(upbeat music) >> Hello everyone. We're here at theCUBE startup showcase made possible by AWS. Thanks so much for joining us today. You know, when Zhamak Dehghani was formulating her ideas around data mesh, she wasn't the only one thinking about decentralized data architectures. HelloFresh was going into hyper-growth mode and realized that in order to support its scale, it needed to rethink how it thought about data. Like many companies that started in the early part of the last decade, HelloFresh relied on a monolithic data architecture and the internal team it had concerns about its ability to support continued innovation at high velocity. The company's data team began to think about the future and work backwards from a target architecture, which possessed many principles of so-called data mesh, even though they didn't use that term specifically. The company is a strong example of an early but practical pioneer of data mesh. Now, there are many practitioners and stakeholders involved in evolving the company's data architecture many of whom are listed here on this slide. Two are highlighted in red and joining us today. We're really excited to welcome you to theCUBE, Clemence Chee, who is the global senior director for data at HelloFresh, and Christoph Sawade, who's the global senior director of data also of course at HelloFresh. Folks, welcome. Thanks so much for making some time today and sharing your story. >> Thank you very much. >> Thanks, Dave. >> All right, let's start with HelloFresh. You guys are number one in the world in your field. You deliver hundreds of millions of meals each year to many, many millions of people around the globe. You're scaling. Christoph, tell us a little bit more about your company and its vision. >> Yeah. Should I start or Clemence? Maybe take over the first piece because Clemence has actually been longer a director at HelloFresh. >> Yeah go ahead Clemence. >> I mean, yes, about approximately six years ago I joined and HelloFresh, and I didn't think about the startup I was joining would eventually IPO. And just two years later, HelloFresh went public. And approximately three years and 10 months after HelloFresh was listed on the German stock exchange which was just last week, HelloFresh was included in the DAX Germany's leading stock market index and that, to mind a great, great milestone, and I'm really looking forward and I'm very excited for the future for HelloFresh and also our data. The vision that we have is to become the world's leading food solution group. And there are a lot of attractive opportunities. So recently we did launch and expand in Norway. This was in July. And earlier this year, we launched the US brand, Green Chef, in the UK as well. We're committed to launch continuously different geographies in the next coming years and have a strong path ahead of us. With the acquisition of ready to eat companies like factor in the US and the plant acquisition of Youfoodz in Australia, we are diversifying our offer, now reaching even more and more untapped customer segments and increase our total address for the market. So by offering customers and growing range of different alternatives to shop food and to consume meals, we are charging towards this vision and this goal to become the world's leading integrated food solutions group. >> Love it. You guys are on a rocket ship. You're really transforming the industry. And as you expand your TAM, it brings us to sort of the data as a core part of that strategy. So maybe you guys could talk a little bit about your journey as a company, specifically as it relates to your data journey. I mean, you began as a startup, you had a basic architecture and like everyone, you've made extensive use of spreadsheets, you built a Hadoop based system that started to grow. And when the company IPO'd, you really started to explode. So maybe describe that journey from a data perspective. >> Yes, Dave. So HelloFresh by 2015, approximately had evolved what amount, a classical centralized data management set up. So we grew very organically over the years, and there were a lot of very smart people around the globe, really building the company and building our infrastructure. This also means that there were a small number of internal and external sources, data sources, and a centralized BI team with a number of people producing different reports, different dashboards and, and products for our executives, for example, or for different operations teams to see a company's performance and knowledge was transferred just by our talking to each other face-to-face conversations. And the people in the data warehouse team were considered as the data wizard or as the ETL wizard. Very classical challenges. And it was ETL, who reserved, indicated the kind of like a style of knowledge of data management, right? So our central data warehouse team then was responsible for different type of verticals in different domains, different geographies. And all this setup gave us in the beginning, the flexibility to grow fast as a company in 2015. >> Christoph, anything to add to that? >> Yes, not explicitly to that one, but as, as Clemence said, right, this was kind of the setup that actually worked for us quite a while. And then in 2017, when HelloFresh went public, the company also grew rapidly. And just to give you an idea how that looked like as well, the tech departments have actually increased from about 40 people to almost 300 engineers. And in the same way as the business units, as there Clemence has described, also grew sustainably. So we continue to launch HelloFresh in new countries, launched new brands like Every Plate, and also acquired other brands like we have Factor. And that grows also from a data perspective, the number of data requests that the central (mumbles), we're getting become more and more and more, and also more and more complex. So that for the team meant that they had a fairly high mental load. So they had to achieve a very, or basically get a very deep understanding about the business and also suffered a lot from this context, switching back and forth. Essentially, they had to prioritize across our product requests from our physical product, digital product, from a physical, from, sorry, from the marketing perspective, and also from the central reporting teams. And in a nutshell, this was very hard for these people, and that altered situations that let's say the solution that we have built. We can not really optimal. So in a, in a, in a, in a nutshell, the central function became a bottleneck and slow down of all the innovation of the company. >> It's a classic case. Isn't it? I mean, Clemence, you see, you see the central team becomes a bottleneck, and so the lines of business, the marketing team, sales teams say "Okay, we're going to take things into our own hands." And then of course IT and the technical team is called in later to clean up the mess. Maybe, maybe I'm overstating it, but, but that's a common situation. Isn't it? >> Yeah this is what exactly happened. Right. So we had a bottleneck, we had those central teams, there was always a bit of tension. Analytics teams then started in those business domains like marketing, supply chain, finance, HR, and so on started really to build their own data solutions. At some point you have to get the ball rolling, right? And then continue the trajectory, which means then that the data pipelines didn't meet the engineering standards. And there was an increased need for maintenance and support from central teams. Hence over time, the knowledge about those pipelines and how to maintain a particular infrastructure, for example, left the company, such that most of those data assets and data sets that turned into a huge debt with decreasing data quality, also decreasing lack of trust, decreasing transparency. And this was an increasing challenge where a majority of time was spent in meeting rooms to align on, on data quality for example. >> Yeah. And the point you were making Christoph about context switching, and this is, this is a point that Zhamak makes quite often as we've, we've, we've contextualized our operational systems like our sales systems, our marketing systems, but not our, our data systems. So you're asking the data team, okay, be an expert in sales, be an expert in marketing, be an expert in logistics, be an expert in supply chain and it's start, stop, start, stop. It's a paper cut environment, and it's just not as productive. But, but, and the flip side of that is when you think about a centralized organization, you think, hey, this is going to be a very efficient way across functional team to support the organization, but it's not necessarily the highest velocity, most effective organizational structure. >> Yeah. So, so I agree with that piece, that's up to a certain scale. A centralized function has a lot of advantages, right? So it's a tool for everyone, which would go to a destined kind of expert team. However, if you see that you actually would like to accelerate that in specific as the type of growth. But you want to actually have autonomy on certain teams and move the teams, or let's say the data to the experts in these teams. And this, as you have mentioned, right, that increases mental load. And you can either internally start splitting your team into different kinds of sub teams focusing on different areas, however, that is then again, just adding another piece where actually collaboration needs to happen because the external seized, so why not bridging that gap immediately and actually move these teams end to end into the, into the function themselves. So maybe just to continue what Clemence was saying, and this is actually where our, so, Clemence and my journey started to become one joint journey. So Clemence was coming actually from one of these teams who builds their own solutions. I was basically heading the platform team called data warehouse team these days. And in 2019, where (mumbles) become more and more serious, I would say, so more and more people have recognized that this model does not really scale, in 2019, basically the leadership of the company came together and identified data as a key strategic asset. And what we mean by that, that if he leveraged it in a, in a, an appropriate way, it gives us a unique, competitive advantage, which could help us to, to support and actually fully automate our decision making process across the entire value chain. So once we, what we're trying to do now, or what we would be aiming for is that HelloFresh is able to build data products that have a purpose. We're moving away from the idea that it's just a bi-product. We have a purpose why we would like to collect this data. There's a clear business need behind that. And because it's so important to, for the company as a business, we also want to provide them as a trustworthy asset to the rest of the organization. We'd say, this is the best customer experience, but at least in a way that users can easily discover, understand and securely access, high quality data. >> Yeah. So, and, and, and Clemence, when you see Zhamak's writing, you see, you know, she has the four pillars and the principles. As practitioners, you look at that say, okay, hey, that's pretty good thinking. And then now we have to apply it. And that's where the devil meets the details. So it's the for, the decentralized data ownership, data as a product, which we'll talk about a little bit, self-serve, which you guys have spent a lot of time on, and Clemence your wheelhouse, which is, which is governance and a federated governance model. And it's almost like if you, if you achieve the first two, then you have to solve for the second two, it almost creates a new challenges, but maybe you could talk about that a little bit as to how it relates to HelloFresh. >> Yes. So Chris has mentioned that we identified kind of a challenge beforehand and said, how can we actually decentralized and actually empower the different colleagues of ours? And this was more a, we realized that it was more an organizational or a cultural change. And this is something that someone also mentioned. I think ThoughtWorks mentioned one of the white papers, it's more of an organizational or a cultural impact. And we kicked off a phased reorganization, or different phases we're currently on, in the middle of still, but we kicked off different phases of organizational restructuring or reorganization trying to lock this data at scale. And the idea was really moving away from ever growing complex matrix organizations or matrix setups and split between two different things. One is the value creation. So basically when people ask the question, what can we actually do? What should we do? This is value creation and the how, which is capability building, and both are equal in authority. This actually then creates a high urge in collaboration and this collaboration breaks up the different silos that were built. And of course, this also includes different needs of staffing for teams staffing with more, let's say data scientists or data engineers, data professionals into those business domains, enhance, or some more capability building. >> Okay, go ahead. Sorry. >> So back to Zhamak Dehghani. So we, the idea also then crossed over when she published her papers in May, 2019. And we thought, well, the four pillars that she described were around decentralized data ownership, product, data as a product mindset, we have a self-service infrastructure. And as you mentioned, federated computational governance. And this suited very much with our thinking at that point of time to reorganize the different teams and this then that to not only organizational restructure, but also in completely new approach of how we need to manage data, through data. >> Got it. Okay. So your businesses is exploding. The data team was having to become domain experts to many areas, constantly context switching as we said, people started to take things into their own hands. So again, we said classic story, but, but you didn't let it get out of control and that's important. And so we, we actually have a picture of kind of where you're going today and it's evolved into this, Pat, if you could bring up the picture with the, the elephant, here we go. So I will talk a little bit about the architecture. It doesn't show it here, the spreadsheet era, but Christoph, maybe you could talk about that. It does show the Hadoop monolith, which exists today. I think that's in a managed hosting service, but, but you, you preserve that piece of it. But if I understand it correctly, everything is evolving to the cloud. I think you're running a lot of this or all of it in AWS. You've got, everybody's got their own data sources. You've got a data hub, which I think is enabled by a master catalog for discovery and all this underlying technical infrastructure that is, is really not the focus of this conversation today. But the key here, if I understand correctly is these domains are autonomous and that not only this required technical thinking, but really supportive organizational mindset, which we're going to talk about today. But, but Christoph, maybe you could address, you know, at a high level, some of the architectural evolution that you guys went through. >> Yeah, sure. Yeah. Maybe it's also a good summary about the entire history. So as you have mentioned, right, we started in the very beginning, it's a monolith on the operational plan, right? Actually it wasn't just one model it was two, one for the backend and one for the front end. And our analytical plan was essentially a couple of spreadsheets. And I think there's nothing wrong with spreadsheets, but it allows you to store information, it allows you to transform data, it allows you to share this information, it allows you to visualize this data, but all kind of, it's not actually separating concern, right? Every single one tool. And this means that it's obviously not scalable, right? You reach the point where this kind of management's set up in, or data management is in one tool, reached elements. So what we have started is we created our data lake, as we have seen here on our dupe. And just in the very beginning actually reflected very much our operation upon this. On top of that, we used Impala as a data warehouse, but there was not really a distinction between what is our data warehouse and what is our data lakes as the Impala was used as kind of both as a kind of engine to create a warehouse and data lake constructed itself. And this organic growth actually led to a situation. As I think it's clear now that we had the centralized model as, for all the domains that were really lose Kimball, the modeling standards and there's new uniformity we used to actually build, in-house, a base of building materialized use, of use that we have used for the presentation there. There was a lot of duplication of effort. And in the end, essentially the amendments and feedback tool, which helped us to, to improve of what we, have built during the end in a natural, as you said, the lack of trust. And this basically was a starting point for us to understand, okay, how can we move away? And there are a lot of different things that we can discuss of apart from this organizational structure that we have set up here, we have three or four pillars from Zhamak. However, there's also the next, extra question around, how do we implement product, right? What are the implications on that level and I think that is, that's something that we are, that we are currently still in progress. >> Got it. Okay. So I wonder if we could talk about, switch gears a little bit, and talk about the organizational and cultural challenges that you faced. What were those conversations like? And let's, let's dig into that a little bit. I want to get into governance as well. >> The conversations on the cultural change. I mean, yes, we went through a hyper growth through the last year, and obviously there were a lot of new joiners, a lot of different, very, very smart people joining the company, which then results that collaborations got a bit more difficult. Of course, the time zone changes. You have different, different artifacts that you had recreated in documentation that were flying around. So we were, we had to build the company from scratch, right? Of course, this then resulted always this tension, which I described before. But the most important part here is that data has always been a very important factor at HelloFresh, and we collected more of this data and continued to improve, use data to improve the different key areas of our business. Even when organizational struggles like the central (mumbles) struggles, data somehow always helped us to grow through this kind of change, right? In the end, those decentralized teams in our local geographies started with solutions that serve the business, which was very, very important. Otherwise, we wouldn't be at the place where we are today, but they did violate best practices and standards. And I always use the sports analogy, Dave. So like any sport, there are different rules and regulations that need to be followed. These routes are defined by, I'll call it, the sports association. And this is what you can think about other data governance and then our compliance team. Now we add the players to it who need to follow those rules and abide by them. This is what we then call data management. Now we have the different players, the professionals they also need to be trained and understand the strategy and the rules before they can play. And this is what I then called data literacy. So we realized that we need to focus on helping our teams to develop those capabilities and teach the standards for how work is being done to truly drive functional excellence in the different domains. And one of our ambition of our data literacy program for example, is to really empower every employee at HelloFresh, everyone, to make the right data-informed decisions by providing data education that scales (mumbles), and that can be different things. Different things like including data capabilities with, in the learning path for example, right? So help them to create and deploy data products, connecting data, producers, and data consumers, and create a common sense and more understanding of each other's dependencies, which is important. For example, SIS, SLO, state of contracts, et cetera, people get more of a sense of ownership and responsibility. Of course, we have to define what it means. What does ownership means? What does responsibility mean? But we are teaching this to our colleagues via individual learning patterns and help them upscale to use also their shared infrastructure, and those self-service data applications. And of all to summarize, we are still in this progress of learning. We're still learning as well. So learning never stops at Hello Fresh, but we are really trying this to make it as much fun as possible. And in the end, we all know user behavior is changed through positive experience. So instead of having massive training programs over endless courses of workshops, leaving our new joiners and colleagues confused and overwhelmed, we're applying gamification, right? So split different levels of certification where our colleagues, can access, have had access points. They can earn badges along the way, which then simplifies the process of learning and engagement of the users. And this is what we see in surveys, for example, where our employees value this gamification approach a lot and are even competing to collect those learning pet badges, to become the number one on the leaderboard. >> I love the gamification. I mean, we've seen it work so well in so many different industries, not the least of which is crypto. So you've identified some of the process gaps that you, you saw, you just gloss over them. Sometimes I say, pave the cow path. You didn't try to force. In other words, a new architecture into the legacy processes, you really had to rethink your approach to data management. So what did that entail? >> To rethink the way of data management, 100%. So if I take the example of revolution, industrial revolution or classical supply chain revolution, but just imagine that you have been riding a horse, for example, your whole life, and suddenly you can operate a car or you suddenly receive just a complete new way of transporting assets from A to B. So we needed to establish a new set of cross-functional business processes to run faster, drive faster, more robustly, and deliver data products which can be trusted and used by downstream processes and systems. Hence we had a subset of new standards and new procedures that would fall into the internal data governance and compliance sector. With internal, I'm always referring to the data operations around new things like data catalog, how to identify ownership, how to change ownership, how to certify data assets, everything around classical is software development, which we now apply to data. This, this is some old and new thinking, right? Deployment, versioning, QA, all the different things, ingestion policies, the deletion procedures, all the things that software development has been doing, we do it now with data as well. And it's simple terms, it's a whole redesign of the supply chain of our data with new procedures and new processes in asset creation, asset management and asset consumption. >> So data's become kind of the new development kit, if you will. I want to shift gears and talk about the notion of data product, and we have a slide that, that we pulled from your deck. And I'd like to unpack it a little bit. I'll just, if you can bring that up, I'll, I'll read it. A data product is a product whose primary objective is to leverage on data to solve customer problems, where customers are both internal and external. so pretty straightforward. I know you've, you've gone much deeper in your thinking and into your organization, but how do you think about that and how do you determine for instance, who owns what, how did you get everybody to agree? >> I can take that one. Maybe let me start as a data product. So I think that's an ongoing debate, right? And I think the debate itself is the important piece here, right? You mentioned the debate, you've clarified what we actually mean by that, a product, and what is actually the mindset. So I think just from a definition perspective, right? I think we find the common denominator that we say, okay, that our product is something which is important for the company that comes with value. What do you mean by that? Okay. It's a solution to a customer problem that delivers ideally maximum value to the business. And yes, leverage is the power of data. And we have a couple of examples, and I'll hit refresh here, the historical and classical ones around dashboards, for example, to monitor our error rates, but also more sophisticated based for example, to incorporate machine learning algorithms in our recipe recommendation. However, I think the important aspects of a data product is A: there is an owner, right? There's someone accountable for making sure that the product that you're providing is actually served and has maintained. And there are, there's someone who's making sure that this actually keeps the value of what we are promising. Combined with the idea of the proper documentation, like a product description, right? The people understand how to use it. What is this about? And related to that piece is the idea of, there's a purpose, right? We need to understand or ask ourselves, okay, why does a thing exist? Does it provide the value that we think it does? Then it leads in to a good understanding of what the life cycle of the data product and product life cycle. What do we mean? Okay. From the beginning, from the creation, you need to have a good understanding. You need to collect feedback. We need to learn about that, you need to rework, and actually finally, also to think about, okay, when is it time to decommission that piece So overall I think the core of this data product is product thinking 101, right? That we start, the point is, the starting point needs to be the problem and not the solution. And this is essentially what we have seen, what was missing, what brought us to this kind of data spaghetti that we have built there in Rush, essentially, we built it. Certain data assets develop in isolation and continuously patch the solution just to fulfill these ad hoc requests that we got and actually really understanding what the stakeholder needs. And the interesting piece as a results in duplication of (mumbled) And this is not just frustrating and probably not the most efficient way, how the company should work. But also if I build the same data assets, but slightly different assumption across the company and multiple teams that leads to data inconsistency. And imagine the following scenario. You, as a management, for management perspective, you're asking basically a specific question and you get essentially from a couple of different teams, different kinds of graphs, different kinds of data and numbers. And in the end, you do not know which ones to trust. So there's actually much (mumbles) but good. You do not know what actually is it noise for times of observing or is it just actually, is there actually a signal that I'm looking for? And the same as if I'm running an AB test, right? I have a new feature, I would like to understand what is the business impact of this feature? I run that with a specific source and an unfortunate scenario. Your production system is actually running on a different source. You see different numbers. What you have seen in the AB test is actually not what you see then in production, typical thing. Then as you asking some analytics team to actually do a deep dive, to understand where the discrepancies are coming from, worst case scenario again, there's a different kind of source. So in the end, it's a pretty frustrating scenario. And it's actually a waste of time of people that have to identify the root cause of this type of divergence. So in a nutshell, the highest degree of consistency is actually achieved if people are just reusing data assets. And also in the end, the meetup talk they've given, right? We start trying to establish this approach by AB testing. So we have a team, but just providing, or is kind of owning their target metric associated business teams, and they're providing that as a product also to other services, including the AB testing team. The AB testing team can use this information to find an interface say, okay, I'm drawing information for the metadata of an experiment. And in the end, after the assignment, after this data collection phase, they can easily add a graph to a dashboard just grouped by the AB testing barrier. And we have seen that also in other companies. So it's not just a nice dream that we have, right? I have actually looked at other companies maybe looked on search and we established a complete KPI pipeline that was computing all these information and this information both hosted by the team and those that (mumbles) AB testing, deep dives and, and regular reporting again. So just one last second, the, the important piece, Now, why I'm coming back to that is that it requires that we are treating this data as a product, right? If we want to have multiple people using the thing that I am owning and building, we have to provide this as a trust (mumbles) asset and in a way that it's easy for people to discover and to actually work with. >> Yeah. And coming back to that. So this is, to me this is why I get so excited about data mesh, because I really do think it's the right direction for organizations. When people hear data product, they think, "Well, what does that mean?" But then when you start to sort of define it as you did, it's using data to add value that could be cutting costs, that could be generating revenue, it could be actually directly creating a product that you monetize. So it's sort of in the eyes of the beholder, but I think the other point that we've made, is you made it earlier on too, and again, context. So when you have a centralized data team and you have all these P&L managers, a lot of times they'll question the data 'cause they don't own it. They're like, "Well, wait a minute." If it doesn't agree with their agenda, they'll attack the data. But if they own the data, then they're responsible for defending that. And that is a mindset change that's really important. And I'm curious is how you got to that ownership. Was it a top-down or was somebody providing leadership? Was it more organic bottom up? Was it a sort of a combination? How do you decide who owned what? In other words, you know, did you get, how did you get the business to take ownership of the data and what does owning the data actually mean? >> That's a very good question, Dave. I think that one of the pieces where I think we have a lot of learning and basically if you ask me how we could stop the filling, I think that would be the first piece that we need to start. Really think about how that should be approached. If it's staff has ownership, right? That means somehow that the team has the responsibility to host themselves the data assets to minimum acceptable standards. That's minimum dependencies up and down stream. The interesting piece has to be looking backwards. What was happening is that under that definition, this extra process that we have to go through is not actually transferring ownership from a central team to the other teams, but actually in most cases to establish ownership. I make this difference because saying we have to transfer ownership actually would erroneously suggest that the dataset was owned before, but this platform team, yes, they had the capability to make the change, but actually the analytics team, but always once we had the business understand the use cases and what no one actually bought, it's actually expensive, expected. So we had to go through this very lengthy process and establishing ownership, how we have done that as in the beginning, very naively started, here's a document, here are all the data assets, what is probably the nearest neighbor who can actually take care of that. And then we, we moved it over. But the problem here is that all these things is kind of technical debt, right? It's not really properly documented, pretty unstable. It was built in a very inconsistent way over years. And these people that built this thing have already left the company. So this is actually not a nice thing that you want to see and people build up a certain resistance, even if they have actually bought into this idea of domain ownership. So if you ask me these learnings, what needs to happen is first, the company needs to really understand what our core business concept that we have the need to have this mapping from this other core business concept that we have. These are the domain teams who are owning this concept, and then actually linked that to the, the assets and integrate that better, but suppose understanding how we can evolve, actually the data assets and new data builds things new and the, in this piece and the domain, but also how can we address reduction of technical depth and stabilizing what we have already. >> Thank you for that Christoph. So I want to turn a direction here and talk Clemence about governance. And I know that's an area that's passionate, you're passionate about. I pulled this slide from your deck, which I kind of messed up a little bit, sorry for that. But, but, but by the way, we're going to publish a link to the full video that you guys did. So we'll share that with folks, but it's one of the most challenging aspects of data mesh. If you're going to decentralize, you, you quickly realize this could be the wild west, as we talked about all over again. So how are you approaching governance? There's a lot of items on this slide that are, you know, underscore the complexity, whether it's privacy compliance, et cetera. So, so how did you approach this? >> It's yeah, it's about connecting those dots, right? So the aim of the data governance program is to promote the autonomy of every team while still ensuring that everybody has the right interoperability. So when we want to move from the wild west, riding horses to a civilized way of transport, I can take the example of modern street traffic. Like when all participants can maneuver independently, and as long as they follow the same rules and standards, everybody can remain compatible with each other and understand and learn from each other so we can avoid car crashes. So when I go from country to country, I do understand what the street infrastructure means. How do I drive my car? I can also read the traffic lights and the different signals. So likewise, as a business in HelloFresh we do operate autonomously and consequently need to follow those external and internal rules and standards set forth by the tradition in which we operate. So in order to prevent a, a car crash, we need to at least ensure compliance with regulations, to account for societies and our customers' increasing concern with data protection and privacy. So teaching and advocating this imaging, evangelizing this to everyone in the company was a key community or communication strategy. And of course, I mean, I mentioned data privacy, external factors, the same goes for internal regulations and processes to help our colleagues to adapt for this very new environment. So when I mentioned before, the new way of thinking, the new way of dealing and managing data, this of course implies that we need new processes and regulations for our colleagues as well. In a nutshell, then this means that data governance provides a framework for managing our people, the processes and technology and culture around our data traffic. And that governance must come together in order to have this effective program providing at least a common denominator is especially critical for shared data sets, which we have across our different geographies managed, and shared applications on shared infrastructure and applications. And as then consumed by centralized processes, for example, master data, everything, and all the metrics and KPIs, which are also used for a central steering. It's a big change, right? And our ultimate goal is to have this non-invasive federated, automated and computational governance. And for that, we can't just talk about it. We actually have to go deep and use case by use case and QC by PUC and generate learnings and learnings with the different teams. And this would be a classical approach of identifying the target structure, the target status, match it with the current status, by identifying together with the business teams, with the different domains and have a risk assessment, for example, to increase transparency because a lot of teams, they might not even know what kind of situation they might be. And this is where this training and this piece of data literacy comes into place, where we go in and trade based on the findings, based on the most valuable use case. And based on that, help our teams to do this change, to increase their capability. I just told a little bit more, I wouldn't say hand-holding, but a lot of guidance. >> Can I kind of kind of chime in quickly and (mumbled) below me, I mean, there's a lot of governance piece, but I think that is important. And if you're talking about documentation, for example, yes, we can go from team to team and tell these people, hey, you have to document your data assets and data catalog, or you have to establish a data contract and so on and forth. But if we would like to build data products at scale, following actual governance, we need to think about automation, right? We need to think about a lot of things that we can learn from engineering before, and just starts as simple things. Like if we would like to build up trust in our data products, right? And actually want to apply the same rigor and the best practices that we know from engineering. There are things that we can do. And we should probably think about what we can copy. And one example might be so the level of service level agreements, so that level objectives. So the level of indicators, right, that represent on a, on an engineering level, right? Are we providing services? They're representing the promises we make to our customer and to our consumers. These are the internal objectives that help us to keep those promises. And actually these audits of, of how we are tracking ourselves, how we are doing. And this is just one example of where I think the federated governance, governance comes into play, right? In an ideal world, you should not just talk about data as a product, but also data product that's code. That'd be say, okay, as most, as much as possible, right? Give the engineers the tool that they are familiar with, and actually not ask the product managers, for example, to document the data assets in the data catalog, but make it part of the configuration has as, as a, as a CDCI continuous delivery pipeline, as we typically see in other engineering, tasks through it and services maybe say, okay, there is configuration, we can think about PII, we can think about data quality monitoring, we can think about the ingestion data catalog and so on and forth. But I think ideally in a data product goals become a sort of templates that can be deployed and are actually rejected or verified at build time before we actually make them and deploy them to production. >> Yeah so it's like DevOps for data product. So, so I'm envisioning almost a three-phase approach to governance. And you're kind of, it sounds like you're in the early phase of it, call it phase zero, where there's learning, there's literacy, there's training education, there's kind of self-governance. And then there's some kind of oversight, some, a lot of manual stuff going on, and then you, you're trying to process builders at this phase and then you codify it and then you can