >> But this is really interesting, comes with self-healing right. >> So that's where we want to go with that. That is really the thing we're working towards in the vision is, how do go and do that, change those QoS policies for the customer where we could inject, let's say, a change control within their change management system. They can go hit a button which we orchestrate that change for them. It's all documented and well controlled. >> It's not just storing the data, it's being data driven for the data being stored in the self crafting storage. >> Rod: Exactly, yeah, exactly. >> Rod, Paul thanks so much for sharing the stories and congratulations on the promotion. >> Thank you. >> And congratulations on InfoSight. You guys got great story there. >> But I never get promoted. (everyone laughs) >> Come in theCUBE, >> great story right. >> get promoted. >> Birds of a feather. >> Appreciate it. >> Thanks for having us. More live coverage here from theCUBE. Here at HP Discover 2017 after this short break. I'm John Furrier with Dave Vellante. We'll be right back. (lively music)

hi buddy we're back this is Dave vellante Wikibon org with Stu miniman my co-host in this segment Chad saket just here a long time cube guest good friend of the cube Chad great to see you Dave it's my pleasure as always man Stu it's good to see you my friend you know it's unbelievable right we shot and I've been talking all week we started the cube 2010 at the MC world we did SI p sapphire the week right after and then the big show for us that year was was vmworld 2010 it's the best show in town it really is it's you know we said at that greatest show on earth we're betting the house on on VMware you know as a as a topic because it is the IT economy yeah obviously spent a lot of time and as you know effort and appreciate you know the shout out that you gave us the other day on the research that we just did I appreciate the shout out that you your results found well you know it's it's all legit you know as you know we do our homework but David's do put a lot of time to that Nick Allen as well so we're really proud of that that work and at the same time things are evolving yeah I guess they want to go back to it must have been 2009 maybe we sat in a room and you chuck talked the future storage networking the up security obviously compute yep management and the whole deal and we spent a good four hours in that room yep you know I was spent after that but I was just getting started I know you would just get that much everything you laid on us that day is coming true yeah really it's really true I mean you said storage is going to be invisible eventually going to get to the network I mean you know the security pieces and on and on and on so so you know I think that that's definitely the story of this year's vmworld right the idea of what VMware is done by abstracting the control plane of networking with NSX you know prior to that integration to Sierra having talked with a lot of them to see our customers they're super happy with it but prior to that it only worked with open V switch which meant it was you know reserved for the customers who are going all-in with kvm and with Zen now in vsphere 55 it supports the distributive V switch in vsphere which means that idea of network virtualization can be applied to a large swath of customers and likewise vmware is doing the same thing for the control plane of storage which you know Stu that was starting even when you were intimately involved at it when you were at emc absolutely around control abstraction using Vasa and and the early ideas of V vols and the other thing that's going on is they're disrupting the data services plane by becoming a storage vendor with their own storage stack with v Sam yeah and we're going to talk about that yeah for sure in a second I got some time on gots do so that's it that's just your wheel so on the storage piece you know unpack for us a little bit Chad you know we talked about storage becoming invisible and we'll talk about in the network space you know what is the value of the storage array of the storage stack itself and how does that play with VMware especially as we look down to everybody's showing bball so look the name of the game is hyper automation in the end it's not storage it's not networking it's not even compute right and we talked about in the past that the design and the dream of the software-defined data center we use different words for it back you know four years ago but the vision of joe and and all of the parts of the federation of emc vmware and now the third one pivotal is to try and say how do we make all the infrastructure in essence invisible pivot even takes it further by just saying hey we'll just use paths and and get rid of even all the measures are going service for years ago right I remember it well they so really do that yes so so that the reason for it is is that it sucks when you try to provision something a workload whatever the workload maybe and the tail the long tail in the process is touching the physical infrastructure of storage networking and compute virtualization historically has tackled and I would call that problem for compute in essence solved can there be improvements for compute sure bigger faster stronger right in storage land inevitably you know we move from the stage of you talk to the storage person they provision something to you to the storage person provisions a pool of something and then you can automate that and deuce from vsphere and use it through plugins and automation ultimately though you would not even want to have that step you'd want to have the storage advertise its capabilities and then when the vm gets created it says I want out of that catalog of services this stuff and that's what Vasa and whole storage policy based management stuff from vSphere 50 51 and now 55 we're all about in networking land you don't want to have to configure VLANs you don't want to have to configure