automate it. Is that fair? >> Yeah. I would rather think, think about automation as early as possible in a way, and yes, it needs to be separate rules, but then actually start actually use case by use case. Is there anything that small piece that we can already automate? If just possible roll that out at the next extended step-by-step. >> Is there a role though, that adjudicates that? Is there a central, you know, chief state officer who's responsible for making sure people are complying or is it, how do you handle it? >> I mean, from a, from a, from a platform perspective, yes. This applies in to, to implement certain pieces, that we are saying are important and actually would like to implement, however, that is actually working very closely with the governance department, So it's Clemence's piece to understand that defy the policies that needs to be implemented. >> So good. So Clemence essentially, it's, it's, it's your responsibility to make sure that the policy is being followed. And then as you were saying, Christoph, you want to compress the time to automation as fast as possible. Is that, is that-- >> Yeah, so it's a really, it's a, what needs to be really clear is that it's always a split effort, right? So you can't just do one or the other thing, but there is some that really goes hand in hand because for the right information, for the right engineering tooling, we need to have the transparency first. I mean, code needs to be coded. So we kind of need to operate on the same level with the right understanding. So there's actually two things that are important, which is one it's policies and guidelines, but not only that, because more importantly or equally important is to align with the end-user and tech teams and engineering and really bridge between business value business teams and the engineering teams. >> Got it. So just a couple more questions, because we got to wrap up, I want to talk a little bit about the business outcome. I know it's hard to quantify and I'll talk about that in a moment, but, but major learnings, we've got some of the challenges that, that you cited. I'll just put them up here. We don't have to go detailed into this, but I just wanted to share with some folks, but my question, I mean, this is the advice for your peers question. If you had to do it differently, if you had a do over or a Mulligan, as we like to say for you, golfers, what, what would you do differently? >> I mean, I, can we start with, from, from the transformational challenge that understanding that it's also high load of cultural exchange. I think this is, this is important that a particular communication strategy needs to be put into place and people really need to be supported, right? So it's not that we go in and say, well, we have to change into, towards data mash, but naturally it's the human nature, nature, nature, we are kind of resistant to change, right? And (mumbles) uncomfortable. So we need to take that away by training and by communicating. Chris, you might want to add something to that. >> Definitely. I think the point that I've also made before, right? We need to acknowledge that data mesh it's an architectural scale, right? If you're looking for something which is necessary by huge companies who are vulnerable, that are product at scale. I mean, Dave, you mentioned that right, there are a lot of advantages to have a centralized team, but at some point it may make sense to actually decentralize here. And at this point, right, if you think about data mesh, you have to recognize that you're not building something on a green field. And I think there's a big learning, which is also reflected on the slide is, don't underestimate your baggage. It's typically is you come to a point where the old model doesn't work anymore. And as had a fresh write, we lost the trust in our data. And actually we have seen certain risks of slowing down our innovation. So we triggered that, this was triggering the need to actually change something. So at this transition applies that you took, we have a lot of technical depth accumulated over years. And I think what we have learned is that potentially we have, de-centralized some assets too early. This is not actually taking into account the maturity of the team. We are actually investigating too. And now we'll be actually in the face of correcting pieces of that one, right? But I think if you, if you, if you start from scratch, you have to understand, okay, is all my teams actually ready for taking on this new, this new capability? And you have to make sure that this is decentralization. You build up these capabilities and the teams, and as Clemence has mentioned, right? Make sure that you take the, the people on your journey. I think these are the pieces that also here it comes with this knowledge gap, right? That we need to think about hiring literacy, the technical depth I just talked about. And I think the, the last piece that I would add now, which is not here on the slide deck is also from our perspective, we started on the analytical layer because it was kind of where things are exploding, right? This is the bit where people feel the pain. But I think a lot of the efforts that we have started to actually modernize the current stage and data products, towards data mesh, we've understood that it always comes down basically to a proper shape of our operational plan. And I think what needs to happen is I think we got through a lot of pains, but the learning here is this needs to really be an, a commitment from the company. It needs to have an end to end. >> I think that point, that last point you made is so critical because I, I, I hear a lot from the vendor community about how they're going to make analytics better. And that's not, that's not unimportant, but, but true data product thinking and decentralized data organizations really have to operationalize in order to scale it. So these decisions around data architecture and organization, they're fundamental and lasting, it's not necessarily about an individual project ROI. They're going to be projects, sub projects, you know, within this architecture. But the architectural decision itself is organizational it's cultural and, and what's the best approach to support your business at scale. It really speaks to, to, to what you are, who you are as a company, how you operate and getting that right, as we've seen in the success of data-driven companies is, yields tremendous results. So I'll, I'll, I'll ask each of you to give, give us your final thoughts and then we'll wrap. Maybe. >> Just can I quickly, maybe just jumping on this piece, what you have mentioned, right, the target architecture. If you talk about these pieces, right, people often have this picture of (mumbled). Okay. There are different kinds of stages. We have (incomprehensible speech), we have actually a gesture layer, we have a storage layer, transformation layer, presentation data, and then we are basically putting a lot of technology on top of that. That's kind of our target architecture. However, I think what we really need to make sure is that we have these different kinds of views, right? We need to understand what are actually the capabilities that we need to know, what new goals, how does it look and feel from the different kinds of personas and experience view. And then finally that should actually go to the, to the target architecture from a technical perspective. Maybe just to give an outlook what we are planning to do, how we want to move that forward. Yes. Actually based on our strategy in the, in the sense of we would like to increase the maturity as a whole across the entire company. And this is kind of a framework around the business strategy and it's breaking down into four pillars as well. People meaning the data culture, data literacy, data organizational structure and so on. If you're talking about governance, as Clemence had actually mentioned that right, compliance, governance, data management, and so on, you're talking about technology. And I think we could talk for hours for that one it's around data platform, data science platform. And then finally also about enablements through data. Meaning we need to understand data quality, data accessibility and applied science and data monetization. >> Great. Thank you, Christoph. Clemence why don't you bring us home. Give us your final thoughts. >> Okay. I can just agree with Christoph that important is to understand what kind of maturity people have, but I understand we're at the maturity level, where a company, where people, our organization is, and really understand what does kind of, it's just kind of a change applies to that, those four pillars, for example, what needs to be tackled first. And this is not very clear from the very first beginning (mumbles). It's kind of like green field, you come up with must wins to come up with things that you really want to do out of theory and out of different white papers. Only if you really start conducting the first initiatives, you do understand that you are going to have to put those thoughts together. And where do I miss out on one of those four different pillars, people process technology and governance, but, and then that can often the integration like doing step by step, small steps, by small steps, not pulling the ocean where you're capable, really to identify the gaps and see where either you can fill the gaps or where you have to increase maturity first and train people or increase your tech stack. >> You know, HelloFresh is an excellent example of a company that is innovating. It was not born in Silicon Valley, which I love. It's a global company. And, and I got to ask you guys, it seems like it's just an amazing place to work. Are you guys hiring? >> Yes, definitely. We do. As, as mentioned right as well as one of these aspects distributing and actually hiring as an entire company, specifically for data. I think there are a lot of open roles, so yes, please visit or our page from data engineering, data, product management, and Clemence has a lot of roles that you can speak to about. But yes. >> Guys, thanks so much for sharing with theCUBE audience, you're, you're pioneers, and we look forward to collaborations in the future to track progress, and really want to thank you for your time. >> Thank you very much. >> Thank you very much Dave. >> And thank you for watching theCUBE's startup showcase made possible by AWS. This is Dave Volante. We'll see you next time. (cheerful music)

>>Mhm Yes. Hello and welcome back to the cube coverage of red hat summit 2021 Virtual. I'm john furrier host of the Q. We've got a great lineup here. We've got two great guests just bad padan, E. S. V. P. Of cloud platforms at red hat and Tracy ranking VP of open shift engineering at Red Hat folks. Thanks for coming on. Good to see. You got some big news, you guys have made some acquisitions. Uh stack rocks you guys bought into red hat was a really big deal. People want to know, what's the story? How's it going? What's the uptake? What's the integration, how's it going? >>Right, thanks john, thanks for having us on. Um so yeah, we're really excited with stack rocks acquisition being the team on board. Uh Well, the first thing to note before even why we did it uh was for for you and and then the beers have been following us closely. This is our first acquisition as red Hat being part of IBM. So, so, so quite big for us from that perspective as well. Right? Continue to maintain our independence um within uh IBM uh and I really appreciate that way of working together. Um but saying all of that aside, you know, as a company have always been focused on ensuring that were direct enterprise capabilities to just sort of doing that for two decades. With with Lennox, security has always been a big part of our story, right, ensuring that, you know, we're finding cbs updating uh and sending out patches to our customers and doing that in a reliable fashion running mission critical applications. We applied that same if you will um security mindset on the community side with the open ship platform. Um we've invested insecurity ourselves organically, right, you know, uh in various areas and making it more secure, all right, can't run containers uh as Root by default, uh investing in things like role based access control and so on. And we really felt like we want to deepen our commitment to security. Uh and so, you know, in conversations with stack rocks, we found just a great fit, just a great team building a really interesting approach to community security, right? You know, very declared of approach to it. Uh you know, focus on a vision around this notion of shift left. But you've probably been hearing from that because we're a little bit right. Which is this uh idea that, you know, we're in the world moving from devops to death setups. Uh and the approach that sack rocks were saying, so great team, great product, really great vision with regard to kind of weather going forward and finding a nice alignment between, you know what, you know, they've been thinking about the value that we want to bring >>Yeah, I want to dig into the depths cops, piece of it. But you brought up the IBM acquisition as part of now Red Hat bought IBM you know it's just you remember back in 2019 I interviewed Arvin on the cube when he was at IBM you guys were still independent and he had a smile on his face. He is pro cloud, he is all about cloud Native and even that interview I had no idea what was going on behind the scenes but I was kind of drilling him on some of the things that were important at that time which are now certainly relevant today which is cloud Native, Agile development Programmable infrastructure. I don't think we touched on security that much was kind of inherent in the conversation. He was like all smiling, he loves the cloud Native and and this is where it comes into the relevant, I have to ask you, what was it like to get this through? IBM where they're like girl green light or was it, was it different? What was different about this acquisition? >>John great, great question for you to ask. And you know, I will say that, uh, you know, everyone's heard the stories they're telling us. They get, you know, part of IBM, you know, it's definitely working on red hat jOHn the cube we've talked to you and several of your colleagues about that. Um, the great thing has been that, look, the redhead way of working, uh, are still pushing forward with regard to our commitment to open source, uh, and our culture, you know, is still the way it is. And I have to give huge credit not just to urban and his and his team, but definitely to orbit right. He's always champion, He's champion rather acquisition. He's champion kind of, you know, the independence that we've had and he takes very, very firm stance around it. Um, and look, IBM uh, story company uh, in the United States and really in the world, um, they have, there was working and you know, for redhead, they've kind of said, look, we'll give you a pass path, right? So, uh, getting the acquisition through, if you will, diarrhea processes, um, really was, was hugely supported by, you know, from mormon, but all the way down. Russian strategic >>strategic bet with the dollars involved trace, they want to get you in this because, you know, one of the things about shift left and getting security built in by default, which has always been part of red hat, that's never been an issue. It just extends as developers want to have native security built in. There's a technology angle to this as well. So, um, obviously cloud native is super important. What investments are you guys making with this acquisition and how does that translate to customer benefits? >>Yeah, I mean the one thing that is really important about the stock rocks acquisition and kind of, you know, key for us is, you know, this was a cube native solution and I think that's really, you know, was important piece as to why stock rocks might have been, you know, was a great fit for us. Um, and so you know, what we've been trying to do in the short time that that team has been on board with us is really, you know, taken a deep look and understanding where are the intersection points of some of the things that we have been trying to focus on, you know, just with inside of, you know, open shift in red hat in general and where do they have bring the additional value. Um, and really trying to make sure that when we create this solution and ultimately it is a solution that's cohesive across the board. Um, we don't add confusion too. You know what, some of the things that maybe we already do this team knows, you know, how to they know their customer base. They really know what the customers are looking for. And we are just trying to absorb, I would say so much of this information uh as we are trying to, you know, create what the right road map will be uh for stack rocks from a long term and infrared had ultimately in the security space. I mean, as the chef said, I mean we are red hats known for being, you know, security mind focus built on top of realm, you know, uh the leader and so we want to make sure that what we've got that actually serves, you know, the developers being able to not just secure the environment and the platform, but also the workloads, customers need that security from us. Um and build it in so that we have, you know, into the cube native >>controls. >>So stack rocks was known for reinventing and security enterprise security with cloud native. How is it complimentary? How does it fit in? Can you guys just quickly talk to that point because um like you said, you guys had security but as kubernetes and containers in general continue to rise up and and kubernetes continue to become a hybrid cloud kind of linchpin for applications. Um where's the synergy? Where's where does this connect? And what are some of the uh the part of the areas where it's it's fitting in nicely or or any overlaps that you can talk about as well? >>Yeah, I can start and then maybe Tracy if you want to add to that securities of it's a wide space. Right? So, you know, just saying security is like, well, you know what security you're talking about, you're talking about, you know, and use the security, like what your desktop are you talking about? You know, intrusion prevention? I mean, it's a huge, huge, you know, space. Uh you know, many companies devoted to the entire spectrum, you know, self has a very robust security business. We're very focused on uniting Tracy. Was talking about this, the Kubernetes Native security part of this. Right. You know, do we have the appropriate runtime uh, controls in place? Uh You know, our policies configured appropriately Well, if they're in one cluster, are they being applied consistently across, you know, every cluster? How do we make sure that, you know, we make security the domain, not just of the operators but also uh in in uh make it easier for it to be adopted at development time. So, you know, there's a, there's a, if you will, a very sort of uh a lot of surface area for security, we're trying to really think about the pieces that are most relevant for our enterprise customers and the ones that are deploying it at scale. And I'm sure we can build on it. Having said that, john what I do want to add also is that because expands even of Cuban any security is so large, there is a lot of room for our partners to play. Right? And so before you asked me that question, I want to say that there is space. Right? So you know, I've had conversations with you know, all the other folks in the cloud native security space. We know them well, we've been working with them over the years and we could do to look forward to ensure that they're building over and above the foundation of Berlin. >>So plenty of beachhead, what you're saying from a, from a security sample, you guys hit the table stakes added into the product, but there's so much surface area going on with this hybrid cloud and soon to be multi cloud that you're saying this room for partners to play. >>Exactly, right, >>okay. Tracy quick under the hood, you know, actually shift left. That's kind of the mindset for developers who are writing modern applications might not want to get under the hood, who just wanted all the program ability of security and not have to come back to it. I mean that seems to be the complaint that I hear. It's like okay I gotta come back and do a security, more security work. I just wrote the code that was last week or yesterday and that seems to be the developer productivity. Then there's also under the hood devops what how does this all fit? >>Yeah, so it's uh let's take a take a step back and this is how I kind of like to think about it. So we are trying to look at, you know, how do we just enable in some of the C. I. C. D. The tooling that we have? How do we actually take and enable some of the technology that was already available in stock rocks today and actually put it into those tools. Because if we can make it easy for you to not just develop your application and, you know, integrated in with what you're, the tooling is that you're trying to use for the entire life cycle of developing your application. It then becomes exactly what you didn't say, you know, what they're doing now is it's an after thought. We don't need it to be an afterthought. Um and I think, you know, we're seeing the changing from a customer mindset where um they're become customers are becoming a lot more aware of these things. So if we actually get this into, you know, some of the Argo and the ci cd pipe pipeline work, then it just becomes something natural and not a secondary thought because actually when it's a secondary thought, uh we have exposures and that's not what a customer wants when they're creating, you know, creating these workloads, they're trying to rapidly create the workloads, so we need to make it um to have those integration points in as quickly >>as possible. >>Totally nailed. I mean there's productivity issues and there's also the top line which is security. Great stuff. Congratulations on that acquisition. Security continues to be built in from the beginning. That's what people want. They want productivity want want security, great stuff, Great acquisition. Congratulations. Um Next next segment I want to get into is uh open shifts around telemetry. Tell us about telemetry for open shift. What is this about? >>Yeah, another big interesting topic for us. So over a year ago we released open Ship for and you know, we learned a lot of lessons, you know, shipping open ship three up and over the years and really getting feedback from hundreds of customers around the globe. One of the things obviously we heard from a lot was you know, make install the upgrade experience better. Right. But you know, we were thinking about how can we take that forward to the next level, which is is there a way for us to say, you know, let these clusters they connected up so we can get a better sense of cluster help and help with remote health monitoring will be able to proactively provide information back to our customers around, let's say, you know, if applications are healthy clusters healthy and how they're running and how we can help them um could figure them if they're not. Um And so that led us to introducing uh inflammatory remote health monitoring directly into open ship for as a value that we can provide to customers. Um And what that really starts doing is starts bringing this notion of a public cloud, like experience to customers with clusters run across the hybrid cloud. Right? So you have the expectation that, you know, your clusters are monitored and watched over in the public cloud and we want to make sure we can provide that to customers regardless of, you know, where they're running in. So, so that's just >>a quick question on that insights for open shit. That's what you're getting to. Is that on premise? And in the cloud? So it's hybrid environment, is that correct? >>Exactly. Right. So, the insights for open ship is all about that, Right? So how can be proactively, you know, uh identify risk helped remediated? How can we uh do things like, for example, give you recommendations, cost optimization, right insights around around around that. Uh and to your point, right? The goal is to make it completely hybrid. So, it's obviously a new area right for customers want Leslie used to that, you know, in an on premise environment, they're used to that in a public cloud or cloud native environment. And we're trying to make sure we bring that consistently across to our customers, you know, regardless of where they're running apart. >>Tracy. Talk about the the developer productivity involved because if you have telemetry and you have insight into what's going on in the infrastructure and the data, what's going on the application, you can be more proactive, You don't have to get pulled into these rabbit holes of troubleshooting. Oh, is a trace over here or something going on over here. Are clusters going down or should I could have caught that there's a lot of, you know, good intentions with with the code and then all of a sudden new code gets pushed and then also that triggers this to go off and you have all these kind of dependencies, day two operations, many people call this kind of that phenomenon where everything looks good and then you start pushing more stuff more code and then the cluster goes down and then it's like wait, that could have been avoided. That was a dumb error, we could have fixed that this is kind of the basic what I call human software error kind of stuff that's not intended. The telemetry help this area. >>Yeah, it does. And actually one point that even to take it further, that I think it's important is our customers can learn from each other not even having to talk to each other, which is the beauty of what telemetry is and what redhead insights, rope and shift is. You know, what we have been able to see is you know, there are certain characteristics that happen even across, you know, certain groups of customers but they don't know that they don't talk to each other, but the telemetry is giving us a night into what some of those patterns are. And so when a customer in one site starts to have, we start to see telemetry, you know, you know, maybe a. T. D. Is going down for a certain reason and and we can determine that we then have the ability to take that telemetry and you know, be able to send alerts back to all the other customers and say, hey we recognize this might be becoming an issue, You know, here's how you might re mediate it or hey we've already put a fix out for this issue that we're starting to see you having an issue, you should probably take action on. So it's an increasing the the efficiency of customers without them necessarily having to, you know, constantly be understanding, monitoring, you know, watching everything like they had had to do from of the three perspective, we're now giving them some of the insights of what we know as developers back to them, >>you know, that's interesting. I think that's really key because it's talking to a friend last night we just talked about cybersecurity and we're talking about how a lot of these things are patterns that have that are the same and people just don't talk to each other. There's no shared insights. I think this is an interesting dynamic where you can get the collective intelligence of other patterns and then share that. So the question that I mean that's that's a game changer in my opinion. So that's awesome. The question I have is can you guys push alerts and recommendations to the customers? So from this data? So how does that work? Is that built into the product? Can I get some proactive notifications and saying, hey, you know, your cluster might go down and we've seen this before, we've seen this movie. I mean she is that built in. >>Yeah, so john you're keeping it exactly where we're taking this, right? And I think Tracy started putting out some breadcrumbs for you there. So uh, first get comfortable with the foundation was laid out, get clusters connected right. Then information starts going, reported, we start getting exactly to what you said, john write a set of patterns that we can see Tracy, start talking about what we can, if we see pattern on one end, we can go off and help customers on other end. Now, if you take this forward interest for your viewers today, um introduce a I you know, into this, right? And then we can start almost starting to proactive now of saying, look, you know, following actions are going to be committed or we expect them to be committed. You know, here's what the outcome is a result of that. Here's what we recommend for you to do, right? So start proactive remediation along that. So that is exactly, you know, the surface that we're trying to lay down here and I think this is a huge, >>huge game changer. Well, great stuff, want to move on the next we're getting go on for hours on that one topic. I think telemetry is a super important trend. Uh you guys are on top of a great, great job to bring in the Ai piece. I think that's super cool. Let's get back to the end of blocking and tackling Tracy. You know, one of the things that we're seeing with devops as it goes mainstream now, you've got def sec apps in there too, is you've got the infrastructure and you've got the modern application development, modern application developers, just wanna code, be productive, all that security shifting left, everyone's all happy that things are going great under the hood. You have a whole set of developers working on infrastructure. The end of the customers don't want to manage their own infrastructure. How is red hat focused on these two groups? Because you got this SRE like cloud Ops persona developing in the enterprise and you got the developers, it's kind of like almost two worlds coming together, how you, how you helping customers, you know, control their infrastructure and manage it better. >>Yeah, so great question. And you know, this really plays to the strength of what, you know, we have been trying to champion here at red hat for for many years now around the hybrid cloud and this, you know, hopefully everybody's recently heard about the announcement we've made with our new offering Rosa in partnership with amazon. Um you know, we've got different offerings that enables customers to really focus, as you mentioned on the key aspects that they are concerned about, which is how do they drive their businesses, how do they create their applications, their workloads that they need to and offload, you know, the need for having to understand all of the I. T. Infrastructure that's underneath. Um We want to red hat to reduce the operational complexity that customers are having um and give them the ability to really focus on what's important for them. Um how can they be able to scale out their applications, their businesses and continue to add value where they need to have and so um I think it's great we're seeing a huge uptake right now and we've got customers and they understand completely this hybrid cloud model where they're, you know, purchasing open shift um for certain, you know, applications and workloads that they want to run inside their own data centers. And then for those that they know that they don't, you know, don't have to be inside their own data centers. They don't want to have all of that operational complexity. They want to utilize some of the clouds. That's when they're starting to look at other things like rosa or open shift dedicated and and really starting to find the right mix that works well for their business. >>So are you saying that you guys are going to the next level because the previous, I won't say generation but the current situation was okay, you're born in the cloud or you lift and shift to the cloud, You do that manually, then you go on premise to build that cloud operations. Now you're in a hybrid environment. So you're saying if I get this right that you guys are providing automation around standing up in building services on AWS and cloud, public cloud and hybrid, is that kinda what you're getting at? >>Yeah. So the to go to the higher multi cloud world, right? You want platform consistency, right? Running my application running on a platform consistently, you know, where we go. Right. Tracy started talking about this idea of in some cases you say, well I've got the infrastructure team, I've got the ops team, johnny talked about this notion of, well the dwarves can be hard, sometimes right to some groups. Um, and so hey, red hat or hey redhead, plus, you know, my hyper scale of choice, you know, take that off of my hands, Right. Run that for me consistently yourself. Right. So I focused on my application uh and the management of infrastructure is something that's on you Tracy talked about rosa, that's our joint uh first party service that you know, we've got with amazon were directly available in amazon's console, you can go pull that down, right. You'll see red hat open shift on AWS, right on their uh we've got a similar one with Microsoft Azure Tracy mentioned open dedicated, we stand up the platform, we have our own sorry team that manages it with IBM as well as with google. So you pick your cloud of choice and we'll make sure, you know, we'll give you a platform that if you as a customer so choose to self manage. Great, go for it. If you'd like for us to manage it directly ourselves or in conjunction with the cloud provider and provided to you as a native service, you know, we can do that for you as well. Right? So that day to obsolete, you know, challenge that we're talking about. You know, it's something that we can get your hands if you want us to. >>That's really cool. You gotta manage service. They can do it themselves whatever they want. They can do it on public cloud and hybrid. Great stuff. Yeah, I think that's the key. Um, and that's, that's, that's killer. Now, the next question is my favorite. I want to ask you guys both pretend I'm a customer and I'm like, okay, Tracy shit, tell me what's in it for me. What is open shifts and red hat doing for me is the customer? What are you bringing to the table for me? What are you gonna do for me? What is red hat doing for me today? So if you have the kind of bottom line we were in the elevator or probably I ask you, I like what I'm hearing. Why? Why are you cool? Why are you relevant? What's in it for me? >>You >>already start? Okay. Yeah, so I mean I think it's a couple of things that we let's just tie it back to the first initial blend. I mean we've got, we're enabling the customers to choose like where do they want to work that run their workloads, what do they want to focus on? I think that's the first thing. Um we're enabling them to also determine like what workloads do they want to put on there. We continue to expand the workloads that we are providing um capabilities to customers. You know most, you know one of the more recent ones we've had is you know, enablement of Windows containers a huge plus for us. Um, you know, it's just kind of talked about, dropped the buzzword ai you know, recently, you know, we're looking at that, we're talking about, you know, moving workloads need to go to the edge now. It's not just about being in the data centers, so it's about enablement. That's really what open shift as you know, bread and butter is, is, you know, let us, you know, create the ability for you to drive your workloads, whichever, whatever your workloads is, modernize those workloads um, in place them wherever you want to. >>Yes, your your answer. How would you say to that? >>I'll build on what Tracy said, right. She obviously took the, you know, build up tribal Benjamin perspective and I'll sort of talk about a business thing you're introducing, actually add threat at summit. So, you know, we go up and acquire stock rocks, you know, further deepen investment in communities or containment of security. Uh if you recall, john, we've talked to you about, you know, advanced cluster management team that we actually got from IBM incorporate that within red hat, um, to start providing, you know, those capabilities are consistent, you know, cluster policy, immigration management. Um, and you know, in the past we've made an acquisition of Core West, we've got a lot of technology from that incorporated the platform and also things like the quake container registry. What we're introducing address had some it is a way for us to package all of that together. So a customer doesn't say, look, you know, let me pick out a container platform here, let me go find, you know, somebody manage it over there. You let me see, you know what security you adhere. We introduced something called open shift platform plus right. Which is the packaging of, you know, core Open shift contain a platform uh, capabilities within uh, stack rocks, which we're calling advanced cluster security capabilities of cluster management, which is called advanced cluster management. And the quake container registry always want to make it much easier for customers to consume that. And again, you know, the goal is, you know, run that consistently in your hybrid multi club >>chef Tracy. Great, great segment, great insight. Um, here on the cloud platform and open shift under the hood. Uh, you guys are well positioned and I was talking about Arvin and idea who acquired red hat. You know, it's pretty clear that cloud native hybrid is the new cloud operating environment. That's clear. You guys are well positioned. And congratulations. Final question Chef. Take a minute to quickly put the plug in for open shift. What's next? Um, looking forward, what do you guys building on? Um, what's on the roadmap if you can negative share the road map, but yeah, tell us what you're thinking about. I mean you're innovating out in the open, love your shirt by the way and that's the red hat way, looking ahead. What's coming for? Open shift? >>So john I will say this, our roadmap is out in the open every quarter. Our product managers host the session right open to anybody, right? You know, customers prospect, competitors, anybody can can come on. Um, and uh, you hear about our road map, lots of interesting things they're working on uh, as you can imagine investments on the edge front, right? So that's across our portfolio, right on the open shift side, but also on learning platform as well as on the open stack front, make it easier to have, you know, slim down open shift. we'll run that you won't be able to run uh open ship in remote locations and then manage it. Um So expect for us uh you know, just to show you more work there, drinking things like uh ai and more workloads directly onto the platform, but you'll see what they're doing to get more Alex on what we're doing to take uh technologies that we've got called Open data hub to make it easier to run more data intensive, more ai ml types of frameworks directly a platform. Um And so that's a great interest, more workloads Tracy, start talking about that. Right, so Windows containers, support has G eight, uh and what's really awesome about that is that we've done that with Microsoft, right, so that offering is jointly supported by both us and our partners over at Microsoft uh virtualization, which is taking much machines and being able to run them as dangerous orchestrated by communities Um, and and doing more work, you know, on that front as well. So just a lot of different areas uh, were investigated and really, really excited to bring more workloads on 2:00. >>Well, Chef Tracy, great segment with a lot of data in there. Thanks for spending time in and providing that insight and uh, sharing the information. A lot of flowers blooming um, here in the cloud native environment, a lot of action. A lot of new stuff going on. Love the shift left. I think that's super relevant. You guys do a great job. Thanks for coming on. I appreciate it. >>Okay. >>This the cubes coverage of red hat summit. I'm john for a host of the cube. Thank you for watching.