firewalls you want it to be all able to be done programmatically an only way to do that is if you can like we're just talked about with storage and with compute abstract out the network topology yeah I mean I really look at it what we've always said is we need to get rid of that undifferentiated heavy lifting so that the question I have there's there's a lot of startups in this space that have built their products for this new generation builds is a vm aware if you will or just just simple simple and the critique on emc is that this is legacy equipment and well it might be integrated and you're updating it you know this was still legacy architectures you know how does that fit into the new world so you know when you are the leader everybody will throw stones at you and occasionally even as the leader sometimes we throw stones at others and I don't like that right but I think you might be talking about our friends at perhaps tintri as an example well that they are one that they are built for virtual environments absolutely and if you take a look at it what everyone who is in this space new players emerging players we're trying to today hack at that problem tintri to write because there's no constructs at the vSphere layer for vm awareness what they do in their Nasdaq we do in our Nasdaq is to say AHA file is an object a file can be snapped a file can be replicated and if we hyper couple it into vSphere using plugins and extensions we can then manage and operate on those files right now again I'm not saying that our implement eight it's up to the customers to decide about whether emcs is better or ten trees is better and ultimately the customers choose right but basically we're all kind of trying to hack at that because right now Vasa which is the official policy communication vehicle only operates on data stores data store unit of granularity right V vols has always been the target of how we would all as an industry do that right so i would i would argue that what we showed today about you know recoverpoint and the splitter driver and being able to do tivo like functionality for a vm or replicate for a vm i would argue we more than hold our own with the competition but the right answer ultimately is actually to keep going down the path of V vols in the evolution of vaasa so that you know it can be done correctly and not fake vm awareness but actually have fundamental vm awareness I so since we started on storage I got to chime in here so a couple things so I asked Pat this this morning and his response was essentially hey it's all good these guys are on board but I'm skeptical so about what here's the here's the about what so as I said sort of off-camera Microsoft and Oracle I've already been grabbing storage function and their narrow little parts of the world but you p.m. seen a nap everybody else you've seen NEP but particularly Mabel to find ways to add value I compete very effectively there iam VMware's this horizontal player mm-hm and doing something like v san yep you know its nose software-defined this is you know the future I said to Pat well don't guys like EMC and netapp and shirts certainly HP and itachi and IBM etc don't they want to do their own software-defined he goes yes but they're sort of bought bought into this and what do you think about that as a salt as emc I think I think I don't know whether it's right to say it on camera or not I think that basically as NSX was announced and v san has been announced and everybody in the industry is known that these things are coming you know you could hear audibly people's uh what this what the you know you know you're kind of a cisco right of these in I mean so V sans idea of saying hey I'm going to glom the storage that's in the server the dads the flash the pcie-based flash and use it as a distributed storage layer is a good idea it's an idea that is real and innovation is non containable as as Pat would say you know he's a super fan of andy grove right you know is his mentor Andy Grove had a famous quote that basically said innovation can't be stopped if the incumbents don't do it new startups will arrive that will do it yeah no that's that's fair right Sam Palmisano as well said you're going to get commoditized no matter what so so but the key thing is that it will take some time for V Santa mature the 10 target was correctly positioned in the in the keynotes as use it for non-persistent VDI use it for tests and Dev customers are slowly starting to grok the idea of hey wait a second this thing by definition has to create multiple copies of the data on multiple servers so it's space efficiency is not as good right but I think what's going to occur is your people are going to start to use it and they're gonna dig it yeah they're going to want more and they're going to want more which is great right now from our standpoint EMC sales reps may not like it but EMC likes it because you know what there are portions in the market where we have had great success taking lots of share continue to outgrow the competition but there's other places where we frankly fail to serve properly and if those customers choose v san kumbaya customer happy shareholder happy it's all good right v san will expand though right and in fact as a company we embrace the idea of a software-only data service and this is a data plane thing not a control plane thing that's why we acquired scale io recently right because we're looking a look v san will be the answer for customers who love vmware and our 100% vmware and i talked to a big one today who are like yep that's us likewise i talked to a huge one that were like nope we need an answer that's like v san but works with kvm Zen and hyper-v and vsphere some people like their stacks to lock in at one point and their trade that off and your surveys showed that yeah yeah others about half a woman to live with that right and get function they get function and simplicity right and V San will be phenomenal at that as people are