>>from around the globe. It's the Cube with coverage of Yukon and Cloud. Native Con North America 2020 Virtual brought to you by Red Hat The Cloud, Native Computing Foundation and Ecosystem Partners. Hello, everyone. And welcome back to the cubes Ongoing coverage of Cuba con North America. Joe Fernandez is here. He's with Stephanie, Cheras and Joe's, the V, P and GM for core cloud platforms. That red hat and Stephanie is this s VP and GM of the Red Hat Enterprise. Lennox bu. Two great friends of the Cube. Awesome seeing you guys. How you doing? >>It's great to be here, Dave. Yeah, thanks >>for the opportunity. >>Hey, so we all talked, you know, recently, uh, answerable fest Seems like a while ago, but But we talked about what's new? Red hat really coming at it from an automation perspective. But I wonder if we could take a view from open shift and what's new from the standpoint of you really focus on helping customers, you know, change their operations and operationalize. And Stephanie, Maybe you could start, and then, you know, Joe, you could bring in some added color. >>No, that's great. And I think you know one of the things we try and do it. Red hat clearly building off of open source. We have been focused on this open hybrid cloud strategy for, you know, really years. Now the beauty of it is that hybrid cloud and open hybrid cloud continues to evolve right with bringing in things like speed and stability and scale and now adding in other footprints, like manage services as well as edge and pulling that all together across the whole red hat portfolio from the platforms, right? Certainly with Lennox and roll into open shift in the platform with open shift and then adding automation, which certainly you need for scale. But it's ah, it's continues to evolve as the as the definition of open hybrid cloud evolves. >>Great. So thank you, Stephanie jokes. You guys got hard news here that you could maybe talk about 46? >>Yeah. Eso eso open shift is our enterprise kubernetes platform. With this announcement, we announced the release of open ship 4.6 Eso eso We're doing releases every quarter tracking the upstream kubernetes release cycle. So this brings communities 1.19, which is, um but itself brings a number of new innovations, some specific things to call out. We have this new automated installer for open shift on bare metal, and that's definitely a trend that we're seeing is more customers not only looking at containers but looking at running containers directly on bare metal environments. Open shift provides an abstraction, you know, which combines Cuban. And he's, uh, on top of Lennox with RL. I really across all environments, from bare metal to virtualization platforms to the various public clouds and out to the edge. But we're seeing a lot of interest in bare metal. This is basically increasing the really three automation to install seamlessly and manage upgrades in those environments. We're also seeing a number of other enhancements open shifts service mesh, which is our SDO based solution for managing, uh, the interactions between micro services being able to manage traffic against those services. Being able to do tracing. We have a new release of that on open shift Ford out six on then, um, some work specific to the public cloud that we started extending into the government clouds. So we already supported AWS and Azure. With this release, we added support for the A W s government cloud as well. Azaz Acela's Microsoft Azure government on dso again This is really important to like our public sector customers who are looking to move to the public cloud leveraging open shift as an abstraction but wanted thio support it on the specialized clouds that they need to use with azure gonna meet us Cup. >>So, joke, we stay there for a minute. So so bare metal talking performance there because, you know, you know what? You really want to run fast, right? So that's the attractiveness there. And then the point about SDO in the open, open shift service measure that makes things simpler. Maybe talk a little bit about sort of business impact and what customers should expect to get out of >>these two things. So So let me take them one at a time, right? So so running on bare metal certainly performances a consideration. You know, I think a lot of fixed today are still running containers, and Cuban is on top of some form of virtualization. Either a platform like this fear or open stack, or maybe VMS in the in one of the public clouds. But, you know containers don't depend on a virtualization layer. Containers only depend on Lennox and Lennox runs great on bare metal. So as we see customers moving more towards performance and Leighton see sensitive workloads, they want to get that Barry mental performance on running open shift on bare metal and their containerized applications on that, uh, platform certainly gives them that advantage. Others just want to reduce the cost right. They want to reduce their VM sprawl, the infrastructure and operational cost of managing avert layer beneath their careers clusters. And that's another benefit. So we see a lot of uptake in open shift on bare metal on the service match side. This is really about You know how we see applications evolving, right? Uh, customers are moving more towards these distributed architectures, taking, you know, formally monolithic or enter applications and splitting them out into ah, lots of different services. The challenge there becomes. Then how do you manage all those connections? Right, Because something that was a single stack is now comprised of tens or hundreds of services on DSO. You wanna be able to manage traffic to those services, so if the service goes down, you can redirect that those requests thio to an alternative or fail over service. Also tracing. If you're looking at performance issues, you need to know where in your architecture, er you're having those degradations and so forth. And, you know, those are some of the challenges that people can sort of overcome or get help with by using service mash, which is powered by SDO. >>And then I'm sorry, Stephanie ever get to in a minute. But which is 11 follow up on that Joe is so the rial differentiation between what you bring in what I can just if I'm in a mono cloud, for instance is you're gonna you're gonna bring this across clouds. I'm gonna You're gonna bring it on, Prem And we're gonna talk about the edge in in a minute. Is that right? From a differentiation standpoint, >>Yeah, that That's one of the key >>differentiations. You know, Read has been talking about the hybrid cloud for a long time. We've we've been articulating are open hybrid cloud strategy, Andi, >>even if that's >>not a strategy that you may be thinking about, it is ultimately where folks end up right, because all of our enterprise customers still have applications running in the data center. But they're also all starting to move applications out to the public cloud. As they expand their usage of public cloud, you start seeing them adopted multi cloud strategies because they don't want to put all their eggs in one basket. And then for certain classes of applications, they need to move those applications closer to the data. And and so you start to see EJ becoming part of that hybrid cloud picture on DSO. What we do is basically provide a consistency across all those environments, right? We want run great on Amazon, but also great on Azure on Google on bare metal in the data center during medal out at the edge on top of your favorite virtualization platform. And yeah, that that consistency to take a set of applications and run them the same way across all those environments. That is just one of the key benefits of going with red hat as your provider for open hybrid cloud solutions. >>All right, thank you. Stephanie would come back to you here, so I mean, we talk about rail a lot because your business unit that you manage, but we're starting to see red hats edge strategy unfolded. Kind of real is really the linchpin I wanna You could talk about how you're thinking about the edge and and particularly interested in how you're handling scale and why you feel like you're in a good position toe handle that massive scale on the requirements of the edge and versus hey, we need a new OS for the edge. >>Yeah, I think. And Joe did a great job of said and up it does come back to our view around this open hybrid cloud story has always been about consistency. It's about that language that you speak, no matter where you want to run your applications in between rela on on my side and Joe with open shift and and of course, you know we run the same Lennox underneath. So real core os is part of open shift that consistently see leads to a lot of flexibility, whether it's through a broad ecosystem or it's across footprints. And so now is we have been talking with customers about how they want to move their applications closer to data, you know, further out and away from their data center. So some of it is about distributing your data center, getting that compute closer to the data or closer to your customers. It drives, drives some different requirements right around. How you do updates, how you do over the air updates. And so we have been working in typical red hat fashion, right? We've been looking at what's being done in the upstream. So in the fedora upstream community, there is a lot of working that has been done in what's called the I. O. T Special Interest group. They have been really investigating what the requirements are for this use case and edge. So now we're really pleased in, um, in our most recent release of really aid relate 00.3. We have put in some key capabilities that we're seeing being driven by these edge use cases. So things like How do you do quick image generation? And that's important because, as you distribute, want that consistency created tailored image, be able to deploy that in a consistent way, allow that to address scale, meet security requirements that you may have also right updates become very important when you start to spread this out. So we put in things in order to allow remote device mirroring so that you can put code into production and then you can schedule it on those remote devices toe happen with the minimal disruption. Things like things like we all know now, right with all this virtual stuff, we often run into things like not ideal bandwidth and sometimes intermittent connectivity with all of those devices out there. So we put in, um, capabilities around, being able to use something called rpm Austria, Um, in order to be able to deliver efficient over the air updates. And then, of course, you got to do intelligent rollbacks for per chance that something goes wrong. How do you come back to a previous state? So it's all about being able to deploy at scale in a distributed way, be ready for that use case and have some predictability and consistency. And again, that's what we build our platforms for. It's all about predictability and consistency, and that gives you flexibility to add your innovation on top. >>I'm glad you mentioned intelligent rollbacks I learned a long time ago. You always ask the question. What happens when something goes wrong? You learn a lot from the answer to that, but You know, we talk a lot about cloud native. Sounds like you're adapting well to become edge native. >>Yeah. I mean, I mean, we're finding whether it's inthe e verticals, right in the very specific use cases or whether it's in sort of an enterprise edge use case. Having consistency brings a ton of flexibility. It was funny, one of our talking with a customer not too long ago. And they said, you know, agility is the new version of efficiency. So it's that having that sort of language be spoken everywhere from your core data center all the way out to the edge that allows you a lot of flexibility going forward. >>So what if you could talk? I mentioned just mentioned Cloud Native. I mean, I think people sometimes just underestimate the effort. It takes tow, make all this stuff run in all the different clouds the engineering efforts required. And I'm wondering what kind of engineering you do with if any with the cloud providers and and, of course, the balance of the ecosystem. But But maybe you could describe that a little bit. >>Yeah, so? So Red Hat works closely with all the major cloud providers you know, whether that's Amazon, Azure, Google or IBM Cloud. Obviously, Andi, we're you know, we're very keen on sort of making sure that we're providing the best environment to run enterprise applications across all those environments, whether you're running it directly just with Lennox on Ralph or whether you're running it in a containerized environment with Open Chef, which which includes route eso eso, our partnership includes work we do upstream, for example. You know, Red Hat help. Google launched the Cuban community, and I've been, you know, with Google. You know, we've been the top two contributors driving that product that project since inception, um, but then also extends into sort of our hosted services. So we run a jointly developed and jointly managed service called the Azure Red Hat Open Shift Service. Together with Microsoft were our joint customers can get access to open shift in an azure environment as a native azure service, meaning it's, you know, it's fully integrated, just like any other. As your service you can tied into as you're building and so forth. It's sold by by Azure Microsoft's sales reps. Um, but you know, we get the benefit of working together with our Microsoft counterparts and developing that service in managing that service and then in supporting our joint customers. We over the summer announced sort of a similar partnership with Amazon and we'll be launching are already doing pilots on the Amazon Red Hat Open ship service, which is which is, you know, the same concept now applied to the AWS cloud. So that will be coming out g a later this year, right? But again, whether it's working upstream or whether it's, you know, partnering on managed services. I know Stephanie team also do a lot of work with Microsoft, for example, on sequel server on Lenox dot net on Lenox. Whoever thought be running that applications on Linux. But that's, you know, a couple of years old now, a few years old, So eso again. It's been a great partnership, not just with Microsoft, but with all the cloud providers. >>So I think you just shared a little little He showed a little leg there, Joe, what's what's coming g A. Later this year. I want to circle back to >>that. Yeah, eso we way announced a preview earlier this year of of the Amazon Red Hat Open ships service. It's not generally available yet. We're you know, we're taking customers. We want toe, sort of be early access, get access to pilots and then that'll be generally available later this year. Although Red Hat does manage our own service Open ship dedicated that's available on AWS today. But that's a service that's, you know, solely, uh, operated by Red Hat. This new service will be jointly operated by Red Hat and Amazon together Idea. That would be sort of a service that we are delivering together as partners >>as a managed service and and okay, so that's in beta now. I presume if it's gonna be g a little, it's >>like, Yeah, that's yeah, >>that's probably running on bare metal. I would imagine that >>one is running >>on E. C. Two. That's running an A W C C T V exactly, and >>run again. You know, all of our all of >>our I mean, we you know, that open shift does offer bare metal cloud, and we do you know, we do have customers who can take the open shift software and deploy it there right now are managed. Offering is running on top of the C two and on top of Azure VM. But again, this is this is appealing to customers who, you know, like what we bring in terms of an enterprise kubernetes platform, but don't wanna, you know, operated themselves, right? So it's a fully managed service. You just come and build and deploy your APS, and then we manage all of the infrastructure and all the underlying platform for you >>that's going to explode. My prediction. Um, let's take an example of heart example of security. And I'm interested in how you guys ensure a consistent, you know, security experience across all these locations on Prem Cloud. Multiple clouds, the edge. Maybe you could talk about that. And Stephanie, I'm sure you have a perspective on this is Well, from the standpoint of of Ralph. So who wants to start? >>Yeah, Maybe I could start from the bottom and then I'll pass it over to Joe to talk a bit. I think one of these aspects about security it's clearly top of mind of all customers. Um, it does start with the very bottom and base selection in your OS. We continue to drive SC Lennox capabilities into rural to provide that foundational layer. And then as we run real core OS and open shift, we bring over that s C Lennox capability as well. Um, but, you know, there's a whole lot of ways to tackle this we've done. We've done a lot around our policies around, um see ve updates, etcetera around rail to make sure that we are continuing to provide on DCA mitt too. Mitigating all critical and importance, providing better transparency toe how we assess those CVS. So security is certainly top of mind for us. And then as we move forward, right there's also and joke and talk about the security work we do is also capabilities to do that in container ization. But you know, we we work. We work all the way from the base to doing things like these images in these easy to build images, which are tailored so you can make them smaller, less surface area for security. Security is one of those things. That's a lifestyle, right? You gotta look at it from all the way the base in the operating system, with things like sc Lennox toe how you build your images, which now we've added new capabilities. There And then, of course, in containers. There's, um there's a whole focus in the open shift area around container container security, >>Joe. Anything you want to add to that? >>Yeah, sure. I >>mean, I think, you know, obviously, Lennox is the foundation for, you know, for all public clouds. It's it's driving enterprise applications in the data center, part of keeping those applications. Security is keeping them up to date And, you know, through, you know, through real, we provide, you know, securing up to date foundation as a Stephanie mentioned as you move into open shift, you're also been able to take advantage of, uh, Thio to take advantage of essentially mutability. Right? So now the application that you're deploying isn't immutable unit that you build once as a container image, and then you deploy that out all your various environments. When you have to do an update, you don't go and update all those environments. You build a new image that includes those updates, and then you deploy those images out rolling fashion and, as you mentioned that you could go back if there's issues. So the idea, the notion of immutable application deployments has a lot to do with security, and it's enabled by containers. And then, obviously you have cured Panetti's and, you know, and all the rest of our capabilities as part of open Shift managing that for you. We've extended that concept to the entire platform. So Stephanie mentioned, real core West Open shift has always run on real. What we have done in open shift for is we've taken an immutable version of Ralph. So it's the same red hat enterprise Lennox that we've had for years. But now, in this latest version relate, we have a new way to package and deploy it as a relic or OS image, and then that becomes part of the platform. So when customers want toe in addition to keeping their applications up to date, they need to keep their platform up to dates. Need to keep, you know, up with the latest kubernetes patches up with the latest Lennox packages. What we're doing is delivering that as one platform, so when you get updates for open shift, they could include updates for kubernetes. They could include updates for Lennox itself as well as all the integrated services and again, all of this is just you know this is how you keep your applications secure. Is making sure your you know, taking care of that hygiene of, you know, managing your vulnerabilities, keeping everything patched in up to date and ultimately ensuring security for your application and users. >>I know I'm going a little bit over, but I have I have one question that I wanna ask you guys and a broad question about maybe a trends you see in the business. I mean, you look at what we talk a lot about cloud native, and you look at kubernetes and the interest in kubernetes off the charts. It's an area that has a lot of spending momentum. People are putting resource is behind it. But you know, really, to build these sort of modern applications, it's considered state of the art on. Do you see a lot of people trying to really bring that modern approach toe any cloud we've been talking about? EJ. You wanna bring it also on Prem And people generally associate this notion of cloud native with this kind of elite developers, right? But you're bringing it to the masses and there's 20 million plus software developers out there, and most you know, with all due respect that you know they may not be the the the elites of the elite. So how are you seeing this evolve in terms of re Skilling people to be able, handle and take advantage of all this? You know, cool new stuff that's coming out. >>Yeah, I can start, you know, open shift. Our focus from the beginning has been bringing kubernetes to the enterprise. So we think of open shift as the dominant enterprise kubernetes platform enterprises come in all shapes and sizes and and skill sets. As you mentioned, they have unique requirements in terms of how they need toe run stuff in their data center and then also bring that to production, whether it's in the data center across the public clouds eso So part of it is, you know, making sure that the technology meets the requirements and then part of it is working. The people process and and culture thio make them help them understand what it means to sort of take advantage of container ization and cloud native platforms and communities. Of course, this is nothing new to red hat, right? This is what we did 20 years ago when we first brought Lennox to the Enterprise with well, right on. In essence, Carozza is basically distributed. Lennox right Kubernetes builds on Lennox and brings it out to your cluster to your distributed systems on across the hybrid cloud. So So nothing new for Red Hat. But a lot of the same challenges apply to this new cloud native world. >>Awesome. Stephanie, we'll give you the last word, >>all right? And I think just a touch on what Joe talked about it. And Joe and I worked really closely on this, right? The ability to run containers right is someone launches down this because it is magical. What could be done with deploying applications? Using a container technology, we built the capabilities and the tools directly into rural in order to be able to build and deploy, leveraging things like pod man directly into rural. And that's exactly so, folks. Everyone who has a real subscription today can start on their container journey, start to build and deploy that, and then we work to help those skills then be transferrable as you movinto open shift in kubernetes and orchestration. So, you know, we work very closely to make sure that the skills building can be done directly on rail and then transfer into open shift. Because, as Joe said, at the end of the day, it's just a different way to deploy. Lennox, >>You guys are doing some good work. Keep it up. And thanks so much for coming back in. The Cube is great to talk to you today. >>Good to see you, Dave. >>Yes, Thank you. >>All right. Thank you for watching everybody. The cubes coverage of Cuba con en a continues right after this.

>> Announcer: From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> As you may recall, coming into the second part of 2019 we reported, based on ETR Survey data, that there was a narrowing of spending on emerging tech and an unplugging of a lot of legacy systems. This was really because people were going from experimentation into operationalizing their digital initiatives. When COVID hit, conventional wisdom suggested that there would be a flight to safety. Now, interestingly, we reported with Eric Bradley, based on one of the Venns, that a lot of CIOs were still experimenting with emerging vendors. But this was very anecdotal. Today, we have more data, fresh data, from the ETR Emerging Technology Study on private companies, which really does suggest that there's a notable decline in experimentation, and that's affecting emerging technology vendors. Hi, everybody, this is Dave Vellante, and welcome to this week's Wikibon Cube Insights, powered by ETR. Once again, Sagar Kadakia is joining us. Sagar is the Director of Research at ETR. Sagar, good to see you. Thanks for coming on. >> Good to see you again. Thanks for having me, Dave. >> So, it's really important to point out, this Emerging Tech Study that you guys do, it's different from your quarterly Technology Spending Intention Survey. Take us through the methodology. Guys, maybe you could bring up the first chart. And, Sagar, walk us through how you guys approach this. >> No problem. So, a lot of the viewers are used to seeing a lot of the results from the Technology Spending Intention Survey, or the TSIS, as we call it. That study, as the title says, it really tracks spending intentions on more pervasive vendors, right, Microsoft, AWS, as an example. What we're going to look at today is our Emerging Technology Study, which we conduct biannually, in May and November. This study is a little bit different. We ask CIOs around evaluations, awareness, planned evaluations, so think of this as pre-spend, right. So that's a major differentiator from the TSIS. That, and this study, really focuses on private emerging providers. We're really only focused on those really emerging private companies, say, like your Series B to Series G or H, whatever it may be, so, two big differences within those studies. And then today what we're really going to look at is the results from the Emerging Technology Study. Just a couple of quick things here. We had 811 CIOs participate, which represents about 380 billion in annual IT spend, so the results from this study matter. We had almost 75 Fortune 100s take it. So, again, we're really measuring how private emerging providers are doing in the largest organizations. And so today we're going to be reviewing notable sectors, but largely this survey tracks roughly 356 private technologies and frameworks. >> All right, guys, bring up the pie chart, the next slide. Now, Sagar, this is sort of a snapshot here, and it basically says that 44% of CIOs agree that COVID has decreased the organization's evaluation and utilization of emerging tech, despite what I mentioned, Eric Bradley's Venn, which suggested one CIO in particular said, "Hey, I always pick somebody in the lower left "of the magic quadrant." But, again, this is a static view. I know we have some other data, but take us through this, and how this compares to other surveys that you've done. >> No problem. So let's start with the high level takeaways. And I'll actually kind of get into to the point that Eric was debating, 'cause that point is true. It's just really how you kind of slice and dice the data to get to that. So, what you're looking at here, and what the overall takeaway from the Emerging Technology Study was, is, you know, you are going to see notable declines in POCs, of proof-of-concepts, any valuations because of COVID-19. Even though we had been communicating for quite some time, you know, the last few months, that there's increasing pressure for companies to further digitize with COVID-19, there are IT budget constraints. There is a huge pivot in IT resources towards supporting remote employees, a decrease in risk tolerance, and so that's why what you're seeing here is a rather notable number of CIOs, 44%, that said that they are decreasing their organization's evaluation and utilization of private emerging providers. So that is notable. >> Now, as you pointed out, you guys run this survey a couple of times a year. So now let's look at the time series. Guys, if you bring up the next chart. We can see how the sentiment has changed since last year. And, of course, we're isolating here on some of larger companies. So, take us through what this data means. >> No problem. So, how do we quantify what we just saw in the prior slide? We saw 44% of CIOs indicating that they are going to be decreasing their evaluations. But what exactly does that mean? We can pretty much determine that by looking at a lot of the data that we captured through our Emerging Technology Study. There's a lot going on in this slide, but I'll walk you through it. What you're looking at here is Fortune 1000 organizations, so we've really isolated the data to those organizations that matter. So, let's start with the teal, kind of green line first, because I think it's a little bit easier to understand. What you're looking at, Fortune 1000 evaluations, both planned and current, okay? And you're looking at a time series, one year ago and six months ago. So, two of the answer options that we provide CIOs in this survey, right, think about the survey as a grid, where you have seven answer options going horizontally, and then 300-plus vendors and technologies going vertically. For any given vendor, they can essentially indicate one of these options, two of them being on currently evaluating them or I plan to evaluate them in six months. So what you're looking at here is effectively the aggregate number, or the average number of Fortune 1000 evaluations. So if you look into May 2019, all the way on the left of that chart, that 24% roughly means that a quarter of selections made by Fortune 1000 of the survey, they selected plan to evaluate or currently evaluating. If you fast-forward six months, to the middle of the chart, November '19, it's roughly the same, one in four technologies that are Fortune 1000 selected, they indicated that I plan or am currently evaluating them. But now look at that big drop off going into May 2020, the 17%, right? So now one out of every six technologies, or one out of every selections that they made was an evaluation. So a very notable drop. And then if you look at the blue line, this is another answer option that we provided CIOs: I'm aware of the technology but I have no plans to evaluate. So this answer option essentially tracks awareness levels. If you look at the last six months, look at that big uptick from 44% to over 50%, right? So now, essentially one out of every two technologies, or private technologies that a CIO is aware of, they have no plans to evaluate. So this is going to have an impact on the general landscape, when we think about those private emerging providers. But there is one caveat, and, Dave, this is what you mentioned earlier, this is what Eric was talking about. The providers that are doing well are the ones that are work-from-home aligned. And so, just like a few years ago, we were really analyzing results based on are you cloud-native or are you Cloud-aligned, because those technologies are going to do the best, what we're seeing in the emerging space is now the same thing. Those emerging providers that enable organizations to maintain productivity for their employees, essentially allowing their employees to work remotely, those emerging providers are still doing well. And that is probably the second biggest takeaway from this study. >> So now what we're seeing here is this flight to perceive safety, which, to your point, Sagar, doesn't necessarily mean good news for all enterprise tech vendors, but certainly for those that are positioned for the work-from-home pivot. So now let's take a look at a couple of sectors. We'll start with information security. We've reported for years about how the perimeter's been broken down, and that more spend was going to shift from inside the moat to a distributed network, and that's clearly what's happened as a result of COVID. Guys, if you bring up the next chart. Sagar, you take us through this. >> No problem. And as you imagine, I think that the big theme here is zero trust. So, a couple of things here. And let me just explain this chart a little bit, because we're going to be going through a couple of these. What you're seeing on the X-axis here, is this is effectively what we're classifying as near term growth opportunity from all customers. The way we measure that effectively is we look at all the evaluations, current evaluations, planned evaluations, we look at people who are evaluated and plan to utilize these vendors. The more indications you get on that the more to the top right you're going to be. The more indications you get around I'm aware of but I don't plan to evaluate, or I'm replacing this early-stage vendor, the further down and on the left you're going to be. So, on the X-axis you have near term growth opportunity from all customers, and on the Y-axis you have near term growth opportunity from, really, the biggest shops in the world, your Global 2000, your Forbes Private 225, like Cargill, as an example, and then, of course, your federal agencies. So you really want to be positioned up and to the right here. So, the big takeaway here is zero trust. So, just a couple of things on this slide when we think about zero trust. As organizations accelerate their Cloud and Saas spend because of COVID-19, and, you know, what we were talking about earlier, Dave, remote work becomes the new normal, that perimeter security approach is losing appeal, because the perimeter's less defined, right? Apps and data are increasingly being stored in the Cloud. That, and employees are working remotely from everywhere, and they're accessing all of these items. And so what we're seeing now is a big move into zero trust. So, if we look at that chart again, what you're going to see in that upper right quadrant are a lot of identity and access management players. And look at the bifurcation in general. This is what we were talking about earlier in terms of the landscape not doing well. Most security vendors are in that red area, you know, in the middle to the bottom. But if you look at the top right, what are you seeing here? Unify ID, Auth0, WSO2, right, all identity and access management players. These are critical in your zero trust approach, and this is one of the few area where we are seeing upticks. You also see here BitSight, Lucideus. So that's going to be security assessment. You're seeing VECTRA and Netskope and Darktrace, and a few others here. And Cloud Security and IDPS, Intrusion Detection and Prevention System. So, very few sectors are seeing an uptick, very few security sectors actually look pretty good, based on opportunities that are coming. But, essentially, all of them are in that work-from-home aligned security stack, so to speak. >> Right, and of course, as we know, as we've been reporting, buyers have options, from both established companies and these emerging companies that are public, Okta, CrowdStrike, Zscaler. We've seen the work-from-home pivot benefit those guys, but even Palo Alto Networks, even CISCO, I asked (other speaker drowns out speech) last week, I said, "Hey, what about this pivot to work from home? "What about this zero trust?" And he said, "Look, the reality is, yes, "a big part of our portfolio is exposed "to that traditional infrastructure, "but we have options for zero trust as well." So, from a buyer's standpoint, that perceived flight to safety, you have a lot of established vendors, and that clearly is showing up in your data. Now, the other sector that we want to talk about is database. We've been reporting a lot on database, data warehouse. So, why don't you take us through the next graphic here, if you would. >> Sagar: No problem. So, our theme here is that Snowflake is really separating itself from the pack, and, again, you can see that here. Private database and data warehousing vendors really continue to impact a lot of their public peers, and Snowflake is leading the way. We expect Snowflake to gain momentum in the next few years. And, look, there's some rumors that IPOing soon. And so when we think about that set-up, we like it, because as organizations transition away from hybrid Cloud architectures to 100% or near-100% public Cloud, Snowflake is really going to benefit. So they look good, their data stacks look pretty good, right, that's resiliency, redundancy across data centers. So we kind of like them as well. Redis Labs bring a DB and they look pretty good here on the opportunity side, but we are seeing a little bit of churn, so I think probably Snowflake and DataStax are probably our two favorites here. And again, when you think about Snowflake, we continue to think more pervasive vendors, like Paradata and Cloudera, and some of the other larger database firms, they're going to continue seeing wallet and market share losses due to some of these emerging providers. >> Yeah. If you could just keep that slide up for a second, I would point out, in many ways Snowflake is kind of a safer bet, you know, we talk about flight to safety, because they're well-funded, they're established. You can go from zero to Snowflake very quickly, that's sort of their mantra, if you will. But I want to point out and recognize that it is somewhat oranges and tangerines here, Snowflake being an analytical database. You take MariaDB, for instance, I look at that, anyway, as relational and operational. And then you mentioned DataStax. I would say Couchbase, Redis Labs, Aerospike. Cockroach is really a... EValue Store. You've got some non-relational databases in there. But we're looking at the entire sector of databases, which has become a really interesting market. But again, some of those established players are going to do very well, and I would put Snowflake on that cusp. As you pointed out, Bloomberg broke the story, I think last week, that they were contemplating an IPO, which we've known for a while. >> Yeah. And just one last thing on that. We do like some of the more pervasive players, right. Obviously, AWS, all their products, Redshift and DynamoDB. Microsoft looks really good. It's just really some of the other legacy ones, like the Teradatas, the Oracles, the Hadoops, right, that we are going to be impacted. And so the claw providers look really good. >> So, the last decade has really brought forth this whole notion of DevOps, infrastructure as code, the whole API economy. And that's the piece we want to jump into now. And there are some real stand-outs here, you know, despite the early data that we showed you, where CIOs are less prone to look at emerging vendors. There are some, for instance, if you bring up the next chart, guys, like Hashi, that really are standing out, aren't they? >> That's right, Dave. So, again, what you're seeing here is you're seeing that bifurcation that we were talking about earlier. There are a lot of infrastructure software vendors that are not positioned well, but if you look at the ones at the top right that are positioned well... We have two kind of things on here, starting with infrastructure automation. We think a winner here is emerging with Terraform. Look all the way up to the right, how well-positioned they are, how many opportunities they're getting. And for the second straight survey now, Terraform is leading along their peers, Chef, Puppet, SaltStack. And they're leading their peers in so many different categories, notably on allocating more spend, which is obviously very important. For Chef, Puppet and SaltStack, which you can see a little bit below, probably a little bit higher than the middle, we are seeing some elevator churn levels. And so, really, Terraform looks like they're kind of separating themselves. And we've got this great quote from the CIO just a few months ago, on why Terraform is likely pulling away, and I'll read it out here quickly. "The Terraform tool creates "an entire infrastructure in a box. "Unlike vendors that use procedural languages, "like Ants, Bull and Chef, "it will show you the infrastructure "in the way you want it to be. "You don't have to worry about "the things that happen underneath." I know some companies where you can put your entire Amazon infrastructure through Terraform. If Amazon disappears, if your availability drops, load balancers, RDS, everything, you just run Terraform and everything will be created in 10 to 15 minutes. So that shows you the power of Terraform and why we think it's ranked better than some of the other vendors. >> Yeah, I think that really does sum it up. And, actually, guys, if you don't mind bringing that chart back up again. So, a point out, so, Mitchell Hashimoto, Hashi, really, I believe I'm correct, talking to Stu about this a little bit, he sort of led the Terraform project, which is an Open Source project, and, to your point, very easy to deploy. Chef, Puppet, Salt, they were largely disrupted by Cloud, because they're designed to automate deployment largely on-prem and DevOps, and now Terraform sort of packages everything up into a platform. So, Hashi actually makes money, and you'll see it on this slide, and things, Vault, which is kind of their security play. You see GitLab on here. That's really application tooling to deploy code. You see Docker containers, you know, Docker, really all about open source, and they've had great adoption, Docker's challenge has always been monetization. You see Turbonomic on here, which is application resource management. You can't go too deep on these things, but it's pretty deep within this sector. But we are comparing different types of companies, but just to give you a sense as to where the momentum is. All right, let's wrap here. So maybe some final thoughts, Sagar, on the Emerging Technology Study, and then what we can expect in the coming month here, on the update in the Technology Spending Intention Study, please. >> Yeah, no problem. One last thing on the zero trust side that has been a big issue that we didn't get to cover, is VPN spend. Our data is pointing that, yes, even though VPN spend did increase the last few months because of remote work, we actually think that people are going to move away from that as they move onto zero trust. So just one last point on that, just in terms of overall thoughts, you know, again, as we cover it, you can see how bifurcated all these spaces are. Really, if we were to go sector by sector by sector, right, storage and block chain and MLAI and all that stuff, you would see there's a few or maybe one or two vendors doing well, and the majority of vendors are not seeing as many opportunities. And so, again, are you work-from-home aligned? Are you the best vendor of all the other emerging providers? And if you fit those two criteria then you will continue seeing POCs and evaluations. And if you don't fit that criteria, unfortunately, you're going to see less opportunities. So think that's really the big takeaway on that. And then, just in terms of next steps, we're already transitioning now to our next Technology Spending Intention Survey. That launched last week. And so, again, we're going to start getting a feel for how CIOs are spending in 2H-20, right, so, for the back half of the year. And our question changes a little bit. We ask them, "How do you plan on spending in the back half year "versus how you actually spent "in the first half of the year, or 1H-20?" So, we're kind of, tighten the screw, so to speak, and really getting an idea of what's spend going to look like in the back half, and we're also going to get some updates as it relates to budget impacts from COVID-19, as well as how vendor-relationships have changed, as well as business impacts, like layoffs and furloughs, and all that stuff. So we have a tremendous amount of data that's going to be coming in the next few weeks, and it should really prepare us for what to see over the summer and into the fall. >> Yeah, very excited, Sagar, to see that. I just wanted to double down on what you said about changes in networking. We've reported with you guys on NPLS networks, shifting to SD-WAN. But even VPN and SD-WAN are being called into question as the internet becomes the new private network. And so lots of changes there. And again, very excited to see updated data, return of post-COVID, as we exit this isolation economy. Really want to point out to folks that this is not a snapshot survey, right? This is an ongoing exercise that ETR runs, and grateful for our partnership with you guys. Check out ETR.plus, that's the ETR website. I publish weekly on Wikibon.com and SiliconANGLE.com. Sagar, thanks so much for coming on. Once again, great to have you. >> Thank you so much, for having me, Dave. I really appreciate it, as always. >> And thank you for watching this episode of theCube Insights, powered by ETR. This Dave Vellante. We'll see you next time. (gentle music)

>> From the SiliconANGLE Media Office in Boston, Massachusetts, it's theCUBE. Now, here's your host, Dave Vellante. >> Hello everyone and welcome to this week's episode of theCUBE Insights, powered by ETR. In this Breaking Analysis I want to lay out my 2020 predictions using insights gleaned from theCUBE blended with ETR spending data. You know, 2019 marked our 10th year of doing theCUBE. Over that time we've had the pleasure of covering nearly 1000 events and milestones, including the exit from the great softness of 2008 and 2009. You know theCUBE has extensively tracked a 10 year bull market. We've covered the era of data. We saw the rise and profitless prosperity of the big data and opensource Hadoop movement, where we predicted the practitioners, not vendors, would benefit the most from big data. We've covered many dozens of acquisitions including the 60 billion dollar chess move made by Michael Dell acquiring EMC, and a launch of hundreds of startups in flash, hyper-converged, big data, AI, blockchain, crypto, security and SaaS. There'll be other days to talk about theCUBE and review that, today's all about predicting the future, using spending data and insights from the thousands of interviews we've done on theCUBE. So let's get right into the ETR data and start with the high-level spending. Remember in October, ETR released its survey results and stated that we're coming out of a multiyear investment cycle in digital transformation. Enterprise IT buyers have learned what works, and on which technologies they're going to double down. They're now narrowing their investments on emerging technologies, picking those winners for the next gen tech, and at the same time, they're cutting redundancies from legacy players that they were keeping on as a hedge. Buyers are picking bundled suites from a handful of mega vendors, and solidifying their investments. We're seeing a multi-generational dynamic repeat itself, where buyers are creating a balance between the convenience of packaged offerings, i.e. bundles, and leveraging best of breed technologies to drive innovation. So on balance, the ETR data shows that a contraction in spending and tepid CIO sentiment is impacting both emerging vendors as well as traditional players, and these trends are most pronounced in the very largest organizations, which have always been the best bellwether in ETR's data sets. Let me share with you what one IT executive said recently that I think really sums up the situation quite well. He said, "ETR's findings mirror what we're doing today, "in that we spend most of 2018 bringing in "a lot of the new, core technology. "I believe what you're seeing now is not a lull in spend, "but an operationalization of what we've already purchased. "We're not spending on what's next yet, "because we're still rolling out what we just bought." This is from a VP of global IT at a large public manufacturing company, I said he, it could be a she as well. I think that she's summing it up correctly, and it reflects many of what customers on theCUBE tell us. Now, let's take a look at the macroeconomy. GDP growth is going to come in at about 2.3% this year, give or take. It's not going to hit the Trump administration's goal of 3% plus, but consumers are clearly powering steady growth. At least for now. IT spending should grow at about a point or two above GDP, so let's put that at, say, 4%. We're right in the middle of a Santa Claus rally, and the S&P is above 3200 today. Tech has been a powerful tailwind for stocks, and I think stocks, tech stock's going to take a breath in early 2020, but I expect continued strong growth in the economy and tech spending after a Q1 pause. I could see the S&P flirting with 3700 or even higher in 2020, and I think the tech sector will be a benefactor of that momentum, providing an impetus for continued growth. Here's my thinking on that. So much of 2020 is going to be about the election, and to me the election is going to be really about the economy. And I predict the economy is going to remain steady. And as the IT leader I quoted earlier said, customers will be operationalizing what's been previously purchased. Here's what's different in 2020. Tech projects have historically been very risky investments, and have required higher internal rates of return, IRRs, to get approved by CFOs. But the cloud has altered two factors. One, is that it's allowed more experimentation for way less money. The second is cloud, by shifting CAPEX to OPEX, allows for much more incremental, lower risk investments. So I think you'll see continued steady growth, powered by the cloud, which allows experimentation, and importantly higher hit rates of success. These successful projects will throw off cash for companies, and CFOs are getting on board because they realize it's driving innovation. They also realize that IT does matter, maybe not in the form that Nick Carr envisioned, but a new generation of IT that creates competitive advantage. This brings me to my first main prediction, which is the growth of cloud computing is going to moderate, but the cloud will continue to steal significant share from on-prem spending. Now the narrative that the pendulum is swinging back in my view, is a false narrative. Rather, the pendulum has swung, and the cloud is the underpinning of innovation. Now having said that, I do think we're seeing a bit of an equilibrium in spending, where buyers have identified those workloads that are going to remain on-prem, which is why you see, for example, AWS, Azure, and Google making moves in hybrid. Hybrid slash on-prem offerings. What this chart here shows from ETR, so from 2010 through October '19 survey on cloud spending, I had to block out the 2020 survey as it's currently in the field, I'm not allowed to show that data. The yellow line is market share, which in ETR parlance, as you remember, is pervasiveness, or mentions in their survey. The blue line is spending momentum, measured as net score, which essentially subtracts the percent of customers spending less from those spending more. The long, steady march of cloud, as you can see, continues, and there's no indication that it's going to abate. That said, the penetration of cloud has become much more meaningful, so share gains will be more hard-fought for the cloud guys. Now, you may see this as a non-prediction, or a hedge. It's not, let me be clear. Cloud will continue to steal share from on-prem, but share gains for the cloud vendors will be more difficult. Which brings me to part B of this prediction. What I'm showing in this chart is market share from ETR's January 2016 survey through October '19. And I'm showing spending for three on-prem vendors within AWS, Azure, and Google Cloud accounts. And I'm picking on Oracle, IBM, and Dell EMC as three prominent on-prem proxies, and you can see the steady decline in market share for these companies. And even though there's a bit of an uptick in October, I don't see this as a reversal. What's going to happen is that traditional on-prem vendors are going to step up their cloud strategies. Specifically with multicloud management. This is going to be the case with Dell, who's going to leverage VMware, and in the case of IBM, they'll try to take advantage of Red Hat in that multicloud game. Now both IBM and Oracle, who each have public clouds are going to dig their heels in, they're going to get customers in a headlock, and provide big financial incentives for them to use their captive clouds. All right, so with the high-level spending comments that I made earlier, and that cloud discussion that we just had as a backdrop, the question is, which companies will do well in the coming year? I'm going to call out five companies, that I want to highlight where the ETR data intersects what we're seeing on theCUBE. The prediction is these five players will do well in 2020, they're going to power through any downturn in spending, and they're going to thrive in the face of the cloud share shift. So the chart here shows data from the ETR October 2019 survey, and it lays out net score or spending momentum for these companies, that I am predicting will be winners in 2020 and beyond. And the five companies are UIPath, Snowflake, Databricks, HashiCorp, and Rubrik. Let me start with UIPath. They are the leader in robotic process automation. I think RPA is going to do well even in a downturn, because more companies will be looking to automate and save money, even in a softer climate. Automation Anywhere is another player in this space, they're doing pretty well, and I predict that UIPath will come out on top of this space, but both UIPath and Automation Anywhere can thrive. Next company is Snowflake, they are changing the analytic database market, and I've covered them before in previous Breaking Analysis segments. They are going to continue to grow nicely in my view. They are 100% cloud-based, and they participate in all popular cloud platforms. Now ironically, they compete with AWS RedShift, who continues to copy some of the innovations that Snowflake has popularized. But AWS and Snowflake are strong partners, so there's room for both companies to thrive. Snowflake especially, as they play in clouds other than just AWS. Which brings me to Databricks. We're seeing a new type of workload emerge in the cloud for modern analytic databases, where organizations are taking all this data that they have, lots of it in the cloud, and they're structuring it within a Snowflake database, or RedShift, and they're bringing Databricks tooling to the equation to be able to query and visualize the data in near real time. Now of course, as I say, AWS plays here with RedShift, and they're selling a lot of EC2, so they love Snowflake. All major cloud players are seeing this type of workload enter the mix, and it's going to be a strong area of growth in 2020 and beyond. Next thing I want to talk about is HashiCorp. HashiCorp is capitalizing on this trend toward cloud-native computing. The company provides opensource tooling for developers, and is all about simplifying application deployment independent of the underlying platform, whether it's virtual, container, or cloud. Five years ago, the players in the space that got all the attention on theCUBE were Chef, Puppet, Ansible and Salt, and today, especially again on theCUBE, you hear the most about Hashi and Ansible, and in fact we were at AnsibleFest with theCUBE, and we heard lots about HashiCorp, so they both complement and compete with the older players. To me, this reminds me of Spark within the Hadoop ecosystem. Hashi has raised about 174 million in VC, and as you can see they have very strong spending momentum in the ETR dataset, with a net score, as shown, of 63%. Now finally, I want to talk about Rubrik, which has been a consistent performer in the ETR dataset. They're trying to transform backup into data management as a discipline. They compete with established players in the data protection space, guys like Veritas, Dell EMC, IBM and CommVault. Now Rubrik is not the only new or newish player here, that's doing very well, Cohesity, who's relatively new, Veeam, which has been around for a decade, both doing very well and showing up strong in ETR surveys, especially Veeam, but Rubrik has been a consistently strong performer and has been outpacing the others, so I want to call them out. Look for these five to do very well in 2020, and into the next decade. So that brings me to my next prediction, I want to talk about Kubernetes. This prediction is twofold. Kubernetes is going to continue its strong showing as this data from ETR shows. This is Kubernetes' market share in the October 2019 survey, so Kubernetes spend had a 76% net score. So very very strong. But the other part of the prediction is that Kubernetes will become embedded into virtually every platform, and people will stop thinking about it as a separate market. Already today, there's little discussion of the idea of a Kubernetes distro, I mean Anthos is an example of a Kubernetes stack, but it can be run in the cloud, it can be run on-prem, anywhere. VMware Tanzu, Microsoft Azure Arc are other examples, they're really not stacks, but they're management platforms that can manage anyone's Kubernetes instances. I like to think of this as kind of like flash. You remember when everyone looked at flash storage as a separate market, well today it's just embedded everywhere. And that's kind of what's happening with Kubernetes. So spending momentum is going to continue to be strong, but by 2023, Kubernetes will be ubiquitous, and not really thought of as a separate entity. All right, for my next prediction, I want to talk about cybersecurity. I did a Breaking Analysis earlier this year on security, and I showed this slide. And as you can see, I've added a little something in the red stars for my prediction. So what this chart shows is two views of net score, the left-hand side shows the ranking by net score, and you can see CrowdStrike, Okta, Shape Security, which was just, by the way, bought by F5, that was an announcement. Twistlock, which is now Palo Alto Networks, and you can see the others down that list. On the right-hand side is net score, but it's ranked by shared N, which is a measure of pervasiveness in the ETR dataset. What I've added is the four star companies, that is those companies that have both spending momentum and are pervasive in the ETR survey. So the prediction is 2020 we'll see the four star companies maintain their position and gain strength in 2020. These include established players with portfolios where they can bundle like Microsoft, Cisco, Palo Alto Networks, Splunk, Proofpoint, Fortinet, and CyberArk Software. And then the newer companies like Okta and CrowdStrike are going to continue to gain share faster than the larger players. Now you also may see companies like SailPoint, Illumio, and SentinelOne emerge as four star companies over the next 24 months. Now the one company that's not on this list that is a major player in security is AWS. AWS is the cloud security leader, and is in a category all by itself in many ways. As I said in my security segment earlier this year, the market is incredibly fragmented, and it's going to stay that way. Each year we look back and say "Did we spend more on security?" and "Are we more safe?" And every year the answer is yes, and no. And 2020 will be no different. Now if you look at the various data sources, we spend approximately 120 billion dollars annually on cybersecurity. The worldwide economy is about 85 trillion in dollar terms, so on balance, we spend about .14% on securing our economy, so we're barely scratching the surface. The market is going to remain highly fragmented, the rich will get richer if they have four stars, new players will continue to enter the space, and M&A will continue to be robust. Now if you exclude my long shot that the S&P will break through 3700 next year, that makes nine predictions. For my 10th and final prediction, I don't have hard data from ETR, but I have a strong opinion on this, and that is that the edge will be won by developers, you've heard me talk about this before. Specifically, platforms like Outposts, which are essentially programmable infrastructure which bring a cloud development platform to the edge, is how that space will evolve. It won't be won by shoving traditional servers and storage boxes out to the edge. Rather, it will grow by coders being able to build new applications and workloads on top of infrastructure as code. Okay, that wraps up my 2020 predictions. I'd very much like to hear your opinion, so you can leave your thoughts or your own predictions in the comments sections of this video, or go to my LinkedIn posts. You can reach me @DVellante on Twitter, love to hear your thoughts. And don't forget, this series is available on iTunes, Spotify, and other podcast platforms for your listening pleasure. I'd like to wish everyone a safe and restful holiday season and a prosperous, healthy 2020. Enjoy your families, enjoy this time, this is Dave Vellante, signing out from the latest episode of theCUBE Insights powered by ETR, thanks for watching, everybody. We'll see you next time. (techno music)

>> Announcer: Live from San Diego, California, it's theCUBE. Covering KubeCon and CloudNativeCon brought to you by Red Hat, the Cloud Native Computing Foundation, and its ecosystem partners. >> Welcome back to theCUBE here in San Diego for KubeCon CloudNativeCon, with John Troyer, I'm Stu Miniman, and happy to welcome to the program two guests, first time guests, I believe. Julio Tapia, who's the director of Cloud BU partner and community with Red Hat and Gou Rao, who's the founder and CEO at Portworx. Gentlemen, thanks so much for joining us. >> Thank you, happy to be here. >> Thanks for having us. >> Alright, let's start with community, ecosystem, it's a big theme we have here at the show. Tell us your main focus, what the team's doing here. >> Sure, so I'm part of a product team, we're responsible for OpenShift, OpenStack and Red Hat virtualization. And my responsibility is to build a partner ecosystem and to do our community development. On the partner front, we work with a lot of different partners. We work with ISVs, we work with OEMs, SIs, COD providers, TelCo partners. And my role is to help evangelize, to help on integrations, a lot of joint solutions, and then do a little bit of go to market as well. And the community side, it's to evangelize with upstream projects or customers with developers, and so forth. >> Alright, so, Gou, actually, it's not luck, but I had a chance to catch up with the Red Hat storage team. Back when I was on the vendor side I partnered with them. Red Hat doesn't sell gear, they're a software company. Everything open-source, and when it comes to data and storage, obviously they're working with partners. So put Portworx into the mix and tell us about the relationship and what you both do together. >> Sure, yeah, we're a Red Hat OpenShift partner. We've been working with them for quite some time now, partner with IBM as well. But yeah, Portworx, we focus on enabling cloud native storage, right? So we complement the OpenShift ecosystem. Essentially we enable people to run stateful services in OpenShift with a lot of agility and we bring DR backup functionality to OpenShift. I'm sure you're familiar with this, but, people, when they deploy OpenShift, they're running fleets of OpenShift clusters. So, multi-cluster management and data accessibility across clusters is a big topic. >> Yeah, if you could, I hear the term cloud native storage, what does that really mean? You know, back a few years ago, containers were stateless, I didn't have my persistent storage, it was super challenging as to how we deal with this. And now we have some options, but what is the goal of what we're doing here? >> There really is no notion of a stateless application, right? Especially when it comes to enterprise applications. What cloud native storage means is, to us at least, it signifies a couple of things. First of all, the consumer of storage is not a machine anymore, right? Typical storage systems are designed to provide storage to either a virtual machine or a hardware server. The consumer of storage is now a container that's running inside of a machine. And in fact, an application is never just one container, it's many containers running on different systems so it's a distributed problem. So what cloud native storage means is the following things. Providing container granular data services, being application aware, meaning that you're providing services to many containers that are running on different systems, and facilitating the data life cycle management of those applications from a Kubernetes way, right? The user experience is now driven through Kubernetes as opposed to a storage admin driving that functionality so it's these three things that make a platform cloud native. >> I want to dig into the operator concept for a little bit here, as it applies to storage. So, first, Operators. I first heard of this a couple years back with the CoreOS folks, who are now part of Red Hat and it's a piece of technology that came into the Kubernetes ecosystem, seems to be very well adopted, they talked about it today on the keynote. And I'd love to hear a little bit more about the ecosystem. But first I want to figure out what it is and in my head, I didn't quite understand it and I'm like, well, okay, automation and life cycle, I get it. There's a bunch of things, Puppet and Chef and Ansible and all sorts of things there. There's also things that know about cloud like Terraform, or Cloudform, or Halloumi, all these sort of things here. But this seems like this is a framework around life cycle, it might be a little higher in the semantic level or knows a little bit more about what's going on inside Kubernetes. >> I'll just touch on this, so Operators, it's a way to codify business logic into the application, so how to manage, how to install, how to manage the life cycle of the application on top of the Kubernetes cluster. So it's a way of automating. >> Right, but-- >> And just to add to that, you mentioned Ansible, Salt, right? So, as engineers, we're always trying to make our lives easier. And so, infrastructure automation certainly is a concept here. What Operators does is it elevates those same needs to more of an application construct level, right? So it's a piece of intelligent software that is watching the entire run-time of an application as opposed to provisioning infrastructure and stepping out of the way. Think of it as a living being, it is constantly running and reacting to what the application is doing and what its needs are. So, on one hand you have automation that sets things up and then the job is done. Here the job is never done, you're sort of, right there as a side car along with the application. >> Nice, but for any sort of life cycle or for any sort of project like this, you have to have code sharing and contributing, right? And so, Julio, can you tell us a little about that? >> What we do is we're obviously all in on Operators. And so we've invested a great deal in terms of documentation and training and workshops. We have certification programs, we're really helping create the ecosystem and facilitate the whole process. You may be familiar, we announced Operator Framework a year ago, it includes Operator SDKs. So we have an Operator SDK for Helm, for Ansible, for Go. We also have announced Operator Life Cycle Manager which does the install, the maintenance and the whole life cycle management process. And then earlier this year we did introduce also, Operatorhub.io which is a community of our Operators, we have about 150 Operators as part of that. >> How does the Operator Framework relate to OpenShare versus upstream Kubernetes? Is it an OpenShift and Red Hat specific thing, or? >> Yes, so, Operatorhub.io is a listing of Operators that includes community Operators. And then we also have certified Operators. And the community Operators run on any Kubernetes instance. The certified Operators make sure that we run on OpenShift specifically. So that's kind of the distinction between those two. >> I remember a Red Hat summit where you talked about some bits. So, give us a little walk around the show, some of the highlights from Operators, the ecosystem, obviously, we've got Portworx here but there's a broad ecosystem. >> Yeah, so we have a huge huge ecosystem. The ISVs play a big part of this. So we've got Operators database partners, security partners, app monitoring partners, storage partners. Yesterday we had an OpenShift commons event, we showcased five of our big Operator partnerships with Couchbase, with MongoDB, with Portworx obviously, with StorageOS and with Dynatrace. But we have a lot of partners in a lot of different areas that are creating these Operators, are certifying them, and they're starting to get a lot of use with customers so it's pretty exciting stuff. >> Gou, I'd love your viewpoint on this because of course, Portworx, good Red Hat partner but you need to work with all the Kubernetes opt-ins out there so, what's the importance of Operators to your business? >> Yeah, you know. OpenShift, obviously, it's one of the leading platforms for Kubernetes out there and so, the reason that is, it's because it's the expectations that it sets to an enterprise customer. It's that Red Hat experience behind it and so the notion of having an Operator that's certified by Red Hat and Red Hat going through the vetting process and making sure that all of the components that it is recommending from its ecosystem that you're putting onto OpenShift, that whole process gives a whole new level of enterprise experience, so, for us, that's been really good, right? Working with Red Hat, going through the process with them and making sure that they are actually double clicking on everything we submit, and there's a real, we iterate with them. So the quality of the product that's put out there within OpenShift is very high. So, we've deployed these Operators now, the Operator that Portworx just announced, right? We have it running in customers' hands so these are real end users, you'll be talking to Ford later on today. Harvard, for example, and so the level of automation that it has provided to them in their platform, it's quite high. >> I was kind of curious to shift maybe to the conference here that you all have a long history. With organizations and both of you personally in the Kubernetes world and cloud native world. We're here at KubeCon CloudNativeCon, North America, 2019. It's pretty big. And I see a lot of folks here, a lot of vendors, a lot of engineers, huge conference, 12,000 people. I mean, any perspective? >> So I've been at Red Hat a little over six years and I was at the very first KubeCon many years ago in San Francisco, I think we had about 200 people there. So this show has really grown over the years. And we're obviously big supporters, we've participated in KubeCon in Shanghai and Barcelona, we're obviously here. We're just super excited about seeing the ecosystem and the whole community grow and expand, so, very exciting. >> Gou? >> Yeah, I mean, like Julio mentioned, right? So, all the way from DockerCon to where we are today and I think last year was 8000 people in Seattle and I think there're probably I've heard numbers like 12? So it's also equally interesting to see the maturity of the products around Kubernetes. And that level of consistency and lack of fracture, right? From mainstream Kubernetes to how it's being adopted in OpenShift, there's consistency across the different Kubernetes platforms. Also, it's very interesting to see how on-prem and public cloud Kubernetes are coexisting. Four years ago we were kind of worried on how that would turn out, but I think it's enabling those hybrid-cloud workloads and I think today in this KubeCon we see a lot of people talking about that and having interest around it. >> That's a really great point there. Julio, want to give you the final word, for people that aren't yet engaged in the ecosystem of Operators, how can they learn more and get involved? >> Yeah, so we're excited to work with everybody, our ecosystem includes customers, partners, contributors, so as long as you're all in on Operators, we're ready to help. We've got tools, we've documentation, we have workshops, we have training, we have certification programs. And we also can help you with go to market. We're very fortunate to have a huge customer footprint, and so for those partners that have solutions, databases, storage solutions, there's a lot of joint opportunities out there that we can participate in. So, really excited to do that. >> Julio, Gou, thank you so much, you have a final word, Gou? >> I was just going to say, so, to follow up on the Operator comment on the certification that Julio mentioned earlier, so the Operator that we have, we were able to achieve level five certification. The level five signifies just the amount of automation that's built into it, so the concept of having Operators help people deploy these complex applications, that's a very important concept in Kubernetes itself. So, glad to be a Red Hat partner. >> That's actually a really good point, we have an Operator maturity model, level one, two, three, four, five. Level one and two are more your installations and upgrades. But the really highly capable ones, the fours and fives, are really to be commended. And Portworx is one of those partners. So we're excited to be here with them. >> That is a powerful statement, we talk about the complexity and how many pieces are in there. Everybody's looking to really help cross that chasm, get the vast majority of people. We need to allow environments to have more automation, more simplicity, a story I heard loud and clear at AnsibleFest earlier this year and through the partner ecosystem. It's good to see progress, so congratulations and thank you both for joining us. >> Thank you, thank you. >> Thank you. >> All right, for John Troyer, I'm Stu Miniman, back with lots more here from KubeCon CloudNativeCon 2019, thanks for watching theCUBE. (electronic music)

(upbeat techno 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 The Cube's coverage of KubeCon CloudNativeCon 2019. I'm Stu Miniman with Corey Quinn as my cohost, even though he says kucon. And joining us on this segment, we're not going debate how we pronounce certain things, but I will try to make sure that I get Bridget Kromhout correct. She is a Principle Cloud Advocate at Microsoft. Thank you for coming back to The Cube. >> Thank you for having me again. This is fun! >> First of all I do have to say, the bedazzled shirt is quite impressive. We always love the sartorial, ya know, view we get at a show like this because there are some really interesting shirts and there is one guy in a three-piece suit. But ya know-- >> There is, it's the high style, got to have that. >> Oh, absolutely. >> Bringing some class to the joint. >> Wearing a suit is my primary skill. (laughing) >> I will tell you that, yes, they sell this shirt on the Microsoft company store. And yes, it's only available in unisex fitted. Which is to say much like Alice Goldfuss likes to put it, ladies is gender neutral. So, all of the gentleman who say, but I have too much dad bod to wear that shirt! I say, well ya know get your bedazzlers out. You too can make your own shirt. >> I say it's not dad bod, it's a father figure, but I digress. (laughing) >> Exactly! >> Alright, so Bridget you're doing some speaking at the conference. You've been at this show a few times. Tell us, give us a bit of an overview of what you're doing here and your role at Microsoft these days. >> Absolutely. So, my talk is tomorrow and I think that, I'm going to go with its a vote of confidence that they put your talk on the last day at 2:00 P.M. instead of the, oh gosh, are they trying to bury it? But no, it's, I have scheduled enough conferences myself that I know that you have to put some stuff on the last day that people want to go to, or they're just not going to come. And my talk is about, and I'm co-presenting with my colleague, Jessica Deen, and we're talking about Helm 3. Which is to say, I think a lot of times it would, with these open-sourced shows people say, oh, why do you have to have a lot of information about the third release of your, third major release of your project? Why? It's just an iterative release. It is, and yet there are enough significant differences that it's kind of valuable to talk about, at least the end user experience. >> Yeah, so it actually got an applause in the keynote, ya know. (Bridget laughing) There are certain shows where people are hootin' and hollerin' for every, different compute instance that that is released and you look at it a little bit funny. But at the keynote there was a singular moment where it was the removal of Tiller which Corey and I have been trying to get feedback from the community as to what this all means. >> It seems, from my perspective, it seemed like a very strange thing. It's, we added this, yay! We added this other thing, yay! We're taking this thing and ripping it out and throwing it right into the garbage and the crowd goes nuts. And my two thoughts are first, that probably doesn't feel great if that was the thing you spent a lot of time working on, but secondly, I'm not as steep in the ecosystem as perhaps I should be and I don't really know what it does. So, what does it do and why is everyone super happy to con sine it to the dub rubbish bin of history? >> Right, exactly. So, first of all, I think it's 100% impossible to be an expert on every single vertical in this ecosystem. I mean, look around, KubeCon has 7,000 plus people, about a zillion vendor booths. They're all doing something that sounds slightly, overlapping and it's very confusing. So, in the Helm, if you, if people want to look we can say there's a link in the show notes but there, we can, people can go read on Helm.sh/blog. We have a seven part, I think, blog series about exactly what the history and the current release is about. But the TLDR, the too long didn't follow the link, is that Helm 1 was pretty limited in scope, Helm 2 was certainly more ambitious and it was born out of a collaboration between Google actually and a few other project contributors and Microsoft. And, the Tiller came in with the Google folks and it really served a need at that specific time. And it was, it was a server-side component. And this was an era when the Roll by Stacks has control and Kubernetes was, well nigh not existent. And so there were a lot of security components that you kind of had to bolt on after the fact, And once we got to, I think it was Kubernetes 1.7 or 1.8 maybe, the security model had matured enough that instead of it being great to have this extra component, it became burdensome to try to work around the extra component. And so I think that's actually a really good example of, it's like you were saying, people get excited about adding things. People sometimes don't get excited about removing things, but I think people are excited about the work that went into, removing this particular component because it ends up reducing the complexity in terms of the configuration for anyone who is using this system. >> It felt very spiritually aligned in some ways, with the announcement of Open Telemetry, where you're taking two projects and combining them into one. >> Absolutely. >> Where it's, oh, thank goodness, one less thing that-- >> Yes! >> I have to think about or deal with. Instead of A or B I just mix them together and hopefully it's a chocolate and peanut butter moment. >> Delicious. >> One of the topics that's been pretty hot in this ecosystem for the last, I'd say two years now it's been service matched, and talk about some complexity. And I talk to a guy and it's like, which one of these using? Oh I'm using all three of them and this is how I use them in my environment. So, there was an announcement spearheaded by Microsoft, the Service Mesh Interface. Give us the high level of what this is. >> So, first of all, the SMI acronym is hilarious to me because I got to tell you, as a nerdy teenager I went to math camp in the summertime, as one did, and it was named SMI. It was like, Summer Mathematics Institute! And I'm like, awesome! Now we have a work project that's named that, happy memories of lots of nerdy math. But my first Unix system that I played with, so, but what's great about that, what's great about that particular project, and you're right that this is very much aligned with, you're an enterprise. You would very much like to do enterprise-y things, like being a bank or being an airline or being an insurance company, and you super don't want to look at the very confusing CNCF Project Map and go, I think we need something in that quadrant. And then set your ships for that direction, and hopefully you'll get to what you need. And it's especially when you said that, you mentioned that, this, it basically standardizes it, such that whichever projects you want to use, whichever of the N, and we used to joke about JavaScript framework for the week, but I'm pretty sure the Service Mesh Project of the week has outstripped it in terms of like speed, of new projects being released all the time. And like, a lot of end user companies would very much like to start doing something and have it work and if the adorable start-up that had all the stars on GitHub and the two contributors ends up, and I'm not even naming a specific one, I'm just saying like there are many projects out there that are great technically and maybe they don't actually plan on supporting your LTS. And that's fine, but if we end up with this interface such that whatever service mesh, mesh, that's a hard word. Whatever service mesh technology you choose to use, you can be confident that you can move forward and not have a horrible disaster later. >> Right, and I think that's something that a lot of developers when left to our own devices and in my particular device, the devices are pretty crappy. Where it becomes a, I want to get this thing built, and up and running and working, and then when it finally works I do a happy dance. And no one wants to see that, I promise. It becomes a very different story when, okay, how do you maintain this? How do you responsibly keep this running? And it's, well I just got it working, what do you mean maintain it? I'm done, my job is done, I'm going home now. It turns out that when you have a business that isn't being the most clever person in the room, you sort of need to have a longer term plan around that. >> Yeah, absolutely. >> And it's nice to see that level of maturation being absorbed into the ecosystem. >> I think the ecosystem may finally be ready for it. And this is, I feel like, it's easy for us to look at examples of the past, people kind of shake their heads at OpenStack as a cautionary tale or of Sprawl and whatnot. But this is a thriving, which means growing, which means changing, which means very busy ecosystem. But like you're pointing out, if your enterprises are going to adapt some of this technology, they look at it and everyone here was, ya know, eating cupcakes or whatever for the Kubernetes fifth birthday, to an enterprise just 'cause that launched in 2014, June 2014, that sounds kind of new. >> Oh absolutely. >> Like, we're still, we're still running that mainframe that is still producing business value and actually that's fine. I mean, I think this maybe is one of the great things about a company like Microsoft, is we are our customers. Like we also respect the fact that if something works you don't just yolo a new thing out into production to replace it for what reason? What is the business value of replacing it? And I think for this, that's why this, kind of Unix philosophy of the very modular pieces of this ecosystem and we were talking about Helm a little earlier, but there's also, Draft, Brigade, etc. Like the Porter, the CNET spec implementation stuff, and this Cloud Native application bundles, that's a whole mouthful. >> Yes, well no disrespect to your sparkly shirt, but chasing the shiny thing, and this is new and exciting is not necessarily a great thing. >> Right? >> I heard some of the shiny squad that were on the show floor earlier, complaining a little bit about the keynotes, that there haven't been a whole lot of new service and feature announcements. (Bridget laughing) And my opinion on that is feature not bug. I, it turns out most of us have jobs that aren't keeping up with every new commit to an open-source project. >> I think what you were talking about before, this idea of, I'm the developer, I yolo'd out this co-load into production, or I yolo'd this out into production. It is definitely production grade as long as everything stays on the happy path, and nothing unexpected happens. And I probably have air handling, and, yay! We had the launch party, we're drinkin' and eatin' and we're happy and we don't really care that somebody is getting paged. And, it's probably burning down. And a lot of human misery is being poured into keeping it working. I like to think that, considering that we're paying attention to our enterprise customers and their needs, they're pretty interested in things that don't just work on day one, but they work on day two and hopefully day 200 and maybe day 2000. And like, that doesn't mean that you ship something once and you're like, okay, we don't have to change it for three years. It's like, no, you ship something, then you keep iterating on it, you keep bug fixing, you keep, sure you want features, but stability is a feature. And customer value is a feature. >> Well, Bridget I'm glad you brought that up. Last thing I want to ask you 'cause Microsoft's a great example, as you say, as a customer, if you're an Azure customer, I don't ask you what version of Azure you're running or whether you've done the latest security patch that's in there because Microsoft takes care of you. Now, your customers that are pulled between their two worlds is, oh, wait, I might have gotten rid of patch Tuesdays, but I still have to worry and maintain that environment. How are they dealing with, kind of that new world and still have, certain things that are going to stay the old way that they have been since the 90's or longer? >> I mean, obviously it's a very broad question and I can really only speak to the Kubernetes space, but I will say that the customers really appreciate, and this goes for all the Cloud providers, when there is something like the dramatic CVE that we had in December for example. It's like, oh, every Kubernetes cluster everywhere is horribly insecure! That's awesome! I guess, your API gateway is also an API welcome mat for everyone who wants to, do terrible things to your clusters. All of the vendors, Microsoft included, had their managed services patched very quickly. They're probably just like your Harple's of the world. If you rolled your own, you are responsible for patching, maintaining, securing your own. And this is, I feel like that's that tension. That's that continuum we always see our customers on. Like, they probably have a data center full of ya know, veece, fear and sadness, and they would very much like to have managed happiness. And that doesn't mean that they can easily pickup everything in the data center, that they have a lease on and move it instantly. But we can work with them to make sure that, hey, say you want to run some Kubernetes stuff in your data center and you also want to have AKS. Hey, there's this open-source project that we instantiated, that we worked on with other organizations called Vertual Kubelet. There was actually a talk happening about it I think in the last hour, so people can watch the video of that. But, we have now offered, we now have Virtual Node, our product version of it in GA. And I think this is kind of that continuum. It's like, yes of course, you're early adapters want the open-source to play with. Your enterprises want it to be open-source so they can make sure that their security team is happy having reviewed it. But, like you're saying, they would very much like to consume a service so they can get to business value. Like they don't necessarily want to, take, Kelsey's wonderful Kubernetes The Hard Way Tutorial and put that in production. It's like, hmm, probably not, not because they can't, these are smart people, they absolutely could do that. But then they spent their, innovation tokens as, the McKinley blog post puts it, the, it's like, choose boring technology. It's not wrong. It's not that boring is the goal, it's that you want the exciting to be in the area that is producing value for your organization. Like that's where you want most of your effort to go. And so if you can use well vetted open-source that is cross industry standard, stuff like SMI that is going to help you use everything that you chose, wisely or not so wisely, and integrate it and hopefully not spend a lot of time redeveloping. If you redevelop the same applications you already had, its like, I don't think at the end of the quarter anybody is getting their VP level up. If you waste time. So, I think that is, like, one of the things that Microsoft is so excited about with this kind of open-source stuff is that our customers can get to value faster and everyone that we collaborate with in the other clouds and with all of these vendor partners you see on the show floor, can keep the ecosystem moving forward. 'Cause I don't know about you but I feel like for a while we were all building different things. I mean like, instead of, for example, managed services for something like Kubernetes, I mean a few jobs that would go out was that a start up that we, we built our own custom container platform, as one did in 2014. And, we assembled it out of all the LEGOs and we built it out of I think Docker and Packer and Chef and, AWS at the time and, a bunch of janky bash because like if someone tells you there's no janky bash underneath your home grown platform, they are lying. >> It's always a lie, always a lie. >> They're lying. There's definitely bash in there, they may or may not be checking exit codes. But like, we all were doing that for a while and we were all building, container orchestration systems because we didn't have a great industry standard, awesome! We're here at KubeCon. Obviously Kubernetes is a great industry standard, but everybody that wants to chase the shiny is like but surface meshes. If I review talks for, I think I reviewed talks for KubeCon in Copenhagen, and it was like 50 or 60 almost identical service mesh talk proposals. And it's like, and then now, like so that was last year and now everyone is like server lists and its like, you know you still have servers. Like you don't add sensation to them, which is great, but you still have them. I think that that hype train is going to keep happening and what we need to do is make sure that we keep it usable for what the customers are trying to accomplish. Does that make sense? >> Bridget, it does, and unfortunately, we're going to have to leave it there. Thank you so much for sharing everything with our audience here. For Corey, I'm Stu, we'll be back with more coverage. Thanks for watching The Cube. (upbeat techno music)