seeing now right i've been using the beta for a long time so I know what but the reality of it is that it's going to be a broad kind of ecosystem of traditional storage stacks embedded into hypervisor storage stacks ones that are packaged as bring your own server akv San and scale IO type things ones that are packaged as will give you the server to new tannic simplicity we live in a beautiful chaotic works hope so boyer in this piece the piece that he had stew did took a little shot at the cartel and you didn't like that you thought you shot back so no that is absolutely not not how we roll it's not how are you roll so so how do you roll hotel eat what you kill Isaac cuts hit it you know so listen to be very blunt I'd be lying if I didn't said that there weren't moments whereas EMC we don't get frustrated that hey you know VMware you you should always work with us right again it happens more in the in the field rather than from a you know our headquarters standpoint right there's times where VMware gets really grumpy when EMC is supporting hyper V or OpenStack and a customer right there's times where vmware is really angry that pivotal runs on AWS and like the announcement earlier this week was hey it works great on vsphere like so think about how weird that is it's been like running on AWS for a while now it runs on vsphere and I bchs right Joe is I think Joe Tucci i think i have an insane amount of respect for that guy he was wise enough to go i need to resist the temptation to simplify for our own internal purposes and create lock-in from the past stack through the app stack through that you know vmware stack through the emc sec and instead say you must all fight for the customer independently and EMC you have to pursue it assuming that VMware isn't a constant VMware you must pursue it as if EMC is certainly not a constant pivotal you should pursue it as if neither one of them is a constant now the one thing that I would highlight to everybody who's watching is don't understand miss understand what I'm saying at the same time whenever one of them is not the best choice for the other jogos hey hey what's up guys it's got this yeah who's got this ball so when V CHS was being stood up and they were looking at alternate storage choices Pat didn't say you have to use EMC but he knocked and said guys we're looking at different storage choices you better come in here and if you don't win on your own merits we'll go with someone else you know I think thankfully they did right and we made that argument you're saying if part of Pat's 50 billion dollar cam comes out of AMC's hog well that's the MCS problem they got to figure out how to shore it up yeah we have to figure out how to compete Chad wonder if you know you own the global se forth you know for emc in this ever complicated world it was you know it wasn't easy when you created the V specialist force but it was focused on VMware and they got a lot of weight behind that there were product managers marketing people all with vmware yep titles inside emc in this world of OpenStack and you know hyper-v and kvm how do you deal with that in the field so so that's a great question man so the first observation just while there is diversity right your survey reflects what I tend to find at my customers right which is overwhelmingly VMware within the enterprise use of some k VMS and OpenStack you know where they would have used vSphere or or the vcloud suite a little bit of dabbling in the enterprise some enterprise customers more than others the cloud service cloud service providers far more right when we were doing the V specialist thing it was an effort to rapidly ramp things up and so we built small focused team small focused product managers what's now happened over the last four years is you know if you think back man like EMC was like a no-show at vmworld 2006 right we our company got the memo focused in we won the best storage choice for VMware deepest integration blah blah blah the what's HAP makes me very happy now is that's now embedded into the product teams it no longer requires a someone watching it just happens organically gooood from a field standpoint the V specialist role many of those V specialists are now leaders of the SE orgs and all sorts of functions so it's no longer somebody thing it's now on everybody thing right but the V specialist mission which used to be makes sure that emc is the best choice for VMware has broadened out to really be best choice for the vcloud suite and VMware stack and also OpenStack to understand and reflect the fact that it's a it's a dynamic open world so so we brought to get in the hook and made me talk about networking so we're just going to ignore the hook for now and talk about networking so NSX yes awesome we saw Martines yeah a little demo up there but it's not going to be that simple why is networking so so hard and you now remember 2009 yeah showed us the roadmap yep now we're here where's it's so first things first what he demoed it is actually that simple if you can constrain a whole bunch of parameters right so if you can constrain yourself to every endpoint is a distributed virtual switch or an open V switch like that thermodynamics problem you can so know what I'm talking about so so if some assumptions its simplify rate they write it if you can if you can constrain it and say everything is connected to a distributive e switch from VMware or an open V switch from kvm and Zen and you assume that the net physical network layer is a bottomless pit of bandwidth and latency in other words you know that there will never be a contention you know at the core networking layer it actually is really that easy right now that may sound like Chad those two constraints are stupid they're not actually that stupid right within the core data center bandwidth is very easy to apply it's much easier to say I'll deploy 10 gige and then go to 40 gig e than it is to hyper design the data center with qos and manage