>> live from Anaheim, California. It's the queue covering nutanix dot next twenty nineteen Brought to you by nutanix. I'm >> just going to hear you. >> Welcome back, everyone to the cubes. Live coverage of dot Next here at the Anaheim Convention Center in California. I'm your host, Rebecca Night, along with my co host, John Furrier. We are welcoming to the Cube. George Canuck. He is the vice president. Worldwide sales and channels absent us. Thank you so much for coming on the Q. >> Thanks for having me excited to be here. >> So here we are on the convention floor. Sixty five hundred attendees. You actually have a booth here? Yes, you do, actually. Right over >> there. Take a look. Orange logo. >> Very handsome logo. So tell it for our viewers who are not familiar with your company. Tell your Austin, Texas based tell tell our viewers a little bit about what you do. What's what sentences about? >> Sure. So we help professionals do something really important, ultimately solving a big problem for them, which is keeping customers happy. So we're looking at we provide a suffer platform that looks at all of the underlying infrastructure that's actually supporting the application itself. So they're trying to deliver APS and services their customers a happy customers. Somebody clicks their phone or their laptop and just gets to that service. We make sure that that app is available and healthy, but looking at everything underneath it. Whether that's Ah hybrid cloud, it's a private hcea type cloud as well. Or it's micro services or its legacy infrastructure. It doesn't matter. We talk to it and we help make sure that everything's working properly. >> But it works the way it's supposed to >> exactly way had the chief product officer on scene eel from New Chantix talking about hyper convergence. The benefits of that Yeah, it's also thought the hyper converge clouds, I guess, with the lack of a better description that that rules going there, too, When you start to get into this resetting of the infrastructure elements on premises and also in hybrid multi cloud Yeah, a lot of problems arise. They did a huge issue. So can you give us some color commentary on your thoughts on where customers are here summer summer like, Well, we're not there yet. Summer stuck running out of gas or stuck in the mud, and some just saying, you know, we're all in on the cloud, So different profile makeups of sure Wow, adoption. >> Yeah, let me talk about a little bit. So I heard a stat recently that the current adoption of enterprises for clouds about ten percent. So ten per cent of workloads today in the cloud doesn't mean that there isn't a lot of growth and a lot of people aren't trying it, but only ten percent are there. And in a lot of cases, the more progressive organizations actually did move workload with cloud they got there. They found out that maybe things were more expensive than they thought or didn't quite perform well and they took a step back and retooled it. It really was for Nutanix, I think personally Ah, very good time for them to step in with this notion of a private cloud. It's sort of that step in between for some of them. However, when you look at it from our perspective, we you know, we've been around since two thousand five. We started his open source and moved into a commercialized product. We've worked with some of the biggest banks. Insurance companies tell echoes and even MSP is in the world. We've seen that the certain workloads have moved to the cloud pretty quickly or too hyper converged. But yet there's still a lot that hasn't and there's a lot of unknowns that air there. In some cases, it's a function of Is the team ready to make the move and other cases? Is the culture of the organization ready to make the move? For us? It doesn't matter because we can look at all of it. But we can make it easier for them because we actually help them. Look at the various workloads in the performance of those abs and how how they would perform. And they make a move to the >> T. Want to get your thoughts on the psychology of the the environment, the buyer or the abuser. Whenever is a changeover to new technology or new desktop or, you know, cloud, the expectation is better run better, so coming around faster and better, better user experience. Yes, so this kind of puts you guys on the pressure cooker because you guys have toe monitoring starts working worse than it was before. Yeah, so table stakes now is be better. Be faster whether it's a VD, I roll out or cloud implementation. How do you guys hate a lead? >> We know there's there's a piece that actually happens before that. So the first step that we see that happens for organizations making the move is actually rationalizing the views of the truth. That makes sense. And so, in a lot of organizations, there are different silos. I've been in meetings where the Dev Ops team, the same team running service now, for example, and the cops are meeting each other, shaking hands and saying, Hi, Jane. Hi, Bob. Great to meet you for the first time. And that is being Those meetings are being held by what I'LL say are more progressive leaders, the CEOs and GPS. But the first thing that happens is every group says we'LL have this basket of tools that I'm using to make sure that my customers are happy and they have to rationalize all that one of our customers. Huntington Bank had thirty seven tools in place to look at every single part of the business and get that one view and he could match. It's pretty difficult we helped to make that transition. If they're culturally going to make the switch than having a grip on what's working. Now we'LL help them replicate that when they make the move Teo Private cloud or Public cloud. That makes sense. >> Yeah, totally does. And they also mentioned the status quo. A lot of companies don't want to rock the boat. Yes, when they bring in new technologies. How do you see that playing out? Because one of nutanix is advantages that they get in. They change agents? Yes, and cause some benefits there for the customer, and then they grow from there. But yes, the people still gonna buy the old old stuff. >> Yeah, well, so you know what's interesting? So we have a change agent who's a friend of ours that nutanix a customer. So Wendy, fight for the CEO of NUTANIX is actually a customer of ours. They call themselves customer zero. If you've read her interviews, she they drink their own Champaign. And she recently we interviewed her and she talked about that change. And I believe it does need to come from the top town. So progressive leaders will introduce that change of the business and honestly make it comfortable for their team to take risks because it is a risk making a move any of these technologies. I think when you when we look at the I guess the simplest migration for a customer to HCR Private Cloud, it is going to be maintaining that visibility across the legacy into the new world that's going to be critical for them. That view, by the way, is one that that even the CEO wants and the CEO. >> I want to talk about the changing role of the CEO because because it is it is a very big theme and trend in this industry. And you keep talking about this idea of a progressive CEO, and this is someone who is willing to take risks. Willing Teo, tear down silos, make sure people are collaborating. Can you talk a little bit more about what you see as the people who are best at their jobs? Yes, best CEOs out there and what they're doing, what they're doing differently, >> right? Well, so I mentioned these groups meet for the first time the cops, the Dev, Ops and Sam, and probably other groups that come into those rooms as well. The profile today of a lot of the CEOs and the Final one is someone who came up through the operations organization more than likely, and they understand how that world works. They've had to. For some of them, it's been unease e transition to bring the Dev ops folks into the room. I think about this, right Cops roll is in the past. Bring me an apple. Make sure runs flawlessly on this amazing gear that I have. The Dev Ops role is I'm going to take a nap. I'm going to run it on this gear and I'm gonna optimize the app. So it's a different view to get to the same problem in the other end. And so I would tell you that it is about being progressive and that role has shifted. It's very possible the next batch of CEOs will come out of the developer organization one more quick common on that. So there's a pretty provocative Forrester wave that came out a few weeks ago that we're in who for the first time didn't look at the type of tech they actually looked at. The problem being solved and the problem, as they categorize it, is intelligent application and service monitoring. So it is about services and APS running well on DH. There are more than one technology to solve that problem. We're pleased Tio have been recognized for our thought leadership. That's >> how do you guys handle the potential blind spots in the observation space that you guys have to look under the covers and look at everything? How do you guys identify potential blind spots? What's what's you guys filtering out? Take us through an example? >> Sure, we'LL sue a couple things that'LL help you get to the blinds. So there are a lot of blind spots, especially have multiple tools. There's blind spots. The second part of that that's pretty relevant, is getting complete visibility to all the right folks in the organization. So one of the first things we do is look at that entire surface, if you will, the entire landscape lay it all out and started the top with the service and show all the dependencies of everything underneath it. We call that the model, so when the models in place, then we can show the impact of change on the model that could be a bad piece of gear. It could be a bad piece of code. It doesn't really matter to us. We're looking at it that way. That's that's probably the first step in it. The second piece that goes along with this is something we did intentionally, which is we brought a I into the mix. So we partner with Google. We actually pivoted much like Nutanix did a number of years ago last year really seen as cloud and brought in the A, A A and M L capabilities of Google, primarily because the amount of information coming out of all these complex infrastructures is more than a human could handle. So we're using that ay, ay to help look at each anomalies problem as it happens each potential blind spot and uncover that using the technology to determine. Is it a real problem for me, or is it just noise? >> It's interesting you bring up the I T Ops and Dev ops thing. You know one thing that Google proved out. I've been saying this on the Q as you know, for years and recently highlighted at the recent next conference, they nailed the whole s sorry thing it's light reliable with the engineer, and they didn't do it as a strategy to try to get market share. They didn't because they had their own problem. Yeah, that was massive scale, lot of automation, A lot of software. But they had a development environment of debs and ops. Was about one human. Too many machines? Yeah, relationship. That's essentially what you're getting at. Here it >> is. Actually, it's It's interesting. You know Mike Nickerson from Google, who published some of the interesting initial charts, kind of like a Maslow's hierarchy of Sorry, the foundational level actually is monitoring. It's sort of like a RH or water or safety on DH. Having that visibility is the first piece, The one thing all city though you touched on automation, the all that information, the world and all that, eh? Eyes kind of worthless if you can't actually automate the back end of it. So we spent a lot of time working with either cloud optimization, you know, a DBS Lambda or Google of Claude Function. Or we're looking at things like pup in Chef just to automate all of that other end of it. We have a term we use. We called software defined. It stops when you get to the point where the inputs more than a human can handle. They won't deal to react fast enough. A lot of our tools, the human's air used This sounds like I'm talking about the sky net, but a lot of the tools the humans use our eyes. Actually, in forensic analysis, when a problem happens, the remediation and the and the pro activities happening through the machine, you >> know where it's kinda went. Dog starts sniffing out Bala where I want to get the machines, actually, on the stack related question. You know, one of the things we heard from so Neil, the chief product officer, was the multi cloud battles will be fought on the top of the stack or up to stack. So the question is, what line or what? What? What's the line for under the hood now? So as you look at micro services and Deb, ops continues to go with Cooper Netease and service meshes. Yeah, you're gonna have a serious of service is being turned on terror. Tauron down all the times, right? So that challenges on the B on the monitor monitoring and observation. So where do you guys go? How high up do you go? Is there a line where the hood is? What's under the hood? What's about you? Do you think that's >> a fantastic question? I couldn't have asked for a better one. So the one side of it is house. Yeah, performing that sort of above the hood if you will write. And we are looking at that and we're looking at all the way to the level of down to the experience of that application and how it runs on the infrastructure. But we go all way down to the bare metal is well, because we think there's a value in doing it. There's a couple of concepts out there around server. Listen, by the way, Xena's cloud is a survivalist deployment. So, actually, you know, eat her own dog for you. Drink our own Champaign when it comes to this tack. But that notion of below the hood for us is all the way down to the bare metal, and that visibility, if you want to look at it in another way, is actually the great high quality data and raw material to drive the II and the output. It if you have to make sense of the other end of it. Yup. >> I want to ask you about the show. So at how many? How many of these have you been to? And what What's your experience? What are you? What do you What do you hoping to bring back with you to Austin today myself. >> But for Nutanix, we've been We've been a partner with Satanic since since we started working there as a customer, which would have been probably late. Twenty sixteen, twenty, sixteen. We started doing the shows last year. We did we actually attend as a partner. We attend some of their meetings and the partner part's important to come back to in a second, but a zeo as a technology partner initially. Now we're moving into a point we were trying to sell with the team and help them bring our visibility to their customers. The last thing we did was was next Europe, which is a fantastic show in London last fall. And we've also done a lot of the road shows in the cities. The thing we love about it is we both talked to the same customer. Both have the same people were talking to the one thing we're trying to do. And I know that Nutanix is as well as we want to bring more of the developers and Dev ops crew into it. We believe they need to be a part of the discussion. So something we're trying to help facilitate. But but this show has been fantastic for us. Yeah, >> and to your point about the developers, we're seeing that in the infrastructure worlds, not just operation work. There's Debs in there now. Yes. Automating away these mundane, repetitive tasks. Yeah, I think I think it's >> more friendly than it was for sure. >> All right, >> we'LL take your word for it. Thank you so much, George. For coming on. The Cuba was a pleasure having you on. >> Thank you. Pleasure meeting about. Thank you so much. Take care. >> I'm Rebecca Knight for John Furrier. We will have so much more from nutanix dot Next coming up in just a little bit

>> Live from Las Vegas. It's the queue covering Adobe Summit twenty nineteen brought to you >> by Adobe. >> Well, Brian, welcome to the Cube Lives Conversations here. Recovering Adobe summat twenty nineteen in Las Vegas. I'm tougher with Jeff Frick co hosting for the next two days wall to wall coverage around Adobe Summit, a company that is transformed from some making software to being a full blown cloud and data provider. Changing the user experience That's our Kino revue. Jeff, this morning was the keynote. The CEO Sean Tom knew no. Ryan took over in two thousand seven. Bruce Chizen Cube alumni, right. What a transformation. They actually did it. They kind of kept down low. But over those years absolutely changed the face of Adobe. We're seeing it now with a slew of acquisitions. Now seventeen thousand people attending this conference. This is kind of interesting story, your thoughts >> a lot of interesting stuff going on here, John and I think fundamentally they they took the risk right. They change your business from a by a news buying new license every year for eight hundred bucks. Nine hundred bucks, whatever used to be for Creative Cloud to go to an online model. And I think what was interesting about what Johnson, who said, is when you are when you're collecting money monthly, you have to deliver value monthly. And it completely changed the way that they paste their company the way they deliver products the way their product development works. And they moved to as we talked about all the time, instead of a sample of data that's old and making decisions. Now you can make decisions based on real time data in the way people are actually using the product. And so they've driven that transformation. And then now, by putting your whole sweet and with these gargantuan acquisitions of Mar Keto, now they're helping their customers really make that transition to a really time dynamic, digitally driven, data driven enterprise to drive this customer experience. >> It's interesting. Adobes, transformations, realist, legit It happened. It's happening. It's interesting, Jeff, you and I both live in Palo Alto, and I was looking through my Lincoln and my Facebook. There's literally dozens of friends and your colleagues over the years that I've interfaced with that all work at Adobe but feed all the acquisitions. They've built quite a huge company, and they brought a different set of experiences, and this is the to be the big story. That hasn't been told yet. Adobe again. This our first time covering Adobe Summit and excited to be here and continue to cover this. But here's what's going on That's really important. They transformed and are continuing Transformer. They did it in a way that was clever, smart and very predictive in their mind. They took a slow, slow approach to getting it right, and we heard the CEO talk about this. They had an old software model that was too slow. They want to attract the next generation of users, and they wanted to reimagine their product and the ecosystem changed their business model and change their engagement with customers. Very targeted in its approach, very specific to their business model. And their goals were innovate faster, moved to the cloud moved to a subscription based business model. But that's not it. Here the story is, the data equation was some kind of nuances in the keynote, like we didn't get the data right. Initially, we got cloud right, but data is super important, and then they got it right, and that's the big story. Here is the data driven and this is the playbook. I mean, you can almost substitute Adobe for your company. If someone's looking to do Tracy, pick your spots, execute, don't just talk about >> it, right? Right? Yeah. They call it the DDO in the data driven operating model, and he pulled up the dash board with some fake data talked about The management team runs off of this data, and when you know it's everything from marketing spend and direct campaigns and where people are sampling, there was a large conversation, too, about the buyer journey. But to me, the most important part is the buying act is not the end of the story, right. You want to continue to engage with that customer wherever and however, and whenever they want you. There was an interesting stat that came out during the keynote, where you know the more platforms your customer engages with you, the much higher the likelihood that they're goingto that they're going to renew, that they're going to retain so to me. I think you know, we talk a lot about community and engagement and this experience concept where the product is a piece of the puzzle, but it's not. It's not the most important piece that might be the piece Well, what she experiences built around, but it's It's just a simple piece. I think the guy from Best Buy was phenomenal. The story, the transformation, that company. But they want to be your trusted. A provider of all these services of two hundred dollars a year. They'LL come take care of everything in your home so you know they don't just want to ship a box. Say, say goodbye. They want to stay. >> Well, let's talk. Let's talk about that use case. I think the best bike Kino Best Buy was on the Kino with CEO. But I think that what I what? I was teasing out of that interview and you just brought it up. I want to expand on that They actually had massive competition from Amazon. So you think, Oh my God, they're going to be out of business? No, they match the price. They took price off the table so they don't lose their customers who want to buy it on Amazon. You can still come in the story of experience, right? They shifted the game to their advantage where they said, we're not going to be a product sales company. We're going to sell whatever the client want customers want and match Amazons pricing and then provide that level of personalization. That then brought up the keys CEOs personalization piece, which I'd like to get your thoughts on because you made a stat around their emails, right, he said, Quote personalization at scale, Right? That's what they're >> that's that they're doing right? And he talked about, you know, they used to do an e mail blast and it was an email blast. Now they have forty million versions of that e mail that go out forty million version. So it is this kind of personalization at scale. And you know, the three sixty view of the customer has been thrown around. We could go in the archives. We've been talking about that forever. But it seems that now you know the technology is finally getting to where, where needs to be. The cloud based architectures allow people to engage in this Army Channel way that they could never do it before. And you're seeing As you said, the most important thing is a data architecture that can pull from disparate sources they talked about in the Kenya. The show does they actually built their customer profile as the person was engaging with the website as they gave more information so that they can customize all this stuff for that person. Of course, then they always mentioned, But don't be creepy about it. I >> don't have too >> far so really delivering this mask mask, personalization at scale. >> I think one of the lessons that's coming out a lot of our interviews in the Cube is Get the cloud equation right first, then the data one. And I think Adobe validates that here in my mind when it continue investigating, report that dynamic the hard news. Jeff The show was Adobe Cloud experiences generally available, and I thought that was pretty interesting. They have a multiple clouds because a member they bought Magenta and Marquette on a variety of other acquisitions. So they have a full on advertising cloud analytics, cloud marketing cloud and a commerce cloud. And underneath those key cloud elements, they have Adobe, sensi and Adobe Experience platform, and we have a couple of night coming on to talk about that, and that's making up. They're kind of the new new platform. Cloud platforms experience Cloud. They're calling it, but the CEO at Incheon quote. I want to get your reaction to that. This, he said, quote people by experiences, not products. That's why they're calling it the experience cloud. I hear you in the office all the time talking about this, Jeff. So it's about to experience the product anymore, >> right? It is the passion that you can build around a community in that experience. My favorite examples from the old days is Harley Davidson. How many people would give you know they're left pinkie toe, have their customers tattoo their brand on their body? Right in The Harley Davidson brand is a very special, a special connotation, and the people that associate with that really feel like a part of a community. The other piece of it is the ecosystem. They talk about ecosystem of developers and open source. If you can get other people building their business on the back of your platform again, it's just deepens the hook of engagements that opens up your innovation cycle. And I think it's such a winning formula, John, that we see over and over again. Nobody can do by themselves. Nobody's got all the smartest people in the room, so get unengaged community. Get unengaged, developer ecosystem, more talk of developers and really open it up and let the creativity of your whole community drive the engagement and the experience. >> We will be following the personalization of scale Cube alumni former keep alumni who is not at the show. I wanted to get opinion. Satya Krishna Swami. He's head of persuasion. Adobe had pinned them on linked him. We'LL get him on the Cuban studio so keep on, we're going to follow that story. I think that's huge. This notion of personalization of scale is key, and that brings us to the next big news. The next big news was from our friend former CEO of Marquette. Oh, Steve Lucas. Keep alumni. They launched a account based experience initiative with Adobe, Microsoft and Lincoln, and I find that very interesting. And I'd start with Ron Miller TechCrunch on Twitter about this. Lincoln's involved, but they're keeping in Lincoln again. The problem of data is you have these silos, but you have to figure out how to make it work. So I'm really curious to see how that works, so that brings up that. But I think Steve Lucas it was it was very aggressive on stage, but he brought up a point that I want to get your thoughts on, He said. Were B to B company, but we're doing B to seeing metrics the numbers that they were doing at Marquette. Oh, we're in the B to see rain. So is this notion of B to B B to see kind of blurring? I mean, everyone is a B to C company these days. If everything's direct to consumer, which essentially what cloud is, it's a B to see. >> Yeah, well, it's interesting records. We've talked about the consumer ization of again. Check the tapes for years and years and years, and the expectations of our engagement with applications is driven by how we interact with Amazon. How we interact with Facebook, how we interact with these big platforms. And so you're seeing it more and more. The thing that we talked about in studio the other day with Guy is that now, too, you have all these connected devices, so no longer is distribution. This this buffer between the manufacturing, the ultimate consumer, their products. Now they're all connected. Now they phone home. Now the Tesla's says, Hey, people are breaking in the back window. Let's reconfigure the software tohave a security system that we didn't have yesterday that wasn't on our road map. But people want, and now we have it today. So I think Steve's perception is right on. The other thing is that you know, there's so much information out there. So how do you add value when that person finally visits you in their journey? And let's face it, most of the time, a predominant portion of their engagement is going to be Elektronik, right? They're going to fill out a form. They're going to explore things. How are you collecting that data? How are you magic? How are you moving them along? Not only to the purchase but again, is that it was like to say, is never the orders, the reorder in this ongoing engagement. >> And that's their journey. They want to have this whole life cycle of customer experience. But the thing that that got that caught me off guard by McKeen against first time I went satin Aquino for an adobe on event was with me. All these parts coming together with the platform. This is a cloud show. Let's plain and simple. This is Cloud Technologies, the data show we've gone to all the cloud shows Amazon, Google, Microsoft, you name it CNC Athletics Foundation. This is a show about the application of being creative in a variety of use cases. But the underpinnings of the conversations are all cloud >> right, And they had, you know, to show their their commitments of data and the data message right? They had another cube alumni on Jewell of police have rounded to dupe some it all the time, and she talked about the data architecture and again, some really interesting facts goes right to cloud, she said. You know, most people, if you don't have cloud's been too much time baby sitting your architecture, baby sitting your infrastructure Get out of the way Let the cloud babe sit your infrastructure and talk. And she talked about a modern big data pipe, and she's been involved with Duke. She's been involved with Spark has been involved in all this progression, and she said, You know, every engagement creates more data. So how are you collecting that data? How are you analyzing that data and how are you doing it in real time with new real time so you could actually act on it. So it's It's very much kind of pulling together many of the scenes that we've uncovered >> in the last two parts of a Kino wass. You had a CEO discussion between Cynthia Stoddard and >> Atticus Atticus, other kind. Both of them >> run into it again. Both big Amazon customs, by the way, who have been very successful with the cloud. Then you had and you're talking engineering, that's all. They're my takeaway from the CEO. One chef I want to get your thoughts on because it can be long in the tooth, sometimes the CEO conversation. But they highlighted that cloud journey is is there for Adobe Inn into it? But the data is has to be integrated, totally felt like data. Variables come out the commonality of date, and she mentioned three or four other things. And then they made a point and said, quote data architectures are valuable for the experience and the workload. This is critical with hearing us over and over again. The date is not about which cloud you're using. It's about what the workload, right, right? The workloads are determining cloud selection, so if you need one cloud. That's good. You need to write. It's all depending on the workload, not some predetermined risk management. Multi cloud procurement decision. This is a big shift. This is going to change the game in the landscape because that changes how people buy and that is going to be radical. And I think they're they're adobes right on the right wave. Here they're focusing on the user experience, customer experience, building the platform for the needs of the experience. I think it's very clever. I think it's a brilliant architecture. >> Yeah, she said that the data archive data strategy lagged. Right? The reporting lag. They're trying to do this ddo m >> um, >> they didn't have commonality of data. They didn't have really a date. Architecture's so again. You can't build the house unless you put in the rebar. You build the foundation, you get some cement. But once you get that, that enabled you to build something big and something beautiful, and you've got to pay attention. But really, we talk about data driven. We talk about real time data, they're executing it and really forcing themselves by moving into the subscription business model. >> Alright, Final question I want to get one more thought from you before I weigh in on my my answer to my question, which is What do you mean your opinion? What was the most important story that came out of the keynote one or two >> or well or again? You know, John, I was in the TV business for years and years before getting into tech, and I know the best buy story on what came before them and what came before them and what came before them. So what really impressed me was the digital transformation story that the CEO shared first, to basically try to get even with their number one competitors with which was Amazon in terms of pricing and delivery. And then really rethink who they are Is a company around using technology to improve people's lives. They happen to play in laundry. They play in kitchen, they play in home entertainment. They play in computers and education, so they have a broad footprint and to really refocus. And as he said, To be successful, you need to align your corporate strategy and mission with people's strategy and mission. Sounds like they've been very successful in that and they continue to change the company. >> I agree. And I would just kind of level it up and say the top story, in my opinion, wass the fact that Adobe is winning their innovating. If you look at who's on stage like best buy into it, the people around them are actually executing with Cloud with Dae that at a whole another level that they've gone the next level. I think the big story here is Adobe has transferred, has transformed and continues to do transformation. And they just had a whole nother level. And I think the story is Oracle will be eating their dust because I think they're going to tow. You know, I think sales force should be watching Adobe. This is a big move. I think Oracle is gonna be twisting in the wind from adobes success. >> Well, like he said, you know, they tie the whole thing together from the creativity, which is what creative cloud is to the delivery to them, the monetization in the measuring. So now they you know, they put those pieces together, so it's a pretty complete suite. So now you can tie back. How has my conversion based on What type of creative How is my conversion based on what type of campaigns? And again the forty million email number just blows me away. It's not the same game anymore. You have to do this and you can't do by yourself. You gotta have automation. You got have good analytics and you got a date infrastructure that will support your ability to do that. >> So just a little report card in adobe old suffer model that's over. They have the new model, and it's growing revenues supporting it. They are attracting new generation of users. You look at the demographics here, Jeff. This is not, you know, a bunch of forty something pluses here. This is a young generation new creative model and the products on the customer testimonials standing on this stage represent, in my opinion, a modern architecture, a modern practice, modern cloud kind of capabilities. So, you know, Adobe Certainly looking good from this keynote. I'm impressed, you know. Okay, >> good. Line up all the >> days of live cube coverage here in Las Vegas for Doby summit. I'm John for Jeff. Rick, Thanks for watching. We'll be back with a short break

(upbeat music) >> From our studios in the heart of Silicon Valley, Palo Alto, California, this is a CUBEConversation. >> Hi everyone, welcome to the special CUBEConversation. We're in Palo Alto, California, at theCUBE studio. I'm John Furrier, co-host of the CUBE. We're here with Chris Yeh. He's the co-founder and general partner of Blitzscaling Ventures, author of the book Blitzscaling with Reid Hoffman, founder of LinkedIn and a variety of other ventures, also a partner at Greylock Partners. Chris, great to see you. I've known you for years. Love the book, love Reid. You guys did a great job. So congratulations. But the big news is you're now a TV star as one of the original inaugural contestants on the Mental Samurai, just premiered on Fox, was it >> On Fox. >> On Fox, nine o'clock, on which days? >> So Mental Samurai is on Fox, Tuesdays at 9 p.m. right after Master Chef Junior. >> Alright. So big thing. So successful shows. Take us through the journey. >> Yeah. >> It's a new show, so it's got this kind of like Jeopardy vibe where they got to answer tough questions in what looks like a roller coaster kind of arm that moves you around from station to station, kind of jar you up. But it's a lot of pressure, time clock and hard questions. Tell us about the format. How you got that. Gives all the story. >> So the story behind Mental Samurai is it's from the producers of American Ninja Warrior, if you've ever seen that show. So American Ninja Warrior is a physical obstacle course and these incredible athletes go through and the key is to get through the obstacle course. If you miss any of the obstacles, you're out. So they took that and they translated it to the mental world and they said, okay, we're going to have a mental obstacle course where you going to have different kinds of questions. So they have memory questions, sequence questions, knowledge questions, all these things that are tapping different elements of intelligence. And in order to win at the game, you have to get 12 questions right in five minutes or less. And you can't get a single question wrong. You have to be perfect. >> And they do try to jar you up, to kind of scrabble your brain with those devices, it makes it suspenseful. In watching last night at your watch party in Palo Alto, it's fun to watch because yeah, I'm like, okay, it's going to be cool. I'll support Chris. I'll go there, be great and on TV, and oh my, that's pretty interesting. It was actually riveting. Intense. >> Yeah. You have that element of moving around from station to station and it's dramatic. It's kind of a theater presence. But what's it like in there? Give us some insight. You're coming on in April 30th so you're yet to come on. >> Yes. >> But the early contestants, none of them made it to the 100,000. Only one person passed the first threshold. >> Right >> Take us through the format. How many thresholds are there? What's the format? >> Perfect, so basically when a competitor gets strapped into the chair, they call it Ava, it's like a robot, and basically they got it from some company in Germany and it has the ability to move 360 degrees. It's like an industrial robot or something. It makes you feel like you're an astronaut or in one those centrifugal force things. And the idea is they're adding to the pressure. They're making it more of a challenge. Instead of just Jeopardy where you're sitting there, and answering questions and bantering with Alex Trebek, you're working against the clock and you're being thrown around by this robot. So what happens is first you try to answer 12 questions correctly in less than five minutes. If you do that, then you make it through to the next round, what they call the circle of samurai and you win $10,000. The circle of samurai, what happens is there are four questions and you get 90 seconds plus whatever you have left over from your first run, to answer those four questions. Answer all four questions correctly, you win $100,000 and the official title of Mental Samurai. >> So there's only two levels, circle of samurai but it gets harder. Now also I noticed that it's, their questions have certain puzzles and there's certain kinds of questions. What's the categories, if you will, what's the categories they offer? >> Yes, so the different categories are knowledge, which is just classic trivia, it's a kind of Jeopardy stuff. There's memory, where they have something on screen that you have to memorize, or maybe they play an audio track that you have to remember what happened. And then there's also sequence where you have to put things in order. So all these different things are represented by these different towers which are these gigantic television screens where they present the questions. And the idea is in order to be truly intelligent, you have to be able to handle all of these different things. You can't just have knowledge. You can't just have pop culture. You got to have everything. >> So on the candidates I saw some from Stanford. >> Yeah. >> I saw an athlete. It's a lot of diversity in candidates. How do they pick the candidates? How did you get involved? Did your phone ring up one day? Were you identified, they've read your blog. Obviously they've, you're smart. I've read your stuff on Facebook. How did you get in there? (laughs) >> Excellent question. So the whole process, there's a giant casting department that does all these things. And there's people who just cast people for game shows. And what happened with me is many years ago back in 2014, my sister worked in Hollywood when I was growing up. She worked for ER and Baywatch and other companies and she still keeps track of the entertainment industry. And she sent me an email saying, hey, here's a casting call for a new show for smart people and you should sign up. And so I replied to the email and said hey I'm Chris Yeh. I'm this author. I graduate from Stanford when I was 19, blah blah blah blah. I should be on your show. And they did a bunch of auditions with me over the phone. And they said we love you, the network loves you. We'll get in touch and then I never heard. Turns out that show never got the green light. And they never even shot that show. But that put me on a list with these various casting directors. And for this show it turns out that there was an executive producer of the show, the creator of the show, his niece was the casting director who interviewed me back in 2014. And she told her uncle, hey, there's this guy, Chris Yeh, in Palo Alto. I think would be great for this new show you're doing. Why don't you reach out to him. So they reached out to me. I did a bunch of Skype auditions. And eventually while I was on my book tour for Blitzscaling, I got the email saying, congratulations, you're part of the season one cast. >> And on the Skype interviews, was it they grilling you with questions, or was it doing a mock dry run? What was some of interview vetting questions? >> So they start off by just asking you about yourself and having you talk about who you are because the secret to these shows is none of the competitors are famous in advance, or at least very few of them are. There was a guy who was a major league baseball pitcher, there's a guy who's an astronaut, I mean, those guys are kind of famous already, but the whole point is, they want to build a story around the person like they do with the Olympics so that people care whether they succeed or not. And so they start off with biographical questions and then they proceed to basically use flash cards to simulate the game and see how well you do. >> Got it, so they want to basically get the whole story arc 'cause Chris, obviously Chris is smart, he passed the test. Graduate when he's 19. Okay, you're book smart. Can you handle the pressure? If you do get it, there's your story line. So they kind of look from the classic, kind of marketing segmentation, demographics is your storylines. What are some of the things that they said to you on the feedback? Was there any feedback, like you're perfect, we like this about you. Or is it more just cut and dry. >> Well I think they said, we love your energy. It's coming through very strongly to the screen. That's fantastic. We like your story. Probably the part I struggle the most with, was they said hey, you know, talk to us about adversity. Talk to us about the challenges that you've overcome. And I tell people, listen, I'm a very lucky guy. A lot of great things have happened to me in life. I don't know if there's that much adversity that I can really complain about. Other people who deal with these life threatening illnesses and all this stuff, I don't have that. And so that was probably the part I struggled the most with. >> Well you're certainly impressive. I've known you for years. You're a great investor, a great person. And a great part of Silicon Valley. So congratulations, good luck on the show. So it's Tuesdays. >> 9 p.m. >> 9 p.m. >> On fox. >> On Fox. Mental Samurai. Congratulations, great. Great to be at the launch party last night. The watch party, there'll be another one. Now your episode comes out on April 30th. >> Yes. So on April 30th we will have a big Bay area-wide watch party. I'm assuming that admission will be free, assuming I find the right sponsors. And so I'll come back to you. I'll let you know where it's going to be. Maybe we should even film the party. >> That's, well, I got one more question on the show. >> Yeah. >> You have not been yet on air so but you know the result. What was it like sitting in the chair, I mean, what was it personally like for you? I mean you've taken tests, you've been involved with the situation. You've made some investments. There's probably been some tough term sheets here and there, board meetings. And all that experience in your life, what was it compared to, what was it like? >> Well, it's a really huge adrenaline rush because if you think about there's so many different elements that already make it an adrenaline rush and they all combine together. First of all, you're in this giant studio which looks like something out of a space-age set with this giant robotic arm. There's hundreds of people around cheering. Then you're strapped into a robotic arm which basically makes you feel like an astronaut, like every run starts with you facing straight up, right? Lying back as if you're about to be launched on a rocket. And then you're answering these difficult questions with time pressure and then there's Rob Lowe there as well that you're having a conversation with. So all these things together, and your heart, at least for me, my heart was pounding. I was like trying very hard to stay calm because I knew it was important to stay clam, to be able to get through it. >> Get that recall, alright. Chris, great stuff. Okay, Blitzscaling. Blitzscaling Ventures. Very successful concept. I remember when you guys first started doing this at Stanford, you and Reid, were doing the lectures at Stanford Business School. And I'm like, I love this. It's on YouTube, kind of an open project initially, wasn't really, wasn't really meant to be a book. It was more of gift, paying it forward. Now it's a book. A lot of great praise. Some criticism from some folks but in general it's about scaling ventures, kind of the Silicon Valley way which is the rocket ship I call. The rocket ship ventures. There's still the other venture capitals. But great book. Feedback from the book and the original days at Stanford. Talk about the Blitzscaling journey. >> And one of the things that happened when we did the class at Stanford is we had all these amazing guests come in and speak. So people like Eric Schmidt. People like Diane Greene. People like Brian Chesky, who talked about their experiences. And all of those conversations really formed a key part of the raw material that went into the book. We began to see patterns emerge. Some pretty fascinating patterns. Things like, for example, a lot of companies, the ones that'd done the best job of maintaining their culture, have their founders involved in hiring for the first 500 employees. That was like a magic number that came up over and over again in the interviews. So all this content basically came forward and we said, okay, well how do we now take this and put it into a systematic framework. So the idea of the book was to compress down 40 hours of video content, incredible conversations, and put it in a framework that somebody could read in a couple of hours. >> It is also one of those things where you get lightning in a ball, the classic and so then I'd say go big or go home. But Blitzscaling is all about something new and something different. And I'm reading a book right now called Loonshots, which is a goof on moonshots. It's about the loonies who start the real companies and a lot of companies that are successful like Airbnb was passed over on and they call those loonies. Those aren't moonshots. Moonshots are well known, build-outs. This is where the blitzscaling kind of magic happens. Can you just share your thoughts on that because that's something that's not always talked about in the mainstream press, is that a lot of there blitzscaling companies, are the ones that don't look good on paper initially. >> Yes. >> Or ones that no one's talking about is not in a category or herd mentality of investors. It's really that outlier. >> Yes. >> Talk about that dynamic. >> Yeah, and one of the things that Reid likes to say is that the best possible companies usually sound like they're dumb ideas. And in fact the best investment he's been a part of as a venture capitalist, those are the ones where there's the greatest controversy around the table. It's not the companies that come in and everyone's like this is a no-brainer, let's do it. It's the companies where there's a big fight. Should we do this, should we not? And we think the reason is this. Blitzscaling is all about being able to be the first to scale and the winner take most or the winner take all market. Now if you're in a market where everyone's like, this is a great market, this is a great idea. You're going to have huge competition. You're going to have a lot of people going after it. It's very difficult to be the first to scale. If you are contrarian and right you believe something that other people don't believe, you have the space to build that early lead, that you can then use to leverage yourself into that enduring market leadership. >> And one of the things that I observed from the videos as well is that the other fact that kind of plays into, I want to get your reaction, this is that there has to be a market shift that goes on too because you have to have a tailwind or a wave to ride because if you can be contrarian if there's no wave, >> Right. >> right? so a lot of these companies that you guys highlight, have the wave behind them. It was mobile computing, SaaSification, cloud computing, all kind of coming together. Talk about that dynamic and your reaction 'cause that's something where people can get confused on blitzscaling. They read the book. Oh I'm going to disrupt the dry cleaning business. Well I mean, not really. I mean, unless there's something different >> Exactly. >> in market conditions. Talk about that. >> Yeah, so with blitzscaling you're really talking about a new market or a market that's transforming. So what is it that causes these things to transform? Almost always it's some new form of technological innovation, or perhaps a packaging of different technological innovations. Take mobile computing for example. Many of the components have been around for a while. But it took off when Apple was able to combine together capacitative touchscreens and the form factor and the processor strength being high enough finally. And all these things together created the technological innovation. The technological innovation then enables the business model innovation of building an app store and creating a whole new way of thinking about handheld computing. And then based on that business model innovation, you have the strategy innovation of blitzscaling to allow you to grow rapidly and keep from blowing up when you grow. >> And the spirit of kind of having, kind of a clean entrepreneurial segmentation here. Blitzscaling isn't for everybody. And I want you to talk about that because obviously the book's popular when this controversy, there's some controversy around the fact that you just can't apply blitzscaling to everything. We just talk about some of those factors. There are other entrepreneurialship models that makes sense but that might not be a fit for blitzscaling. Can you just unpack that and just explain, a minute to explain the difference between a company that's good for blitzscaling and one that isn't. >> Well, a key thing that you need for blitzscaling is one of these winner take most or winner take all markets that's just enormous and hugely valuable, alright? The whole thing about blitzscaling is it's very risky. It takes a lot of effort. It's very uncomfortable. So it's only worth doing when you have those market dynamics and when that market is really large. And so in the book we talk about there being many businesses that this doesn't apply to. And we use the example of two companies that were started at the same time. One company is Amazon, which is obviously a blitzscaling company and a dominant player and a great, great company. And the other is the French Laundry. In fact, Jeff Bezos started Amazon the same year that Thomas Keller started the French Laundry. And the French Laundry still serves just 60 people a day. But it's a great business. It's just a very different kind of business. >> It's a lifestyle or cash flow business and people call it a lifestyle business but mainly it's a cash flow or not a huge growing market. >> Yeah. >> Satisfies that need. What's the big learnings that you learned that was something different that you didn't know coming out of blitzscaling experience? Something that surprised you, something that might have shocked you, something that might have moved you. I mean you're well-read. You're smart. What was some learnings that you learned from the journey? >> Well, one of the things that was really interesting to me and I didn't really think about it. Reid and I come from the startup world, not the big company world. One of the things that surprised me is the receptivity of big companies to these ideas. And they explained it to me and they said, listen, you got to understand with a big company, you think it's just a big company growing at 10, 15% a year. But actually there's units that are growing at 100% a year. There's units that are declining at 50% a year. And figuring out how you can actually continue to grow new businesses quicker than your old businesses die is a huge thing for the big, established companies. So that was one of the things that really surprised me but I'm grateful that it appears that it's applicable. >> It's interesting. I had a lot of conversations with Michael Dell before, and before they went private and after they went private. He essentially was blitzscaling. >> Yeah. >> He said, I'm going to winner take most in the mature, somewhat declining massive IT enterprise spend against the HPs of the world, and he's doing it and VMware stock went to an all time high. So big companies can blitz scale. That's the learning. >> Exactly. And the key thing to remember there is one of the reasons why somebody like Michael Dell went private to do this is that blitzscaling is all about prioritizing speed over efficiency. Guess who doesn't like that? Wall street doesn't like because you're taking a hit to earnings as you invest in a new business. GM for example is investing heavily in autonomous vehicles and that investment is not yet delivering cash but it's something that's going to create a huge value for General Motors. And so it's really tough to do blitzscaling as a publicly traded company though there are examples. >> I know your partner in the book, Reid Hoffman as well as in the blitzscaling at Stanford was as visible in both LinkedIn and as the venture capitalist of Greylock. But also he was involved with some failed startups on the front end of LinkedIn. >> Yeah. >> So he had some scar tissue on social networking before it became big, I'll say on the knowledge graph that he's building, he built at LinkedIn. I'm sure he had some blitzscaling lessons. What did he bring to the table? Did he share anything in the classes or privately with you that you can share that might be helpful for people to know? >> Well, there's a huge number of lessons. Obviously we drew heavily on Reid's life for the book. But I think you touched on something that a lot of people don't know, which is that LinkedIn is not the first social network that Reid created. Actually during the dot-com boom Reid created a company called SocialNet that was one of the world's first social networks. And I actually was one of the few people in the world who signed up and was a member of SocialNet. I think I had the handle, net revolutionary on that if you can believe that. And one of the things that Reid learned from his SocialNet experience turned into one of his famous sayings, which is, if you're not embarrassed by your first product launch, you've launched too late. With SocialNet they spent so much time refining the product and trying to get it perfectly right. And then when they launched it, they discovered what everyone always discovers when they launch, which is the market wants something totally different. We had no idea what people really wanted. And they'd wasted all this time trying to perfect something that they've theoretically thought was what the market wanted but wasn't actually what the market wanted. >> This is what I love about Silicon Valley. You have these kind of stories 'cause that's essentially agile before agile came out. They're kind of rearranging the deck chairs trying to get the perfect crafted product in a world that was moving to more agility, less craftsmanship and although now it's coming back. Also I talked to Paul Martino, been on theCUBE before. He's a tribe with Pincus. And it's been those founding fathers around these industries. It's interesting how these waves, they start off, they don't get off the ground, but that doesn't mean the category's dead. It's just a timing issue. That's important in a lot of ventures, the timing piece. Talk about that dynamic. >> Absolutely. When it comes to timing, you think about blitzscaling. If you start blitzscaling, you prioritize speed over efficiency. The main question is, is it the right time. So Webvan could be taken as an example of blitzscaling. They were spending money wildly inefficiently to build up grocery delivery. Guess what? 2000 was not the right time for it. Now we come around, we see Instacart succeeding. We see other delivery services delivering some value. It just turns out that you have to get the timing right. >> And market conditions are critical and that's why blitzscaling can work when the conditions are right. Our days back in the podcast, it was, we were right but timing was off. And this brings up the question of the team. >> Yeah. >> You got to have the right team that can handle the blitzscaling culture. And you need the right investors. You've been on both sides of the table. Talk about that dynamic because I think this is probably one of the most important features because saying you going to do blitzscaling and then getting buy off but not true commitment from the investors because the whole idea is to plow money into the system. You mentioned Amazon, one of Jeff Bezos' tricks was, he always poured money back into his business. So this is a capital strategy, as well financial strategy capital-wise as well as a business trait. Talk about the importance of having that stomach and the culture of blitzscaling. >> Absolutely. And I think you hit on something very important when you sort of talk about the importance of the investors. So Reid likes to refer to investors as financing partners. Or financing co-founders, because really they're coming on with you and committing to the same journey that you're going on. And one of the things I often tell entrepreneurs is you really have to dig deep and make sure you do more due diligence on your investors than you would on your employees. Because if you think about it, if you hire an employee, you can actually fire them. If you take money from an investor, there's no way you can ever get rid of them. So my advice to entrepreneurs is always, well, figure out if they're going to be a good partner for you. And the best way to do that is to go find some of the entrepreneurs they backed who failed and talked to those people. >> 'Cause that's where the truth will come out. >> Well, that's right. >> We stood by them in tough times. >> Exactly. >> I think that's classic, that's perfect but this notion of having the strategies of the elements of the business model in concert, the financial strategy, the capital strategy with the business strategy and the people strategy, all got to be pumping that can't be really any conflict on that. That's the key point. >> That's right, there has to be alignment because again, you're trying to go as quickly as possible and if you're running a race car and you have things that are loose and rattling around, you're not going to make it across the finish line. >> You're pulling for a pit stop and the guys aren't ready to change the tires, (snapping fingers) you know you're out of sync. >> Bingo. >> Chris, great stuff. Blitzscaling is a great book. Check it out. I recommend it, remember blitz scale is not for anyone, it's for the game changers. And again, picking your investors is critical on this. So if you picked the wrong investors, blitzscaling will blow up in a bad way. So don't, don't, pick properly on the visa and pick your team. Chris, so let's talk about you real quick to end the segment and the last talk track. Talk about your background 'cause I think you have a fascinating background. I didn't know that you graduated when you're 19, from Stanford was it? >> Yes. >> Stanford at 19, that's a great accomplishment. You've been an entrepreneur. Take us through your journey. Give us a quick highlight of your career. >> So the quick highlight is I grew up in Southern California and Santa Monica where I graduated from Santa Monica High School along with other luminaries such as Rob Lowe, Robert Downey, Jr., and Sean Penn. I didn't go at the same time that they did. >> They didn't graduate when they were 17. >> They did not, (John laughing) and Charlie Sheen also attended Santa Monica High School but dropped out or was expelled. (laughing) Go figured. >> Okay. >> I came up to Stanford and I actually studied creative writing and product design. So I was really hitting both sides of the brain. You could see that really coming through in the rest of my career. And then at the time I graduated which was the mid-1990s that was when the internet was first opening up. I was convinced the internet was going to be huge and so I just went straight into the internet in 1995. And have been in the startup world ever since. >> Must love that show, Halt and Catch Fire a series which I love reminiscing. >> AMC great show. >> Just watching that my life right before my eyes. Us old folks. Talk about your investment. You are at Wasabi Ventures now. Blitzscaling Ventures. You guys looks like you're going to do a little combination bring capital around blitzscaling, advising. What's Blitzscaling Ventures? Give a quick commercial. >> So the best way to think about it is for the entrepreneurs who are actually are blitzscaling, the question is how are you going to get the help you need to figure out how to steer around the corners to avoid the pitfalls that can occur as you're growing rapidly. And Blitzscaling Ventures is all about that. So obviously I bring a wealth of experience, both my own experience as well as everything I learned from putting this book together. And the whole goal of Blitzscaling Ventures is to find those entrepreneurs who have those blitzscalable opportunities and help them navigate through the process. >> And of course being a Mental Samurai that you are, the clock is really important on blitzscaling. >> There are actually are a lot of similarities between the startup world and Mental Samurai. Being able to perform under pressure, being able to move as quickly as possible yet still be accurate. The one difference of course is in our startup world you often do make mistakes. And you have a chance to recover from them. But in Mental Samurai you have to be perfect. >> Speed, alignment, resource management, capital deployment, management team, investors, all critical factors in blitzscaling. Kind of like entrepreneurial going to next level. A whole nother lesson, whole nother battlefields. Really the capital markets are flush with cash. Post round B so if you can certainly get altitude there's a ton of capital. >> Yeah. And the key is that capital is necessary for blitzscaling but it's not sufficient. You have to take that financial capital and you have to figure out how to combine it with the human capital to actually transform the business in the industry. >> Of course I know you've got to catch a plane. Thanks for coming by in the studio. Congratulations on the Mental Samurai. Great show. I'm looking forward to April 30th. Tuesdays at 9 o'clock, the Mental Samurai. Chris will be an inaugural contestant. We'll see how he does. He's tight-lipped, he's not breaking his disclosure. >> I've got legal requirements. I can't say anything. >> Just say he's sticking to his words. He's a man of his words. Chris, great to see you. Venture capitalist, entrepreneur, kind of venture you want to talk to Chris Yeh, co-founder, general partner of blitzscaling. I'm John Furrier for theCUBE. Thanks for watching. (upbeat music)

>> Live from London, England, it's theCUBE. Covering .NEXT Conference Europe, 2018. Brought to you by Nutanix. >> Welcome back, I'm Stu Miniman, my cohost Joep Piscaer, and you're watching theCUBE here at Nutanix .NEXT, London, 2018. Happy to welcome back to the program the co-founder, CEO, and chairman of Nutanix, Dheeraj Pandey. Dheeraj, thanks so much. Congratulations on 3500 people here at the third annual European show, and thanks so much for having theCUBE. >> Thank you, my pleasure. >> All right. So, Dheeraj, first of all, you got a lot going on. Big company event here, last night you announced the Q1 2019 earnings. I guess, step back for a second. Nutanix is now, nine years since the founding, you've been public now for a little while, you got to be feeling good. The company's reached a certain size, very respected in the marketplace. So how are you and the team feeling? >> Yeah, well, I tell people that it's actually fun to be a public company. And obviously there is a cost to being a public company, because you're on a quarterly treadmill, in some sense. But Wall Street also keeps you honest. Just like Main Street keeps you honest on quality of product and customer service, Wall Street keeps you honest on spend and what does it really mean to grow at scale. So I like the fact that there is two good streets that are keeping the company honest. And it's really fun to think about capital allocation, one of the big things as you grow. I mean, you're going to spend more than a billion dollars this year alone. How do you allocate capital wisely is something that I think a lot about in (mumbles). >> Yeah. So, at this show, you kind of change some of the positioning of the portfolio. It's the Core, Essentials, and Enterprise, and right, that asset allocation, when I look at Essential, Xi Cloud, there's all these different pieces, some of them through acquisition, some of them created internally. You need to be careful that you don't over-commit, but when do you decide to kill stuff or keep it going, so you got a lot of plates to spin now, a lot more than you did a year or two ago. >> Yeah, absolutely, and it's not just product development. It's also marketing and sales and G&A. I mean, there's other departments we need to think hard about. Like, how do you create brand awareness for these new things? How do you do demand generation? How do you have a specialty sales force? All those things have to be considered, so, nine years, it's been a journey, but it still looks like it's nothing. And we're still a very small company, and we need to think hard about the next five years, in some sense. >> Yeah. So, one of the metrics you gave Wall Street to be able to look at is, what percentage of customers are using more than just the Core? So the Essentials or the Enterprise. And if I got it right, it's up to 19% from 15%, the quarter before. I wonder, is the packaging, how much of that is for Wall Street? Somebody cynically might look and be like, hey, is the Core market slowing down? And therefore you need to expand. We've all seen public companies that need to go into adjacencies, and shouldn't you stick to your knitting? You've got a great solid product with leadership in the marketplace. >> Yep, absolutely. Also, look, we are not bundling them in SKUs so we cannot force customers to actually buy them. We're not doing financial engineering of dollars, because these not SKUs or bundles. This is a journey which is mostly advisory, in some sense. This is how you should start, this is how you should go, and this is advisory for our sellers and our buyers and our channel people. Everybody needs to say, look, have the customer go through the journey. If you had to do what he just said, probably would've bundled them in SKUs and then allocated capital to one or the other. I think, to your other comment about just sticking to the core, Juniper stuck to the core. And many companies out there which just stayed as a single-box company, they stayed at the core. And eventually you realize the market has moved faster than your core itself. So there's this business school thinking, they call it the Icarus Effect. The Icarus Effect is all about, I'm so good at what I do that I can fly to the sun and nothing will happen. But you don't realize that Icarus, the wings were actually pasted using wax. And you go to the sun, and the sun actually melts the wax. So companies like FGI and SUN, Norca, many companies just stuck to one thing. And they couldn't evolve, actually. >> Obviously you're not sticking to the core alone, right? You're expanding the portfolio, I mean, you're not just an infrastructure company anymore. You do so much on top of the infrastructure on-prem. You have so many SAP services, so how do you manage the portfolio in terms of the customer journey? Because there's so much to tell to a customer. How do you sell it? How do you convince a customer to go from Core to Essentials to Enterprise? >> The most important thing is leverage. Is Essentials going to leverage Core, and is the Enterprise going to leverage Essentials and Core itself? Case in point, Files is completely built on top of Core. So every time somebody's using Files, they're also using Core. If you think about Flow, it uses AHV underneath. Frame, and case in point. When it's going to deliver desktops, it's going to use Files because every desktop needs a filer as well. And then when Frame delivers desktops on-prem, it's going to use all the Core. So the important thing is how they don't become disparate things, like they're all going in their own direction, is there a level of progressiveness where you say, well, if you're using the Enterprise features, a lot of them actually go in and drag in the Core as well as Essentials. So how do we build that progressive experience for the customer, where each of these layers are actually being utilized, is the important piece. >> Dheeraj, so, we're talking a lot about the expansion beyond the Core. But there was a pretty significant activity that your team did on Core itself. So the first time I heard about it, it basically said, we're doing an entire file system rewrite. Think of it almost as AoS 2.0. Now, from a product name, I believe it's 5.10, so I might have trouble remembering which release it was, but talk about what went involved in that. Obviously a lot has changed in the nine years since you created it, so. >> Absolutely. Yeah, yesterday in the earnings call I talked about it too, that people scoff at Core infrastructure. Like, oh, it's going to be a commodity because it's good enough infrastructure. But then I argue that there's no such thing as good enough infrastructure. And companies struggle when they don't focus on infrastructure itself. It's like food, shelter, clothing in the Maslow's hierarchy of needs. If you don't get that, then there's no point self-actualizing it. So, Core infrastructure completely destroys network insecurity. You got to get it right. I mean, look at Oracle, how it's struggling with IaaS. And look at Google, they're trying to figure out how to make it relevant for the Enterprise. Azure has like three or four different stacks for infrastructure. One for old 265, one for Azure DB, one for Azure, and now they're rewriting it for Azure itself. VMware has three different infrastructure stacks. One for three tier, where they are very happily, they're saying, look, let EMC, their NetApps actually are underneath, and Cisco's, and stuff like that. And then they have this software-defined infrastructure with commodity servers. And finally, they have VMware-enabled AWS which is going to use AWS services. So now you have three different forks of your core base, in some sense. And for us, what's important is how we use a single core base for everything. So architecture matters. I was arguing yesterday in the earnings call that good enough infrastructure is an oxymoron. You need to get core right before you can go and try to live the other layers of the Maslow's hierarchy of needs, actually. And that's why we went back and thought about, as the workloads were growing and increasing, and we had mission-critical stuff in memory databases, what do we need to really do about the way we lay out the data and lay out the metadata? So as you know, metadata is at the core of anything in systems, and especially storage systems. And the metadata of our erstwhile system was actually very completely distributed. And then we realized that some things can be local, and some things can be distributed, and that's better scale. Again, going back to this understanding of what things can be represented locally for a certain disk versus what things need to be global so that you can go and say, okay, where is this data really located? What drive? But once you go to the drive, you can actually get more metadata. So, again, you're getting more progressive scanning. So at the end of the day, our engineers are constantly thinking about performance and scalability, and how do you change the wings of the plane at 35,000 feet? It's a very big challenge. >> So that's one of the issues, right? So you're still focusing on your own infrastructure layer, right? But many customers do already have presence in a different hardware stack, or the public cloud, or some service provider. So not everything runs on your platform. So how are you planning to deliver the services ensemble to customers that don't necessarily run on AoS? >> So that's the multi-cloud journey, which is basically the enterprise journey of our customers. I said this yesterday in the earnings call as well, that all our services should be available both on-prem and off-prem. This idea of a VPC, that is multi-location, is what hybrid cloud is all about. So how do you get a virtual private cloud to really span multiple clouds in multiple locations? I think you saw from the demos today of how you're really running all of AoS on top of GCP virtual infrastructure. And in the course of the coming year or two, you'll see us do the same thing, BEM at Amazon, BEM at Azure. Because they deliver servers in their data centers and that's leverage for them because they've already gone and spent so much money on data centers that it's easy for them to deliver a physical server that our software can run on top of. And if people are not using AoS, they'll still want to use things like Frame and Beam and COM and other such things like that. >> Yep, Dheeraj, what are you hearing from customers and how do you think of hybrid, as it were? You know, a lot of attention gets played to things like Azure Stack from Microsoft from VMware on AWS, I know you've got some view points on this. >> Yeah, no, in fact, so if you go back five years, hyperconvergence had become a buzz word maybe three, four years ago. And there were a lot of companies doing hyperconvergence. And only one or two have survived and it's us and VMware, basically have survived that. Everybody else has a checkbox because the customers said well, what about that? Will we have a check box? But, it's really about operating system sort of hyperconvergence. And it has to be honest. And it has to really blur the lines between compute and storage and networking and security. I think hybrid needs to be honest and one of the killer things that hybrid needs is blurring the lines between networks, blurring the lines on storage so you can do one click replication and one click fail over. So a lot of those things have required a lot of innovations from us. That's why we were delayed in Xi. We didn't want to just put up data centers and just like that. I mean, if you go back in time to many hardware companies were putting open stack data centers and calling it their new cloud in response to Amazon. And VMware tried vCloud Air. And they had a charter to go spend money. They weren't going to spend a ton of money on hardware. Without even knowing that the cloud is not about data centers. Cloud is about an experience. It's about eCommerce and computing coming together. And you have to be passionate about a catalog. You know, the marketplace, the catalog so that people can really go and consume things from a catalog. I think that's what our experience has been that. Look, if you don't think of it like a retail giant or retail customer, which is what Amazon has done such a good job of. You know, they've thought about computing as an eCommerce problem as opposed to as a compute storage networking problem itself. And those are the lessons that we have learned about hybrid just as much >> Alright, you did a nice job on the keynote, laying out that Nutanix, like your customers, you're going through a journey. The crawl-walk-run, if you will. We got a tease in the keynote this morning about something cloud native. Where you're going. Final question for you is as you look at the company, you said it's still young, where are your customers going, where are some of the things they need to work on, and that Nutanix will mature with them as we look to move forward? >> Well, I mean, look. I think everybody knows where customers are headed. They're questioning who fulfills the promise because the requirements are all the same. They all want to go and use next generation infrastructure, they want to modernize their data centers, the infrastructure. They want to use some things that they want to own, some things they want to rent. The question is, where is the best experience possible? And by that, I mean not just systems experience of hybrid clouds but also customer service and having an ever-growing catalog and being able to deliver things for developers and devops. And technology will come and go. Two, three years ago, the Puppet and Chef were the hottest thing on, now today, it's Kubernetes. Tomorrow, it's going to be something else. It's the fact that what you see is what you do. And what you do is what you say. In our business, it's about integrity. I was arguing about this yesterday in the earnings call, as well, that building business software is a little bit easier. I shouldn't trivialize it as much but if people use business software, they can work around weaknesses of business software. But if you are in the business of infrastructure, applications cannot work around weaknesses of infrastructure. So integrity matters a lot in our space, actually, and that is about great products, great customer service, fast innovation, recovering fast, being resilient. Those are the things that we focus a lot on. >> Alright, well, Dheeraj, thanks again, always. We didn't even get to talk about the width part, the fourth H that you've been talking about for the honest, humble, and hungry. So, thank you. Congratulations to the team and always appreciate you having on our program. >> My pleasure. >> Alright, for Joep Piscaer, I'm Stu Miniman. Stay with us. Two days live of wall to wall coverage. Thanks for watching theCUBE. (light music) >> I have been in the software and technology industry for over 12 years now. And so I've had the opportunity as a marketer.