the you know customers have demonstrated time and time again that they'll just go from one gig to 10 gig to 48 to 100 gig rather than trying to hyper engineer the whole thing inside the data center in the wam different story right right also I mean it is a true statement to say in a service provider and in most enterprises eighty ninety percent of their workloads do finish on a thing that is attached to a distributive virtual switch or a physical switch right now where it's going to get funky is that obviously I think NSX is perhaps out there in front in terms of SDN land but they're not alone you know there were lots of partners and we know that cisco has got some cool stuff that they talked about at Cisco live and that our are coming right and you can't you know this goes an amazing company and they have many beloved customers and CC IES and CCNA s around the globe that you know are going to be very interested to see what since you been see I mean interesting play and you can read all about it online and people speculation sure yeah it's going to it's going to be cool though I mean I think one thing that is fun to remember is like innovation is non-stop about it'sit's disruptions or can't be stopped they're going to happen no matter where and in the end it's fundamentally all good for the customer whether it's real CVM where NSX whatever what I love and I said this the pet and I said this did Paul Moretz when I first heard his you know vision I said you guys vmware is ambitious you know if nothing else its ambitious and it's executed on that ambition so it's toss them to watch oh and you know stay tuned for next week September the fourth speed to lead there's some exciting stuff coming from EMC we generally have learned over the years that it's not a good idea to do mega launches and big things during this week because like you said this is this is vmware show and it's the greatest show on earth right yeah okay so we'll stay tuned for that will be watching hi Chad thanks very much for coming on the cubase oh it's my pleasure guys thank you so much I keep right there buddy we're right back after this quick word

Yeah, I'm Aala, They're the co-founder back to back. This is the cube silicon angle.com, Silicon angle dot TV's production of the cube, our flagship telecasts. We go out to the event. That was a great conversation. I was really just, just cool. I could have, we could have probably hit on a few more things, obviously well read. Awesome. Co-founder of Cloudera a. You were, you did a good job teaming up with that co-founder, huh? Not bad on the cube, huh? He's not bad on the cube, isn't he? He, >>He reads the internet. >>That's what I'm saying. >>Anything is going on. >>He's a cube star, you know, And >>Technology. Jeff knows it. Yeah. >>We, we tell you, I'm smarter just by being in Cloudera all those years. And I actually was following what he was saying, Sad and didn't dust my brain. So, Okay, so you're back. So we were talking earlier with Michaels and about the relational database thing. So I kind of pick that up where we left off with you around, you know, he was really excited. It's like, you know, hey, we saw that relational database movement happen. He was part of that. Yeah, yeah. That generation. And then, but things were happening or kind of happening the same way in a similar way, still early. So I was trying to really peg with him, how early are we, like, so, you know, as the curve, you know, this is 1400, it's not the Javit Center yet. Maybe the Duke world, you know, next year might be at the Javit Center, 35,000 just don't go to Vegas. So I'm trying to figure out where we are on that curve. Yeah. And we on the upwards slope, you know, down here, not even hitting that, >>I think, I think, I think we're moving up quicker than previous waves. And actually if you, if you look for example, Oracle, I think it took them 15, 20 years until they, they really became a mature company, VM VMware, which started about, what, 12, 13 years ago. It took them about maybe eight years to, to be a big company, met your company, and I'm hoping we're gonna do it in five. So a couple more years. >>Highly accelerated. >>Yes. But yeah, we see, I mean, I'm, I'm, I've been surprised by the growth. I have been, Right? I've been told, warned about enterprise software and, and that it takes long for production to take place. >>But the consumerization trend is really changing that. I mean, it seems to be that, yeah, the enterprises always last. Why the shorter >>Cycle? I think the shorter cycle is coming from having the, the, the, the right solution for the right problem at the right time. I think that's a big part of it. So luck definitely is a big part of this. Now, in terms of why this is changing compared to a couple of dec decades ago, why the adoption is changing compared to a couple of decades ago. I, I think that's coming just because of how quickly the technology itself, the underlying hardware is evolving. So right now, the fact that you can buy a single server and it has eight cores to 16 cores has 12 hards to terabytes. Each is, is something that's just pushing the, the, the, the limits what you can do with the existing systems and hence making it more likely for new systems to disrupt them. >>Yeah. We can talk about a lot. It's very easy for people to actually start a, a big data >>Project. >>Yes. For >>Example. Yes. And the hardest part is, okay, what, what do I really, what problem do I need to solve? How am I gonna, how am I gonna monetize it? Right? Those are the hard parts. It's not the, not the underlying >>Technology. Yes, Yes, that's true. That's true. I mean, >>You're saying, eh, you're saying >>Because, because I'm seeing both so much. I'm, I'm seeing both. I'm seeing both. And like, I'm seeing cases where you're right. There's some companies that was like, Oh, this Hadoop thing is so cool. What problem can I solve with it? And I see other companies, like, I have this huge problem and, and, and they don't know that HA exists. It's so, And once they know, they just jump on it right away. It's like, we know when you have a headache and you're searching for the medicine in Espin. Wow. It >>Works. I was talking to Jeff Hiba before he came on stage and, and I didn't even get to it cuz we were so on a nice riff there. Right. Bunch of like a musicians playing the guitar together. But like he, we talked about the it and and dynamics and he said something that I thoughts right. On money and SAP is talking the same thing and said they're going to the lines of business. Yes. Because it is the gatekeeper that's, it's like selling mini computers to a mainframe selling client servers from a mini computer team. Yeah. >>There's not, we're seeing, we're seeing both as well. So more likely the, the former one meaning, meaning that yes, line of business and departments, they adopt the technology and then it comes in and they see there's already these five different departments having it and they think, okay, now we need to formalize this across the organization. >>So what happens then? What are you seeing out there? Like when that happens, that mean people get their hands on, Hey, we got a problem to solve. Yeah. Is that what it comes down to? Well, Hadoop exist. Go get Hadoop. Oh yeah. They plop it in there and I what does it do? They, >>So they pop it into their, in their own installation or on the, on the cloud and they show that this actually is working and solving the problem for them. Yeah. And when that happens, it's a very, it's a very easy adoption from there on because they just go tell it, We need this right now because it's solving this problem and it's gonna make, make us much >>More money moving it right in. Yes. No problems. >>Is is that another reason why the cycle's compressed? I mean, you know, you think client server, there was a lot of resistance from it and now it's more much, Same thing with mobile. I mean mobile is flipped, right? I mean, so okay, bring it in. We gotta deal with it. Yep. I would think the same thing. We, we have a data problem. Let's turn it into an >>Opportunity. Yeah. In my, and it goes back to what I said earlier, the right solution for the right problem at the right time. Like when they, when you have larger amounts of unstructured data, there isn't anything else out there that can even touch what had, can >>Do. So Amar, I need to just change gears here a minute. The gaming stuff. So we have, we we're featured on justin.tv right now on the front page. Oh wow. But the numbers aren't coming in because there's a competing stream of a recently released Modern Warfare three feature. Yes. Yes. So >>I was looking for, we >>Have to compete with Modern Warfare three. So can you, can we talk about Modern Warfare three for a minute and share the folks what you think of the current version, if any, if you played it. Yeah. So >>Unfortunately I'm waiting to get back home. I don't have my Xbox with me here. >>A little like a, I'm talking about >>My lines and business. >>Boom. Water warfares like a Christmas >>Tree here. Sorry. You know, I love, I'm a big gamer. I'm a big video gamer at Cloudera. We have every Thursday at five 30 end office, we, we play Call of of Beauty version four, which is modern world form one actually. And I challenge, I challenge people out there to come challenge our team. Just ping me on Twitter and we'll, we'll do a Cloudera versus >>Let's, let's, let's reframe that. Let team out. There am Abalas company. This is the geeks that invent the future. Jeff Haer Baer at Facebook now at Cloudera. Hammerer leading the charge. These guys are at gamers. So all the young gamers out there am are saying they're gonna challenge you. At which version? >>Modern Warfare one. >>Modern Warfare one. Yes. How do they fire in? Can you set up an >>External We'll >>We'll figure it out. We'll figure it out. Okay. >>Yeah. Just p me on Twitter and We'll, >>We can carry it live actually we can stream that. Yeah, >>That'd be great. >>Great. >>Yeah. So I'll tell you some of our best Hadooop committers and Hadoop developers pitch >>A picture. Modern Warfare >>Three going now Model Warfare three. Very excited about the game. I saw the, the trailers for it looks, graphics look just amazing. Graphics are amazing. I love the Sirius since the first one that came out. And I'm looking forward to getting back home to playing the game. >>I can't play, my son won't let me play. I'm such a fumbler with the Hub. I'm a keyboard controller. I can't work the Xbox controller. Oh, I have a coordination problem my age and I'm just a gluts and like, like Dad, sorry, Charity's over. I can I play with my friends? You the box. But I'm around big gamer. >>But, but in terms of, I mean, something I wanted to bring up is how to link up gaming with big data and analysis and so on. So like, I, I'm a big gamer. I love playing games, but at the same time, whenever I play games, I feel a little bit guilty because it's kind of like wasted time. So it's like, I mean, yeah, it's fun and I'm getting lots of enjoyment on it makes my life much more cheerful. But still, how can we harness all of this, all of these hours that gamers spend playing a game like Modern Warfare three, How can we, how can we collect instrument, all of the data that's coming from that and coming up, for example, with something useful with predicted. >>This is exactly, this is exactly the kind of application that's mainstream is gaming. Yeah. Yeah. Danny at Riot G is telling me, we saw him at Oracle Open World. He's up there for the Java one. He said that they, they don't really have a big data platform and their business is about understanding user behavior rep tons of data about user playing time, who they're playing with. Yeah, Yeah. How they want us to get into currency trading, You know, >>Buy, I can't, I can't mention the names, but some of the biggest giving companies out there are using Hadoop right now. And, and depending