>> Live from San Francisco, it's theCUBE covering DockerCon '18, brought to you by Docker and its ecosystem partners. >> Welcome back to theCUBE, we are live at DockerCon 2018 on a stunning day in San Francisco at Moscone West. I'm Lisa Martin with John Troyer and we're excited to welcome two new folks to theCUBE from Skytap. We've got Lucas Welch, the senior director of communications and Hamish Hill, technical product marketing manager. Hey, guys. >> Great to be here, thanks for having us. >> And thanks for adding a lot of color, Lucas, to our set. >> Well, I just wanted to bring enough flair that you could realize I might have something interesting to say. >> Awesome, so speaking of interesting things, tell us about Skytap, what do you guys do, who are you, where are you based? >> Great, so Skytap. Founded in 2006, so relatively old by start-up standards but it's allowed us to learn a lot about where clouded option has been going. And what we've seen is there really is an overlooked challenge that enterprises are facing today, right? So, cloud native development, growing rapidly, gonna continue to develop, but what do you do with all your old stuff, your existing applications? And so Skytap is a cloud purpose-built for modernizing those traditional applications and we do that through a process we call IPA, although we don't advocate that you drink on the job. It's Infrastructure, Process, and Architecture. And the idea is to really get yourself to true modernization and making the most of cloud, and containers, and all of the modern technologies we can see on the show floor behind us, you first need to modernize the infrastructure, get yourself out of the data center. From there, in eliminating that barrier, you're gonna be able to modernize the processes. How do you develop, how do you change your applications? And by getting better in that regard, adopting things like DevOps and agile methodologies, finally you can start to make changes to the application themselves. So, in short order, we are the cloud for modernizing traditional applications and we like to see ourselves as complementary to the folks at AWS behind us and others who are best for that cloud native web scale development of new applications. >> Great conversation this morning at the Key Note about modernizing applications. I think it's on everybody's mind because the world does not start fresh and new every day, right? We all are working with things that we've been carrying, for some cases, for years and decades. So Docker is talking about, in fact we modernized a .NET application, I think, this morning so they showed a little bit of a demo with that and kubernetes. Can you talk a little bit about how you work with Docker and, you know, some of the challenges that you work with in terms of modernizing applications? >> Yeah, Docker has a great framework with what they have with their MTA. Actually, our VP of Product, Dan Jones, presented yesterday on making modernization magical and really looking at how Skytap complements what Docker has with their MTA framework. And I think Skytap provides, with our IPA approach, a great platform for enterprises to execute the Docker MTA approach and beyond that, sort of what Skytap provides is the abilities, sort of, to move out of the data center and get away from the hardware side of things, and start to leverage some of the scale that you can get out of the cloud. >> What are some of the things that an enterprise, that a legacy application expects that it's not gonna have if you just lift and shift it. You know, why do we need Skytap? >> Yeah, I think what's important to remember is often, even just lifting and shifting it is very difficult because if you want a monolithic or traditional application that's very much wed to the infrastructure it was built on five, 10, 15 years ago, taking that and putting it in a hyper scale provider often means you gotta rewrite from scratch and that's a really arduous process, often one that creates a skills challenge in and of itself because not only do you need people to manage the existing application, you need a whole set of new skills to take a cloud-like approach to that development. So that can create a lot of challenges and so what we see here at DockerCon, really the reason we're here, is both Docker and Skytap see the next wave of cloud, the next wave of modern development, is gonna be, "How do we bring all these benefits we've seen "in Cloud Native development "to those existing applications?" and what we see ourselves doing is eliminating that infrastructure barrier so then you can really start using containers to their full benefit, whether it's in Skytap cloud, in another cloud, or both. >> So, just to follow up a little bit. So it's not just some services like, I don't know, you've gotta have authentication, and you've gotta have storage, and you've gotta have all the things that an old legacy application, sitting in a data center, expects. But it sounds like, also, there's operational services as well and being able to operate with that kind of cloud-level agility? >> Yeah, what we provide with Skytap is, you know, we have a concept of a Skytap environment and so within that environment, you can have your traditional X86, sort of, VMware-based workloads. We also support IBM power systems. So we're the only cloud that can run AIX workloads and Linux on power and so alongside that, what we get is sort of the combination of being able to bring in containers as well and so as organizations go through that modernization journey, being able to receive or see value in the hybrid applications, sort of, along the way. >> We saw a lot of stats, thanks Lucas, this morning I think one of the first ones that I saw was in the press release that Docker released which was, this morning, 85% of enterprise organizations are running a multi-cloud strategy, so that's pretty pervasive. We're also seeing stats like, up to 90%, we had Scott Johnston on earlier, their Chief Product Officer, up to 90% of enterprises are spending, sorry. Enterprises are spending up to 90% of their IT budgets just keeping the lights on for traditional applications. As you said, lift and shift isn't practical for a number of reasons. You also talked about, you know, skill-set changes there. So I'm curious, what are some of the, kind of, common challenges you're seeing in the customer environment where they might be trepidatious to go to the container journey and how specifically does Skytap and Docker knock those out of the park? >> Yeah, well those stats, I think, are really indicative of the challenge and then the new approaches that companies are trying to take to solve that challenge which is, you have so much invested in what's made your company successful, and if you're a long-standing enterprise doing well on your market, you've been doing this for 10, 15, 20, maybe more, years and you've done very well to get yourself to where you are. You've invested millions, if not billions in your infrastructure, your talent, and the people that build the systems that run your business so to burn that all to the ground and start from scratch doesn't make a lot of sense and so, I think, one challenge you run into is inertia, right. It's like "Hey, we did well to get here, "why do we need to suddenly change everything we're doing?" And Skytap's recommendation is you don't need to change everything, but you do need to prepare to be able to change much more rapidly as our economy continues to be more driven by digital technique. So you have inertia as a challenge. I think you also have that idea that if you're spending 90%, as you said, right, of what you just got with the lights on, where's the money and where's the time gonna come for net new and how can you bring those two together? And so that's really where I think Skytap would play a big role is bridging that gap from where you are today, allowing you to leverage the people that you have, the skills that they have, the technology that you have invested in. So you don't have to throw that all out overnight. And instead you can get more and more value out of it as you bring it into the cloud, gain incremental agility, and then, over time, make the modernization and evolutionary changes you want to make based on business needs, not having technology drive what your business does. >> How much of that is a cultural change that you guys can help companies understand is essential? Because culture, change in culture is obviously, especially with large enterprises, they can't pivot that quickly, but culture is essential for a company to successfully undergo digital transformation. I'm just wondering, what kind of conversations are you seeing with that inertia? How much of it is culture needs to change and mindsets to embrace, you know, moving forward? >> Yeah, I mean, we see a lot of this in the conversations we have with customers. We see a lot of it comes out of the market from what analysts sort of have to say as well and I think reasons out of an analyst article that was shared sort of publicly so it talks about, actually, enterprises who have adopted DivOps first are actually more successful in the move to containerization and that's what we see, sort of, with the customers that come to us. And what we're able to provide and what the customers see in Skytap is, actually, the simplicity of the UI that we provide is, actually, a good step from what they currently have without sort of needing to get into what can often be multiple UI's and screens in some of the hyper scale cloud providers. And then on top of that, sort of, the on-demand access to environment so you're taking away, sort of, what is typically a reactive approach from corporate IT when they need to reach out and go, "Hey, I need "another environment or I need another BM like this." And these organizations, it's often taking maybe six to eight weeks to get those environments turned around. We can provision a complex environment in less than, sort of, 30 seconds in Skytap. So it enables those teams to be a lot more productive in what they're doing. And there's sort of the first phase of deciding to sort of adopt the changing culture before sort of even getting into that move from, sort of, after linking in with legacy applications, you've gotta waterfall SDLC, and so actually moving from there into, sort of, more agile approaches and looking at how you can increase release cadence and what, sort of, comes into that from a people aspect, and a process change, and a methodology, and how Skytap, sort of, supports that along with integration with other third party's automation tools as well. >> Yeah, I think you nailed it on the culture point and I just wanted to not forget about people as being a big part of culture, right? And you have, fear is a very real thing, right? Fear of change, fear of net new. And so in our own adoption of Docker, and containers, and kubernetes internally. SO our cloud runs on a very large kubernetes cluster of containerized services so internally, over the last few years, we went through our own modernization journey. And I think that, paired with some research we've seen, we recently did a study with 451 Research looking at what enterprise tech leaders are experiencing. The fear of change, the reticence to change, and then just the lack of knowledge of, "Okay, what is required of me?" Like, "You're asking me to change overnight. "All of a sudden I have to take classes at night "while I do my day job." I think these are really, very realistic and human questions to ask and I think you need to take that into account when you're looking at digital transformation, modernization, so thinking about, "Hey, how do we communicate, "with transparency, what we expect and the time frame?" Let's be upfront about the challenges we expect to run into and where we're gonna have problems and how we'll deal with those together. And make sure the communication is crisp, and clear, and consistent, so that people at least know what's going on, even if they may not like it upfront. >> Well, Lucas, you brought up kubernetes and containers, right? We're here at DockerCon, so, obviously, containers on the tip of everybody's tongue. But you also work with legacy apps, which traditionally, I suppose at this point, traditional means a VM. So how does that go together? What are you looking at your customers? Are they able to transition to more containerized infrastructure? Are they sticking with VMs? I mean, how do modern containers fit into the Skytap platform here? >> Yeah, I think we're seeing a lot of adoption with our customers who are moving into Skytap with their traditional applications and we continue to, sort of, learn and observe what they're doing. For us, there's two types of customers that move to Skytap. The first of those are really looking to migrate their whole data center or evacuate the data centers going, "Where can I put these legacy applications?" You know, there's not many places they can sort of go and so they move them into Skytap, get them up and running in there, and sort of see some benefits in that. And then, almost organically, start to look at going, well how else can I make, or get my team to be sort of more cloud-native or cloud reading. It's sort of an evolution of the people component we were talking about before and sort of going, all right, well as I get my teams more ready for cloud native, they start to sort of move towards containers and cloud native services. For our, sort of, other organizations that come in are those who already know and probably have already experienced, you know, other cloud, sort of, modernizations and are looking at what have they been able to achieve and what do they learn from that. And seen the value in actually Skytap and actually come to us with the approach of going, "Right, we want to come in here. "We want to move to more agile sort of methods. "We want to, sort of, start to take our traditional "monolithic applications, break it down "into into microservices, and move it into containers." >> I'm curious. One of the things that Steve Singh, the CEO of Docker, said this morning during his keynote was, about half the room, there's about five to six thousand people here at DockerCon, their fifth conference, that only about half of them are already on this containerization journey. I'm curious, and I know there's no one-size-fits-all, but when you're talking to customers who are at the preface of going, "All right, we've gotta do this. "This is really an essential component "of our transformation." What's the time frame that they could look to see measurable business impact once they start working with Skytap and Docker on this container journey? >> Yeah, well, I think we've gotta move away collectively as an industry from the idea that there's a Big Bang or silver bullet approach to change, right? I spent the five previous years before joining Skytap last year at a company called Chef Software, competes with Puppet, who's here on the show floor. Automation software. And what I saw there in terms of both DevOps adoption, adoption of automation, and the transition to the cloud, is that if you think you can get everybody full sale on the same amount of change at the same time, to do that effectively in a relatively reasonable amount of time, you're going to not only fail, but by failing, you actually set yourself further back than had you taken a more iterative approach. So I think from a time perspective, I think the first answer is you'll never be done so presume that the journey will continue into perpetuity because continuing to gain agility, continuing to get better at delivering software, to deliver value to customers, I don't see an end to that in any sort of near-term in our economy so I think that's gonna go on for a long time. So digital transformation, modernization, whatever buzz word people may want to use, the idea of evolving and changing is an ongoing process. I think, then, business leaders will say, "Well, that's baloney, I need change now. "I want results." I think, start with a project that has a deadline associated with it, alright? We need to be able to deliver our customer banking app online, via mobile, by January. Okay, well, bite that off singularly and so that you focus on that first, you learn from how you do that process, and then you can take those learnings, communicate them, and pick another project and another project. So we recommend kind of an iterative, progressive approach that will put time and measurable goals around a specific project, meet those deadlines, hopefully, if you're successful, and then give you a lot to learn and operate off of the next time. >> That's great. I'm really kind of curious about looking forward and economic models. You know, everything is as a service at this point. You have a lot of traditional providers, the Dells and HPEs of the world who sell a lot of hardware still and sell a lot of things upfront and they, the analysts and everyone else scratching their heads about how they get to sell more services along with that. Skytap's already there. You're selling your cloud provider, you're selling a service, in some ways you're replacing some of the infrastructure or, you know, an adjunct to it. I'm just kind of curious, going forward, I mean, is this the future of cloud? As a service provider, how do you see the economic model of the DNA of Skytap partnering with people? We've ended up talking about process and people more than we've ended up talking about technology today. Which is kind of fascinating. But is that, project us into the future, what do you all see? >> Yeah, I think what we see today with cloud, and the microservices and container model is really the evolution of what was sort of the virtual data center and developing in sort of VMs. And so sort of going a step beyond that, we're seeing the container model grow and as you rightly pointed out, we talked a lot about people and process and I think that sort of was what's holding back a little of the enterprise adoption today and I think as organizations get into this sort of process and mindset, almost and sort of going, "Hey, things are gonna continue to evolve over time "and our organizations need to be "ready to adopt a lot of these." And this isn't just sort of your development level as well as looking at right, well how does your corporate IT teams, how do your security teams and other parts of the business realize this is gonna continue to evolve really quickly? And I think that's what we're gonna continue to see, sort of up front and it's gonna drive a lot of the adoption of the cloud native services and containers but it's gonna take a bit of time for some organizations to get there. >> Yeah, I have a soapbox, I want to stand on it real quickly. I think cloud is the way forward, right? So no one wants to be in the infrastructure business long-term. So I think regardless of what your deployment model will be, most businesses, five, 10, 15, 20 years from now, I don't see them owning a lot of data center real estate, right? So make the infrastructure someone else's problem. Whether that's Skytap, whether that's AWS, whether that's Azure, or, frankly, whether that's all of us, to your multi-cloud statistic, right? We see the same thing. It's much like the data center was today and has been for a long time. Use the right tool for the right job. You've got a mix of technologies so you're not locked in to any single vendor and you're able to fit technology to your business needs so I think, one, we're going cloud and that's gonna be the way it is. I think, two, is open source, right? I mean, that's where containers gained all their momentum where Docker did a fantastic job of really giving a vibrant community of developers an opportunity to do their work much more easy, much easier and much faster. And so I think you'll continue to see open source play a much larger component in how, even very large, long-standing businesses, develop what they're doing. And then you bring those two together, right? You look at, how can the cloud ecosystem best support open source tools to deliver and develop software that's gonna add value at the end of the day. >> Guys, I wish we had more time. Thank you so much for stopping by and sharing with us what Skytap is doing and how you're enabling customers to not just evolve from a technology standpoint but, I think, as we've all talked about here, really, what might even be more important is evolve the people and the processes. So thanks Lucas, thanks Hamish. Thanks for your time. >> Thank you so much for having us. >> We wanna thank you for watching theCUBE. Again, I'm Lisa Martin with John Troyer from DockerCon 2018. Stick around, we'll be right back with our next guest. (upbeat music)

>> Announcer: Live from Las Vegas, it's the Cube. Covering Dell Technologies World 2018. Brought to you by Dell EMC, and its ecosystem partners. >> Welcome back to SiliconANGLE media's coverage of Dell Technologies World 2018. I'm Stu Miniman here with my cohost Keith Townsend, happy to welcome back to the program Tom Burns, who's the SVP of Networking and Solutions at Dell EMC. Tom, great to see ya. >> Great to see you guys as well. Good to see you again. >> All right, so I feel like one of those CNBC guys. It's like, Tom, I remember back when Force10 was acquired by Dell and all the various pieces that have gone on and converged in infrastructure, but of course with the merger, you've gotten some new pieces to your toy chest. >> Tom: That's correct. >> So maybe give us the update first as to what's under your purview. >> Right, right, so I continue to support and manage the entire global networking business on behalf of Dell EMC, and then recently I picked up what we called our converged infrastructure business or the VxBlock, Vscale business. And I continue also to manage what we call Enterprise Infrastructure, which is basically any time our customers want to extend the life of their infrastructure around memory, storage, optics, and so forth. We support them with Dell EMC certified parts, and then we add to that some third-party componentry around rack power and cooling, software, Cumulus, Big Switch, things like that. Riverbed, Silver Peak, others. And so with that particular portfolio we also cover what we call the Dell EMC Ready Solutions, both for the service provider, but then also for traditional enterprises as well. >> Yeah, well luckily there's no change in any of those environments. >> Tom: No, no. >> Networking's been static for decades. I mean they threw a product line that I mean last I checked was somewhere in the three to four billion dollar range. With the VxBlock under what you're talking there. >> Yeah it's a so, yeah-- >> Maybe you could talk, what does this mean? 'Cause if I give you your networking guy. >> Right. >> Keith and I are networking guys by background, obviously networking's a piece of this, but give us a little bit of how the sausage is made inside to-- >> Tom: Sure. >> Get to this stuff. >> Well I think when you talk about all these solutions, Cloud, Hybrid Cloud, Public Cloud, when you think about software-defined X, the network is still pretty darn important, right? I often say that if the network's not working, it's going to be a pretty cloudy day. It's not going to connect. And so the fabric continues to remain one of the most critical parts of the solution. So the thought around the VxBlock and moving that in towards the networking team is the importance of the fabric and the capability to scale out and scale up with our customers' workloads and applications. So that's probably the reason primarily the reason. And then we can also look at how we can work very closely with our storage division 'cause that's the key IP component coming from Dell EMC on the block side. And see how we can continue to help our customers solve their problems when it comes to this not your do-it-yourself but do-it-for-me environment. >> All right, I know Keith wants to jump in, but one just kind of high-level question for you. I look at networking, we've really been talking about disaggregation of what's going on. It's really about disaggregated systems. And then you've got convergence, and there's other parts of the group that have hyper convergence. How do you square the circle on those two trends and how do those go together? >> Well, I think it's pretty similar on whether you go hyper converge, converge, or do-it-yourself, you build your own block so to speak. There's a set of buyers that want everything to be done for them. They want to buy the entire stack, they want it pre-tested, they want it certified, they want it supported. And then there's a set of customers that want to do it themselves. And that's where we see this opportunity around disaggregation. So we see it primarily in hyperscale and Cloud, but we're seeing it more and more in large enterprise, medium enterprise, particular verticals where customers are in essence looking for some level of agility or capability to interchange their solutions by a particular vendor or solutions that are coming from the same vendor but might be a different IP as an example. And I'm really proud of the fact that Dell EMC really kicked off this disaggregation of the hardware and software and networking. Some 4 1/2 years ago. Now you see some of the, let's say, larger industry players starting to follow suit. And they're starting to disaggregate their software as well. >> Yeah, I would have said just the commonality between those two seemingly opposed trends it's scale. >> Right. >> It's how do customers really help scale these environments? >> Exactly, exactly. It depends a lot around the customer environment and what kind of skill sets do they have. Are they willing to help go through some of that do-it-yourself type of process. Obviously Dell EMC services is there to help them in those particular cases. But we kind of have this buying conundrum of build versus buy. I think my old friend, Chad Sakac, used to say, there's different types of customers that want a VxRail or build-it-themselves, or they want a VxBlock. We see the same thing happen in a networking. There's those customers that want disaggregated hardware and software, and in some cases even disaggregated software. Putting those protocols and features on the switch that they actually use in the data center. Rather than buying a full proprietary stack, well we continue to build the full stack for a select number of customers as well because that's important to that particular sector. >> So again, Tom, two very different ends of the spectrum. I was at ONS a couple of months ago, talked to the team. Dell is a huge sponsor of the Open Source community. And I don't think many people know that. Can you talk about the Open Source relationship or the relationship that Dell Networking has with the Open Source community? >> Absolutely, we first made our venture in Open Source actually with Microsoft in their SONiC work. So they're creating their own network operating software, and we made a joint contribution around the switch abstraction interface, or side. So that was put into the Open Compute Project probably around 3 1/2, maybe four years ago. And that's right after we announced this disaggregation. We then built basically an entire layer of what we call our OS10 base, or what's known in the Linux foundation as OPX. And we contributed that to the OPX or to the Linux foundation, where basically that gives the customer the capability through the software that takes care of all the hardware, creates this switch subtraction interface to gather the intelligence from the ASIC and the silicon, and bringing it to a control plane, which allows APIs to be connected for all your north-bound applications or your general analysis that you want to use, or a disaggregated analysis, what you want to do. So we've been very active in Linux. We've been very active in OCP as well. We're seeing more and more of embracing this opportunity. You've probably seen recently AT&T announced a rather large endeavor to replace tens of thousands of routers with basically white box switches and Open Source software. We really think that this trend is moving, and I'm pretty proud that Dell EMC was a part of getting that all started. >> So that was an awful lot of provider talk. You covered both the provider's base and the enterprise space. Talk to us about where the two kind of meet. You know the provider space, they're creating software, they're embracing OpenStack, they're creating plug-ins for disaggregated networking. And then there's the enterprise. There's opportunity there. Where do you see the enterprise leveraging disaggregation versus the service provider? >> Well, I think it's this move towards software-defined. If you heard in Michael's keynote today, and you'll hear more tomorrow from Jeff Clarke. The whole world is moving to software-defined. It's no longer if, it's when. And I think the opportunity for enterprises that are kind of in that transformation stage, and moving from traditional software-defined, or excuse me, traditional data centers to the software-defined, they could look at disaggregation as an opportunity to give them that agility and capability. In a manner of which they can kind of continue to manage the old world, but move forward into the new world of disaggregation software-defined with the same infrastructure. You know it's not well-known that Dell EMC, we've made our switching now capable of running five different operating softwares. That's dependent upon workloads and use cases, and the customer environment. So, traditional enterprise, they want to look at traditional protocols, traditional features. We give them that capability through our own OS. We can reduce that with OS partners, software coming from some of our OS partners, giving them just the protocols and features that they need for the data center or even out to the edge. And it gives them that flexibility and change. So I think it really comes at this point of when are they going to move towards moving from traditional networking to the next generation of networking. And I'm very happy, I think Dell Technologies is leading the way. >> So I'm wondering if you could expand a little bit about that. When I think about Dell and this show, I mean it is a huge ecosystem. We're sitting right near the Solutions Expo, which will be opening in a little bit, but on the networking side, you've got everything from all the SD-WAN pieces, to all the network operating systems that can sit on top. Maybe, give us kind of the update on the overview, the ecosystem, where Dell wins. >> Yeah, yeah I mean, if you think about 30-something years ago when Michael started the company and Dell started, what was it about. It was really about transforming personal computing, right? It was about taking something that was kind of a traditional proprietary architecture and commoditizing it, making sure it's scalable and supportable. You think of the changes that's occurred now between the mainframe and x86. This is what we think's happening in networking. And at Dell Technologies in the networking area whether it's Dell EMC or to VMware, we're really geared towards this SDX type of market. Virtualization, Layer two, day or three disaggregated switching in the data center. Now SD-WAN with the acquisition of Velocloud by VMware. We're really hoping customers transform at the way networking is being managed, operated, supported to give them much more flexibility and agility in a software-defined market. That being said, we continue to support a multitude of other partners. We have Cumulus, Big Switch, IP infusion, and Pluribus as network operating software alternatives. We have our own, and then we have them as partners. On the SD-WAN area while we lead with Velocloud, we have Silver Peak and we also have Versa Technology, which is getting a lot of upkick in the area. Both in the service provider and in the enterprise space. Huge area of opportunity for enterprises to really lower their cost of connectivity and their branch offices. So, again, we at Dell, we want to have an opinion. We have some leading technologies that we own, but we also partner with some very good, best-of-breed solutions. But being that we're open, and we're disaggregated, and we have an incredible scaling and service department or organization, we have this capability to bring it together for our customers and support them as they go through their IT transformation. >> So, Dell EMC is learning a lot of lessons as you guys start to embrace software-defined. Couple of Dell EMC World's ago, big announcement Chad talked about, ScaleIO, and abstracting, and giving away basically, ScaleIO as a basic solution for free. Then you guys pulled back. And you said, you know what, that's not quite what customers want. They want a packaged solution. So we're talking on one end, total disaggregation and another end, you know what, in a different area of IT, customers seem to want packaged solutions. >> Tom: Yeah. >> Can you talk to the importance of software-defined and packaged solutions? >> Right, it's kind of this theory of appliances, right? Or how is that software going to be packaged? And we give that flexibility in either way. If you think of VxRail or even our vSAN operating or vSAN ready node, it gives that customer the capability to know that we put that software and hardware together, and we tested it, we certified it, most importantly we can support it with kind of one throat to choke, one single call. And so I think the importance for customers are again, am I building it myself or do I want to buy a stack. If I'm somewhere in the middle maybe I'm doing a hybrid or perhaps a Rail type of solution, where it's just compute and storage for the most part. Maybe I'm looking for something different on my networking or connectivity standpoint. But Dell EMC, having the entire portfolio, can help them at any point of the venture or at any part of the solution. So I think that you're absolutely right. The customer buying is varied. You've got those that want everything from a single point, and you got others that are saying I want decision points. I think a lot of the opportunity around the cost savings, mostly from an Opex standpoint are those that are moving towards disaggregated. It doesn't lock 'em in to a single solution. It doesn't get 'em into that long life cycle of when you're going to do changes and upgrades and so forth. This gives them a lot more flexibility and capability. >> Tom, sometimes we have the tendency to get down in the weeds on these products. Especially in the networking space. One of my complaints was, the whole SDN wave, didn't seem to connect necessarily to some of the big businesses' challenges. Heard in the keynote this morning a lot of talk about digital transformation. Bring us up to speed as to how networking plays into that overall story. What you're hearing from customers and if you have any examples we'd love to hear. >> Yeah, no so, I think networking plays a critical part of the IT transformation. I think if you think of the first move in virtualization around compute, then you have the software-defined storage, the networking component was kind of the lagger. It was kind of holding back. And in fact today, I think some analysts say that even when certain software-defined storage implementations occur, interruptions or issues happen in the network. Because the network has then been built and architected for that type of environment. So the companies end up going back and re-looking at how that's done. And companies overall are I think are frustrated with this. They're frustrated with the fact that the network is holding them back from enabling new services, new capabilities, new workloads, moving towards a software-defined environment. And so I think this area again, of disaggregation, of software-defined, of offering choice around software, I think it's doing well, and it's really starting to see an uptick. And the customer experiences as follows. One is, open networking where it's based upon standard commodity-based hardware. It's simply less expensive than proprietary hardware. So they're going to have a little bit of savings from the CapEx standpoint. But because they moved towards this disaggregated model where perhaps they're using one of our third-party software partners that happens to be based in Linux, or even our own OS10 is now based in Linux. Look at that, the tools around configuration and automation are the same as compute. And the same as storage. And so therefore I'm saving on this configuration and automation and so forth. So we have examples such as Verizon that literally not only saves about 30% cost savings on their CapEx, they're saving anywhere between 40 and 50% on their Opex. Why? They can roll out applications much faster. They can make changes to their network much faster. I mean that's the benefit of virtualization and NSX as well, right? Instead of having this decisions of sending a network engineer to a closet to do CLI, down in the dirt as you would say, and reconfigure the switch, a lot of that now has been attracted to a software lever, and getting the company much more capability to make the changes across the fabric, or to segregate it using NSX micro segmentation to make the changes to those users or to that particular environment that needs those changes. So, just the incredible amount of flexibility. I think SDN let's say six, seven years ago, everyone thought it was going to be CapEx. You know, cheaper hardware, cheaper ASICs, et cetera. It's all about Opex. It's around flexibility, agility, common tool sets, better configuration, faster automation. >> So we all have this nirvana idea that we can take our traditional stacks, whether it's pre-packaged CI configurations that's pre-engineered, HCI, SDN, disaggregated networking. Add to that a software layer this magical automation. Can you unpack that for us a little bit? What are you seeing practically whether it's in the server provider perspective or on the enterprise. What are those crucial relationships that Dell EMC is forming with the software industry to bring forth that automation? >> Well obviously we have a very strong relationship with VMware. >> Keith: Right. >> And so you have vRealize and vROps and so forth, and in fact in the new VxBlock 1000, you're going to see a lot of us gearings, a lot of our development towards the vRealize suite, so that helps those customers that are in a VMware environment. We also have a very strong relationship with Red Hat and OpenStack, where we've seen very successful implementations in the service provider space. Those that want to go a little bit more, a little bit more disaggregated, a little bit more open, even it from the storage participation like SAP and so forth. But then obviously we're doing a lot of work with Ansible, Chef, and Puppet, for those that are looking for more of a common open source set of tools across server, compute, networking storage and so forth. So I think the real benefit is kind of looking at it at that 25,000-foot view on how we want to automate. Do you want to go towards containers, do you want to go traditional? What are the tool sets that you've been using in your compute environment, and can those be brought down to the entire stack? >> All right, well Tom Burns, really appreciate catching up with you. I know Keith will be spending a little time at Interop this week too. I know, I'm excited that we have a lot more networking here at this end of the strip also this week. >> Appreciate it. Listen to Pat's talk this afternoon. I think we're going to be hearing even more about Dell Technology's networking. >> All right. Tom Burns, SVP of Networking and Solutions at Dell EMC. I'm Stu Miniman and this is Keith Townsend. Thanks for watching The Cube. (upbeat music)