on CDH for doing exactly that kind of thing, creating >>A good user experience >>Today, they're doing it for the purpose of enhancing the user experience and improving retention. So they do track everything. Like every single bullet, you fire everything in best Ball Head, you get everything home run, you do. And, and, and in, in a three >>Type of game consecutive headshot, you get >>Everything, everything is being Yeah. Headshot you get and so on. But, but as you said, they are using that information today to sell more products and, and, and retain their users. Now what I'm suggesting is that how can you harness that energy for the good as well? I mean for making money, money is good and everything, but how can you harness that for doing something useful so that all of this entertainment time is also actually productive time as well. I think that'd be a holy grail in this, in this environment if we >>Can achieve that. Yeah. It used to be that corn used to be the telegraph of the future of about, of applications, but gaming really is, if you look at gaming, you know, you get the headset on. It's a collaborative environment. Oh yeah. You got unified communications. >>Yeah. And you see our teenager kids, how, how many hours they spend on these things. >>You got play as a play environments, very social collaborative. Yeah. You know, some say, you know, we we're saying, what I'm saying is that that's the, that's the future work environment with Skype evolving. We're our multiplayer game's called our job. Right? Yeah. You know, so I'm big on gaming. So all the gamers out there, a has challenged you. Yeah. Got a big data example. What else are we seeing? So let's talk about the, the software. So we, one of the things you were talking about that I really liked, you were going down the list. So on Mike's slide he had all the new features. So around the core, can you just go down the core and rattle off your version of what, what it means and what it is. So you start off with say H Base, we talked about that already. What are the other ones that are out there? >>So the projects that we have right there, >>The projects that are around those tools that are being built. Cause >>Yeah, so the foundational, the foundational one as we mentioned before, is sdfs for storage map use for processing. Yeah. And then the, the immediate layer above that is how to make MAP reduce easier for the masses. So how can, not everybody knows how to learn map, use Java, everybody knows sql, right? So, so one of the most successful projects right now that has the highest attach rate, meaning people usually when they install had do installed as well is Hive. So Hive takes sequel and so Jeff Harm Becker, my co-founder, when he was at Facebook, his team built the Hive system. Essentially Hive takes sql so you don't have to learn a new language, you already know sql. And then converts that into MAP use for you. That not only expands the developer base for how many people can use adu, but also makes it easier to integrate Hadoop through all DBC and JDBC integrated with BI tools like MicroStrategy and Tableau and Informatica, et cetera, et cetera. >>You mentioned R too. You mentioned R Program R >>As well. Yeah, R is one of our best partnerships. We're very, very happy with them. So that's, that's one of the very key projects is Hive assisted project to Hive ISS called Pig. A pig Latin is a language that ya invented that you have to learn the language. It's very easy, it's very easy to learn compared to map produce. But once you learn it, you can, you can specify very deep data pipelines, right? SQL is good for queries. It's not good for data pipelines because it becomes very convoluted. It becomes very hard for the, the human brain to understand it. So Pig is much more natural to the human. It's more like Pearl very similar to scripting kind of languages. So with Peggy can write very, very long data pipelines, again, very successful projects doing very, very well. Another key project is Edge Base, like you said. So Edge Base allows you to do low latencies. So you can do very, very quick lookups and also allows you to do transactions. So you can do updates in inserts and deletes. So one of the talks here that had World we try to recommend people watch when the videos come out is the Talk by Jonathan Gray from Facebook. And he talked about how they use Edge Base, >>Jonathan, something on here in the Cube later. Yeah. So >>Drill him on that. So they use Edge Base now for many, many things within Facebook. They have a big team now committed to building an improving edge base with us and with the community at large. And they're using it for doing their online messaging system. The live mail system in Facebook is powered by Edge Base right now. Again, Pro and eBay, The Casini project, they gave a keynote earlier today at the conference as well is using Edge Base as well. So Edge Base is definitely one of the projects that's growing very, very quickly right now within the Hudu system. Another key project that Jeff alluded to earlier when he was on here is Flum. So Flume is very instrumental because you have this nice system had, but Hadoop is useless unless you have data inside it. So how do you get the data inside do? >>So Flum essentially is this very nice framework for having these agents all over your infrastructure, inside your web servers, inside your application servers, inside your mobile devices, your network equipment that collects all of that data and then reliably and, and materializes it inside Hado. So Flum does that. Another good project is Uzi, so many of them, I dunno how, how long you want me to keep going here, But, but Uzi is great. Uzi is a workflow processing system. So Uzi allows you to define a series of jobs. Some of them in Pig, some of them in Hive, some of them in map use. You can define a series of them and then link them to each other and say, only start this job when these other jobs, two jobs finish because