>> Announcer: Live from Las Vegas, (electronic music) it's theCUBE. Covering IBM Think 2018. Brought to you by IBM. >> We're back at IBM Think 2018. This is theCUBE, the leader in live tech coverage. My name is Dave Vellante, and this is day two of our wall-to-wall coverage of IBM Think. We've been doing IBM shows for years. This is the big, consolidated show, 30 to 40 thousand people, too many people to count. Cameron Clayton is here. He is a GM of Watson Content and IoT Platform at IBM. Thanks for coming on. >> Thanks very much for having me. >> So quite a show, right? Standing room only! >> A large, large show. >> Standing room only and also great announcements. >> So tell us about your announcements. >> Yeah, so we got to couple of things we're really, really excited about. The team's been working really hard on for the last few months. One is a way to train Watson to make Watson even smarter than it already is out of the box. And so, we've been building data kits by vertical industry. So for financial services, for travel and transportation, for the hospitality industry, for health care and for government, on how do you give Watson a high machine IQ right out of the gate as opposed to having to train it in your area of industry. And so, once again, we're really focused on making Watson the AI system for Enterprise, and this is another step on that journey to make Watson really, really smart. >> It's really prioritizing it in a way that's much easier to consume. >> Much easier to consume, and if you think about it, there's a lot of jargon in each industry, right? To be an expert in industry, you got to know a lot of jargon, understand the context of that. An AI system doesn't know that unless it's taught that. And so we are teaching Watson that. And then how to apply it successfully in each of those industries. So it's a pretty material leap forward in how we're training Watson. >> So it hits the content component >> Cameron: Hits the content. >> And then industries you're knocking down? Where are you starting? >> Yeah, so we're starting with financial services. We're launching in travel and transportation and in hospitality. So we're basically, this is a pretty fun one, I love food. But basically Watson went out and scanned the entire internet and collected all the recipes that it could find on the internet and trained itself on food. And so, you can ask it now questions about food, what restaurants, about really specific things. If you're a vegan you can find out what's available near you. If you're gluten intolerant, you can find out things on the menu like that. But then there's other things, like in the travel and transportation industry. Virtual agents for travel agents, they can ask questions of Watson, and it can ask very specific, very deep things, very much like a human would. And so you can say a simple thing like, "Where should I stay in New York?" And a human would respond, "Well, are you a member of any hotel rewards program?" Normal AI chatbot wouldn't. It would just say, "These are the lists of the 4,000 hotels in New York." Watson will actually ask human-like questions to give you the best answer possible. But all that requires training, and that's what were built in with these Watson content data kits, and we're really excited about 'em. >> So I'll come back to that. But so if I take that example of Watson Chef, there's this discussion on AI for the enterprise versus AI for consumers. >> Right. Are you crossing over? That was kind of a consumer-y application. >> Cameron: Yeah. >> Is that just an example? >> It's just an example. No, it's very much about AI for the enterprise, right? And so the four priority industries that we're focused on, first is financial services, sort of the sweet spot for IBM. The second is supporting our government clients to make sure that Watson is trained in the language and nuisances the of government. The third is Watson health, so the health care industry, both the regulation and the language itself. So everything from pharmacology, et cetera. And then the fourth is travel and transportation. So it's very much about making Watson the smartest AI system for enterprise. That's absolutely its focus. >> What's the IoT angle in your title? >> Yeah, so-- >> What's going on there? >> I run the IoT platform for IBM, and so The Weather Company, which is how I joined IBM, which I also run, really is one of the largest IoT platforms in the world, which was actually a big part of the acquisition case for acquiring The Weather Company. We're now bringing the ability to ingest 35 to 40 billion data requests every day with The Weather Company platform to the IoT platform. We've combined those things together. So we can ingest data and content at a scale unlike pretty much anyone else in the world, sort of second only to Google in terms of the scale of data and content we can ingest. And we use that data to help train Watson on one hand, and on the other hand, to support our clients in multiple industries around the world. >> Yeah, I remember when IBM did that acquisition, Bob Picciano told me, "Well, you got to understand. "This is an IoT play as much as it is a data science play." So how has that evolved, come together, with IBM's core? >> Yeah, so I think in a couple of ways. One is, it's taken the way the company was mostly a domestic US business. IBM, in the last couple of years, has globalized that business in a very material way. A great example is in aviation, where we have the top 30 US operators. Now we have hundreds of operators all around the world helping them make decisions every day. At its core, this IoT platform that started with the way the company is now much larger than that, has grown into a decision platform, right? We make recommendations for people to make decisions. Mostly that's with Watson and AI, but sometimes it's just with machine learning and more traditional methods. >> So you got some other stuff going on. >> We were talking off camera >> We do. >> about this real-time closed captioning. I was showing you our video clipper tool. You said, "Hey-- >> Yeah! >> "We have something very similar." We're going to maybe talk and see if we can't-- >> Yeah, that'll be great. >> collaborate. I can't wait to try that out. So talk more about what you're doing with real-time closed captioning. It's a mandate, >> That's right. >> for broadcasters and other folks like YouTube. >> That's right. . How are you helping them? >> Yeah, so, as you mention, closed captioning is a regulated space for broadcasters, both local and national. It's a cost center for them, right? They have to do it, and it takes time, people, effort, and energy. We're automating that and we're doing it in a real-time way, so in true real time. So as we're speaking, Watson is listening. It's recording and it's annotating everything that goes on in the video clip. And then it's also breaking it up into essentially a highlight reel, right? And so you can ask questions. Hey, show me the highlights of the US Open or the Masters Golf Tournament. And it'll automatically select the very best clips that came from that tournament based on sentiment analysis, tone of voice, trending key words that were showing in social media, and surface those clips up, typically to a human editor who will then process them. It basically automates a system that today requires human intervention to deliver and makes it completely seamless by being in real-time. >> So Watson will analyze social data, Twitter data, take the fire hose and say, "OK, based on the Olympics," or whatever it was, "this is what was hot." >> Cameron: That's right. >> Curling was off the charts hot. >> (laughs) Curling is always hot in Olympics. >> Hashtag curling. >> Right. >> OK, cool. >> That's right. >> And this is a product that's out on the market today? >> It's a product that's launching here at Think and is being tested by multiple clients right now and is a really great accuracy, quality scores, 95% plus accuracy. But most importantly, it's no human intervention. So no person has to do anything, and it meets all of the regulatory requirements. For digital content creators, which are the fastest growing part of the video ecosystem, people like yourself and others, are also using it to automatically meta tag all their clips. So not only does it do sentiment analysis of the clips and the content itself using the closed captioning, but it's also going out and measuring social media key words and hashtags that are trending and looking for those key words in the closed captioning and clipping that out and surfacing it to make it easier. >> And I consume that as a monthly service kind of thing? >> Exactly, exactly, yep. >> How 'about GDPR? That's hot topic these days. Can you help me with my GDPR problem? 'Cause the clocks ticking on my defines, kicking in. >> Clocks ticking on GDPR. If you haven't started on GDPR yet, you're in some trouble. >> You're way late. >> You're way late, but you better call IBM pretty quickly, and we'll parachute in and try and help. >> How can you help? >> So I think we can help in multiple ways. So one is, obviously, our services group with GBS. We're doing thousands of engagements trying to help people with GDPR. I think, secondly, is we've got a big effort with our consumer weather business to be ready for GDPR. We have 250 million users of our weather app around the world, and they'll have to be compliant here pretty quickly. And so, we've got that all set up, ready to go. And then, these data kits also learn the regulations, right? So you can ask questions of Watson about GDPR and your specific use cases as a customer, and we'll show you how to apply the regulations of GDPR to your business. >> So earlier on, you talked about these data kits. I mean, in my head I was thinking SDK. >> Cameron: Right. So how does that all work? >> Yeah, so you can, you basically on a SAS basis, you essentially rent these data kits, everything from a general knowledge kit to a industry specific kit for financial services, to a sub-industry like wealth management within financial services. And you basically can rent each of those pieces. Within the government category, we have a GDPR capability, along with other regulatory capabilities within the data kits. >> OK, so how does that work? I sort of train my internal system? >> It's super easy. You, basically, go to Bluemix, and you can just use it as a subscription out of Bluemix is the fastest, easiest way to do it. Secondly, you can talk to any of your IBM associates about how you use data kits with Watson. It's always used in conjunction with Watson services themselves, is how you basically deploy our products. >> Let's say I got data all over the place in my organization, it's siloed out, and I'm freaking out because I've got personal data on an individual here and one over her and one over here. What do I do? I point my corpus of data at Watson, and it helps me extract from itities, dedupe, surface? >> The first step in all of our engagements is to listen and understand exactly where all the data is, and everyone's on a journey, right? From on prem to hybrid to some public cloud and everything in between. >> Dave: And they don't know where it all is. >> And they don't know where it all is. And so, step one is for us to go in and listen. We have a rule in our group, two ears and one mouth, use them proportionally. And so we go in and we try to listen, find out, map out sort of a architecture of where our client's data is. And then understand what problem they're really trying to solve because, often times, there's lots of good ideas, but there's only a couple of problems that really matter to that client to solve. Right now, GDPR is certainly one of those problems. But whether it's revenue or efficiency, we can help, but we really need to understand what the problem set is first. And so we have an engineering team that goes in and does sort of architectural work and listens upfront. And then we go into a sort of solutioning mode to solve problems. >> One of the question's we often ask on theCUBE is, how far can we take machine intelligence? How far should we take machine intelligence? What are the things that machines can do that humans can't? How is that changing? How will they complement each other? How will they compete? You must think about that a lot in your role. You're augmenting, sometimes replacing a lot of human tasks. But what are your thoughts on those big picture questions? >> Yes, I think we've, as a company, work really, really hard to make sure that we are always augmenting people wherever possible. We fundamentally believe that every job is going to be changed by AI, but we believe that humans are really good at creativity, at curiosity, and at risk management. We don't really think about us being good at risk management, but from when we're born, just learning to walk is a risk management exercise, right? Look at any toddler wobbling, learning to walk, you sort of realize it's a risk management exercise. AI systems have to learn all these things. And so surfacing and recommending decisions is what we believe Watson and AI is best equipped to do, and then have a person actually make the final call. >> Great. All right, Cameron, hey, thanks very much for coming on theCUBE. >> You're welcome. >> It was really a pleasure meeting you. >> Absolutely, likewise. >> And look forward to the follow up. >> Absolutely, we'll follow up. >> Excited to see that. All right, keep it right there everybody. We'll be back with our next guest right after this short break. You're watching the show theCUBE live from IBM Think 2018. We'll be right back. (electronic music)

>> Announcer: Live from San Jose, it's theCUBE, presenting Big Data, Silicon Valley, brought to you by SiliconANGLE Media and its ecosystem partners. >> Welcome back to theCUBE. Our continuing coverage of our event, Big Data SV, continues, this is our first day. We are down the street from the Strata Data Conference. Come by, we're at this really cool venue, the Forager Tasting Room. We've got a cocktail party tonight. You're going to hear some insights there as well as tomorrow morning. I am Lisa Martin, joined by my co-host, George Gilbert, and we welcome back to theCUBE, for I think the 900 millionth time, the president and CEO of DataTorrent, Guy Churchward. Hey Guy, welcome back! >> Thank you, Lisa, I appreciate it. >> So you're one of our regular VIP's. Give us the update on DataTorrent. What's new, what's going on? >> We actually talked to you a couple of weeks ago. We did a big announcement which was around 3.10, so it's a new release that we have. In all small companies, and we're a small startup, in the big data and analytic space, there is a plethora of features that I can reel through. But it actually makes something a little bit more fundamental. So in the last year... In fact, I think we chatted with you maybe six months ago. We've been looking very carefully at how customers purchase and what they want and how they execute against technology, and it's very very different to what I expected when I came into the company about a year ago off the EMC role that I had. And so, although the features are there, there's a huge amount of underpinning around the experience that a customer would have around big data applications. I'm reminded of, I think it's Gartner that quoted that something like 80% of big data applications fail. And this is one of the things that we really wanted to look at. We have very large customers in production, and we did the analysis of what are we doing well with them, and why can't we do that en masse, and what are people really looking for? So that was really what the release was about. >> Let's elaborate on this a little bit. I want to drill into something where you said many projects, as we've all heard, have not succeeded. There's a huge amount of complexity. The terminology we use is, without tarring and feathering any one particular product, the open source community is kind of like, you're sort of harnessing a couple dozen animals and a zookeeper that works in triplicate... How does DataTorrent tackle that problem? >> Yeah, I mean, in fact I was desperately interested in writing a blog recently about using the word community after open source, because in some respects, there isn't a huge community around the open source movement. What we find is it's the du jour way in which we want to deliver technology, so I have a huge amount of developers that work on a thing called Apache Apex, which is a component in a solution, or in an architecture and in an outcome. And we love what we do, and we do the best we do, and it's better than anybody else's thing. But that's not an application, that's not an outcome. And what happens is, we kind of don't think about what else a customer has to put together, so then they have to go out to the zoo and pick loads of bits and pieces and then try to figure out how to stitch them all together in the best they can. And that takes an inordinately long time. And, in general, people who love this love tinkering with technologies, and their projects never get to production. And large enterprises are used to sitting down and saying, "I need a bulletproof application. "It has to be industrialized. "I need a full SLA on the back of it. "This thing has to have lights out technology. "And I need it quick." Because that was the other thing, as an aspect, is this market is moving so fast, and you look at things like digital economy or any other buzz term, but it really means that if you realize you need to do something, you're probably already too late. And therefore, you need it speedy, expedited. So the idea of being able to wait for 12 months, or two years for an application, also makes no sense. So the arch of this is basically deliver an outcome, don't try and change the way in which open source is currently developed, because they're in components, but embrace them. And so what we did is we sort of looked at it and said, "Well what do people really want to do?" And it's big data analytics, and I want to ingest a lot of information, I want to enrich it, I want to analyze it, and I want to take actions, and then I want to go park it. And so, we looked at it and said, "Okay, so the majority "of stuff we need is what we call a cache stack, "which is KAFKA, Apache Apex, Spark and Hadoop, "and then put complex compute on top." So you would have heard of terms like machine learning, and dimensional compute, so we have their modules. So we actually created an opinionated stack... Because otherwise you have a thousand to choose from and people get confused with choice. I equate it to going into a menu at a restaurant, there's two types of restaurants, you walk into one and you can turn pages and pages and pages and pages of stuff, and you think that's great, I got loads of choice, but the choice kind of confuses you. And also, there's only one chef at the back, and he can't cook everything well. So you know if he chooses the components and puts them together, you're probably not going to get the best meal. And then you go to restaurants that you know are really good, they generally give you one piece of paper and they say, "Here's your three entrees." And you know every single one of them. It's not a lot of choice, but at the end of the day, it's going to be a really good meal. >> So when you go into a customer... You're leading us to ask you the question which is, you're selling the prix fixe tasting menu, and you're putting all the ingredients together. What are some of those solutions and then, sort of, what happens to the platform underneath? >> Yeah, so what you don't want to do is to take these flexible, microdata services, which are open source projects, and hard glue them together to create an application that then has no flexibility. Because, again, one of the myths that I used to assume is applications would last us seven to 10 years. But what we're finding in this space is this movement towards consumerization of enterprise applications. In other words, I need an app and I need it tomorrow because I'm competitively disadvantaged, but it might be wrong, so I then need to adjust it really quick. It's this idea of continual developed, continual adjustment. But that flies in the face of all of this gluing and enterprise-ilities. And I want to base it on open source, and open source, by default, doesn't glue well together. And so what we did is we said okay, not only do you have to create an opinionated stack, and you do that because you want them all to scale into all industries, and they don't need a huge amount of choice, just pick best of breed. But you need to then put a sleeve around them so they all act as though they are a single application. And so we actually announced a thing calls Epoxy. It's a bit of a riff on gluing, but it's called DataTorrent Epoxy. So we have, it's like a microdata service bus, and you can then interchange the components. For instance, right now, Apache Apex is this string-based processing engine in that component. But if there's a better unit, we're quite happy to pull it out, chuck it away, and then put another one in. This isn't a ubiquitous snap-on toolset, because, again, the premise is use open source, get the innovation from there. It has to be bulletproof and enterprise-ility and move really fast. So those are the components I was working on. >> Guy, as CEO, I'm sure you speak with a lot of customers often. What are some of the buying patterns that you're seeing across industries, and what are some of the major business value that DataTorrent can help deliver to your customers? >> The buying patterns when we get involved, and I'm kind of breaking this down into a slightly different way, because we normally get involved when a project's in flight, one of the 80% that's failing, and in general, it's driven by a strategic business partner that has an agenda. And what you see is proprietary application vendors will say, "We can solve everything for you." So they put the tool in and realize it doesn't have the flexibility, it does have enterprise-ility, but it can't adjust fast. And then you get the other type who say, "Well we'll go to a distro or we'll go "to a general purpose practitioner, "and they'll build an application for us." And they'll take open source components, but they'll glue it together with proprietary mush, and then that doesn't then grow past. And then you get the other ones, which is, "Well if I actually am not guided by anybody, "I'll buy a bunch of developers, stick them in my company, "and I've got control on that." But they fiddle around a lot. So we arrive in and, in general, they're in this middle process of saying, "I'm at a competitive disadvantage, "I want to move forward and I want to move forward fast, "and we're working on one of those three channels." The types of outcomes, we just, and back to the expediency of this, we had a telco come to us recently, and it was just before the iPhone X launched, and they wanted to do AB testing on the launch on their platform. We got them up and running within three months. Subsequent from that launch, they then repurposed the platform and some of the components with some augmentation, and they've come out with three further applications. They've all gone into production. So the idea is then these fast cycles of microdata services being stitched together with the Epoxy resin type approach-- >> So faster time to value, lower TCO-- >> Exactly. >> Being able to get to meet their customers' needs faster-- >> Exactly, so it's outcome-based and time to value, and it's time to proof. Because this is, again, the thing that Gartner picked up on, is Hadoop's difficult, this market's complex and people kick the tires a lot. And I sort of joke with customers, "Hey if you want to "obsess about components rather than the outcome, "then your successor will probably come see us "once you're out and your group's failed." And I don't mean that in an obnoxious way. It's not just DataTorrent that solves this same thing, but this it the movement, right? Deal with open source, get enterprise-ilities, get us up and running within a quarter or two, and then let us have some use and agile repurposing. >> Following on that, just to understand going in with a solution to an economic buyer, but then having the platform be reusable, is it opinionated and focused on continuous processing applications, or does it also address both the continuous processing and batch processing? >> Yeah, it's a good answer. In general, and again Gatekeeper, you've got batch and you've got realtime and string, and so we deal with data in motion, which is string-based processing. A string-based processing engine can deal with batch as well, but a batch cannot deal with string. >> George: So you do both-- >> Yeah >> And the idea being that you can have one programming model for both. >> Exactly. >> It's just a window, batch is just a window. >> And the other thing is, a myth bust, is for the last maybe eight plus years, companies assume that the first thing you do in big data analytics is collect all the data, create a data lake, and so they go in there, they ingest the information, they put it into a data lake, and then they poke the data lake posthumously. But the data in the data lake is, by default, already old. So the latency of sticking it into a data lake and then sorting it, and then basically poking it, means that if anybody deals with the data that's in motion, you lose. Because I'm analyzing as it's happening and then you would be analyzing it after at rest, right? So now the architecture of choice is ingest the information, use high performance storage and compute, and then, in essence, ingest, normalize, enrich, analyze, and act on data in motion, in memory. And then when I've used it, then throw it off into a data lake because then I can basically do posthumous analytics and use that for enrichment later. >> You said something also interesting where the DataTorrent customers, the initial successful ones sort of tended to be larger organizations. Those are typically the ones with skillsets to, if anyone's going to be able to put pieces together, it's those guys. Have you not... Well, we always expected big data applications, or sort of adaptive applications, to go mainstream when they were either packaged apps to take all the analysis and embed it, or when you had end to end integrated products to make it simple. Where do you think, what's going to drive this mainstream? >> Yeah, it depends on how mainstream you want mainstream. It's kind of like saying how fast is a fast car. If you want a contractor that comes into IT to create a dashboard, go buy Tableau, and that's mainstream analytics, but it's not. It's mainstream dashboarding of data. The applications that we deal with, by default, the more complex data, they're going to be larger organizations. Don't misunderstand when I say, "We deal with these organizations." We don't have a professional services arm. We work very closely with people like HCL, and we do have a jumpstart team that helps people get there. But our job is teach someone, it's like a kid with a bike and the training wheels, our job is to teach them how to ride the bike, and kick the wheels off, and step away. Because what we don't want to do is to put a professional services drip feed into them and just keep sucking the money out. Our job is to get them there. Now, we've got one company who actually are going to go live next month, and it's a kid tracker, you know like a GPS one that you put on bags and with your kids, and it'll be realtime tracking for the school and also for the individuals. And they had absolutely zero Hadoop experience when we got involved with them. And so we've brought them up, we've helped them with the application, we've kicked the wheels off and now they're going to be sailing. I would say, in a year's time, they're going to be comfortable to just ignore us completely, and in the first year, there's still going to be some handholding and covering up a bruise as they fall off the bike every so often. But that's our job, it's IP, technology, all about outcomes and all about time to value. >> And from a differentiation standpoint, that ability to enable that self service and kick off the training wheels, is that one of the biggest differentiators that you find DataTorret has, versus the Tableau's and the other competitors on the market? >> I don't want to say there's no one doing what we're doing, because that will sound like we're doing something odd. But there's no one doing what we're doing. And it's almost like Tesla. Are they an electric car or are they a platform? They've spurred an industry on, and Uber did the same thing, and Lyft's done something and AirBNB has. And what we've noticed is customer's buying patterns are very specific now. Use open source, get up their enterprise-ilities, and have that level of agility. Nobody else is really doing that. The only people that will do that is your contract with someone like Hortonworks or a Cloudera, and actually pay them a lot of money to build the application for you. And our job is really saying, "No, instead of you paying "them on professional services, we'll give you the sleeve, "we'll make it a little bit more opinionated, "and we'll get you there really quickly, "and then we'll let you and set you free." And so that's one. We have a thing called the Application Factory. That's the snap on toolset where they can literally go to a GUI and say, "I'm in the financial market, "I want a fraud prevention application." And we literally then just self assemble the stack, they can pick it up, and then put their input and output in. And then, as we move forward, we'll have partners who are building the spoke applications in verticals, and they will put them up on our website, so the customers can come in and download them. Everything is subscription software. >> Fantastic, I wish we had more time, but thanks so much for finding some time today to come by theCUBE, tell us what's new, and we look forward to seeing you on the show again very soon. >> I appreciate it, thank you very much. >> We want to thank you for watching theCUBE. Again, Lisa Martin with my co-host George Gilbert, we're live at our event, Big Data SV, in downtown San Jose, down the street from the Strata Data Conference. Stick around, George and I will be back after a short break with our next guest. (light electronic jingle)