I'm waiting for the input from them before I can kick off and so on. >>So Uzi is a very nice framework that will will do that. We'll manage the whole graph of jobs for you and retry things when they fail, et cetera, et cetera. Another good project is where W H I R R and where allows you to very easily start ADU cluster on top of Amazon. Easy two on top of Rackspace, virtualized environ. It's more for kicking off, it's for kicking off Hadoop instances or edge based instances on any virtual infrastructure. Okay. VMware, vCloud. So that it supports all of the major vCloud, sorry, all of the me, all of the major virtualized infrastructure systems out there, Eucalyptus as well, and so on. So that's where W H I R R ARU is another key project. It's one, it's duck cutting's main kind of project right now. Don of that gut cutting came on stage with you guys has, So Aru ARO is a project about how do we encode with our files, the schema of these files, right? >>Because when you open up a text file and you don't know how to what the columns mean and how to pars it, it becomes very hard to work for it. So ARU allows you to do that much more easily. It's also useful for doing rrp. We call rtc remove procedure calls for having different services talk to each other. ARO is very useful for that as well. And the list keeps going on and on Maha. Yeah. Which we just, thanks for me for reminding me of my house. We just added Maha very recently actually. What is that >>Adam? I'm not >>Familiar with it. So Maha is a data mining library. So MAHA takes some of the most popular data mining algorithms for doing clustering and regression and statistical modeling and implements them using the map map with use model. >>They have, they have machine learning in it too or Yes, yes. So that's the machine learning. >>So, So yes. Stay vector to machines and so on. >>What Scoop? >>So Scoop, you know, all of them. Thanks for feeding me all the names. >>The ones I don't understand, >>But there's so many of them, right? I can't even remember all of them. So Scoop actually is a very interesting project, is short for SQL to Hadoop, hence the name Scoop, right? So SQ from SQL and Oops from Hadoop and also means Scoop as in scooping up stuff when you scoop up ice cream. Yeah. And the idea for Scoop is to make it easy to move data between relational systems like Oracle metadata and it is a vertical and so on and Hadoop. So you can very simply say, Scoop the name of the table inside the relation system, the name of the file inside Hadoop. And the, the table will be copied over to the file and Vice and Versa can say Scoop the name of the file in Hadoop, the name of the table over there, it'll move the table over there. So it's a connectivity tool between the relational world and the Hadoop world. >>Great, great tutorial. >>And all of these are Apache projects. They're all projects built. >>It's not part of your, your unique proprietary. >>Yes. But >>These are things that you've been contributing >>To, We're contributing to the whole ecosystem. Yes. >>And you understand very well. Yes. And >>And contribute to your knowledge of the marketplace >>And Absolutely. We collaborate with the, with the community on creating these projects. We employ committers and founders for many of these projects. Like Duck Cutting, the founder of He works in Cloudera, the founder for that UIE project. He works at Calera for zookeeper works at Calera. So we have a number of them on stuff >>Work. So we had Aroon from Horton Works. Yes. And and it was really good because I tell you, I walk away from that conversation and I gotta say for the folks out there, there really isn't a war going on in Apache. There isn't. And >>Apache, there isn't. I mean isn't but would be honest. Like, and in the developer community, we are friends, we're working together. We want to achieve the, there's >>No war. It's all Kumbaya. Everyone understands the rising tide floats, all boats are all playing nice in the same box. Yes. It's just a competitive landscape in Horton. Works >>In the business, >>Business business, competitive business, PR and >>Pr. We're trying to be friendly, as friendly as we can. >>Yeah, no, I mean they're, they're, they're hying it up. But he was like, he was cool. Like, Hey, you know, we know each other. Yes. We all know each other and we're just gonna offer free Yes. And charge with support. And so are they. And that's okay. And they got other things going on. Yes. But he brought up the question. He said they're, they're launching a management console. So I said, Tyler's got a significant lead. He kind of didn't really answer the question. So the question is, that's your core bread and butter, That's your yes >>And no. Yes and no. I mean if you look at, if you look at Cloudera Enterprise, and I mentioned this earlier and when we talked in the morning, it has two main things in it. Cloudera Enterprise has the management suite, but it also has the, the the the support and maintenance that we provide to our customers and all the experience that we have in our team part That subscription. Yes. For a description. And I, I wanna stress the point that the fact that I built a sports car doesn't mean that I'm good at running that sports car. The driver of the car usually is much better at driving the car than the guy who built the car, right? So yes, we have many people on staff that are helping build had, but we have many more people on stuff that helped run Hado at large scale, at at financial indu, financial industry, retail industry, telecom industry, media industry, health industry, et cetera, et cetera. So that's very, very important for our customer. All that experience that we bring in on how to run the system technically Yeah. Within these verticals. >>But their strategies clear. We're gonna create an open source project within Apache for a management consult. Yes. And we sell support too. Yes. So there'll be a free alternative to management. >>So we have to see, But I mean we look at the product, I mean our products, >>It's gotta come down to product differentiation. >>Our product has been in the market for two years, so they just started building their products. It's >>Alpha, It's just Alpha. The >>Product is Alpha in Alpha right now. Yeah. Okay. >>Well the Apache products, it is >>Apache, right? Yeah. The Apache project is out. So we'll see how it does it compare to ours. But I think ours is way, way ahead of anything else out there. Yeah. Essentially people to try that for themselves and >>See essentially, John, when I asked Arro why does the world need Hortonwork? You know, eventually the answer we got was, well it's free. It needs to be more open. Had needs to be more open. >>No, there's, >>It's going to be, That's not really the reason why Warton >>Works. >>No, they want, they want to go make money. >>Exactly. We wasn't >>Gonna say them you >>When I kept pushing and pushing and that's ultimately the closest we can get cuz you >>Just listens. Not gonna >>12 open source projects. Yes. >>I >>Mean, yeah, yeah. You can't get much more open. Yeah. Look >>At management >>Consult, but Airs not shooting on all those. I mean, I mean not only we are No, no, not >>No, no, we absolutely >>Are. No, you are contributing. You're not. But that's not all your projects. There's other people >>Involved. Yeah, we didn't start, we didn't start all of these projects. Yeah, that's >>True. You contributing heavily to all of them. >>Yes, we >>Are. And that's clear. Todd Lipkin said that, you know, he contributed his first patch to HPAC in 2008. Yes. So I mean, you go back through the ranks >>Of your people and Todd now is a committer on Edge base is a committer on had itself. So on a number >>Of you clearly the lead and, and you know, and, but >>There is a concern. But we, we've heard it and I wanna just ask you No, no. So there's a concern that if I build processes around a proprietary management console, Yes. I'm gonna end up being locked into that proprietary management CNA all over again. Now this is so far from ca Yes. >>Right. >>But that's a concern that some people have expressed. And, and, and I think one of the reasons why Port Works is getting so much attention. So Yes. >>Talk about that. It's, it's a very good, it's a very good observation to make. Actually, >>There there is two separate things here. There's the platform where all the data sets and then there's this management parcel beside the platform. Now why did we make the management console why the cloud didn't make the management console? Because it makes our job for supporting the customers much more achievable. When a customer calls in and says, We have a problem, help us fix this problem. When they go to our management console, there is a button they click that gives us a dump of the state, of the cluster. And that's what allows us to very quickly debug what's going on. And within minutes tell them you need to do this and you to do that. Yeah. Without that we just can't offer the support services. There's >>Real value there. >>Yes. So, so now a year from, But, but, but you have to keep in mind that the, the underlying platform is completely open source and free CBH is completely a hundred percent open source, a hundred percent free, a hundred percent Apache. So a year from now, when it comes time to renew with us, if the customer is not happy with our management suite is not happy with our support data, they can, they can go to work >>And works. People are afraid >>Of all they can go to ibm. >>The data, you can take the data that >>You don't even need to take the data. You're not gonna move the data. It's the same system, the same software. Every, everything in CDH is Apache. Right? We're not putting anything in cdh, which is not Apache. So a year from now, if you're not happy with our service to you and the value that we're providing, you can switch. There is no lock in. There is no lock. And >>Your, your argument would be the switching costs to >>The only lock in is happiness. The only lock in is which >>Happiness inspection customer delay. Which by, by the way, we just wrote a piece about those wars and we said the risk of lockin is low. We made that statement. We've got some heat for it. Yes. And >>This is sort of at scale though. What the, what the people are saying, they're throwing the tomatoes is saying if this is, again, in theory at scale, the customers are so comfortable with that, the console that they don't switch. Now my argument was >>Yes, but that means they're happy with it. That means they're satisfied and happy >>With it. >>And it's more economical for them than going and hiding people full-time on stuff. Yeah. >>So you're, you're always on check as, as long as the customer doesn't feel like Oracle. >>Yeah. See that's different. Oracle is very, Oracle >>Is like different, right? Yeah. Here it's like Cisco routers, they get nested into the environment, provide value. That's just good competitive product strategy. Yes. If it they're happy. Yeah. It's >>Called open washing with >>Oracle, >>I mean our number one core attribute on the company, the number one value for us is customer satisfaction. Keeping our people Yeah. Our customers happy with the service that we provide. >>So differentiate in the product. Yes. Keep the commanding lead. That's the strategist. That's the, that's what's happening. That's your goal. Yes. >>That's what's happening. >>Absolutely. Okay. Co-founder of Cloudera, Always a pleasure to have you on the cube. We really appreciate all the hospitality over the beer and a half. And wanna personally thank you for letting us sit in your office and we'll miss you >>And we'll miss you too. We'll >>See you at the, the Cube events off Swing by, thanks for coming on the cube and great to see you and congratulations on all your success. >>Thank >>You. And thanks for the review on Modern Warfare three. Yeah, yeah. >>Love me again. If there any gaming stuff, you know, I.