(chill electronic music) >> Hello everyone, welcome to theCUBE's presentation of the AWS Startups Showcase, AI and machine learning, the top startups building generative AI on AWS. This is the season three, episode one of the ongoing series covering the exciting startups from the AWS ecosystem, talk about AI and machine learning. Can't believe it's three years and season one. I'm your host, John Furrier. Got a great guest today, we're joined by Joseph Nelson, the co-founder and CEO of Roboflow, doing some cutting edge stuff around computer vision and really at the front end of this massive wave coming around, large language models, computer vision. The next gen AI is here, and it's just getting started. We haven't even scratched a service. Thanks for joining us today. >> Thanks for having me. >> So you got to love the large language model, foundation models, really educating the mainstream world. ChatGPT has got everyone in the frenzy. This is educating the world around this next gen AI capabilities, enterprise, image and video data, all a big part of it. I mean the edge of the network, Mobile World Conference is happening right now, this month, and it's just ending up, it's just continue to explode. Video is huge. So take us through the company, do a quick explanation of what you guys are doing, when you were founded. Talk about what the company's mission is, and what's your North Star, why do you exist? >> Yeah, Roboflow exists to really kind of make the world programmable. I like to say make the world be read and write access. And our North Star is enabling developers, predominantly, to build that future. If you look around, anything that you see will have software related to it, and can kind of be turned into software. The limiting reactant though, is how to enable computers and machines to understand things as well as people can. And in a lot of ways, computer vision is that missing element that enables anything that you see to become software. So in the virtue of, if software is eating the world, computer vision kind of makes the aperture infinitely wide. It's something that I kind of like, the way I like to frame it. And the capabilities are there, the open source models are there, the amount of data is there, the computer capabilities are only improving annually, but there's a pretty big dearth of tooling, and an early but promising sign of the explosion of use cases, models, and data sets that companies, developers, hobbyists alike will need to bring these capabilities to bear. So Roboflow is in the game of building the community around that capability, building the use cases that allow developers and enterprises to use computer vision, and providing the tooling for companies and developers to be able to add computer vision, create better data sets, and deploy to production, quickly, easily, safely, invaluably. >> You know, Joseph, the word in production is actually real now. You're seeing a lot more people doing in production activities. That's a real hot one and usually it's slower, but it's gone faster, and I think that's going to be more the same. And I think the parallel between what we're seeing on the large language models coming into computer vision, and as you mentioned, video's data, right? I mean we're doing video right now, we're transcribing it into a transcript, linking up to your linguistics, times and the timestamp, I mean everything's data and that really kind of feeds. So this connection between what we're seeing, the large language and computer vision are coming together kind of cousins, brothers. I mean, how would you compare, how would you explain to someone, because everyone's like on this wave of watching people bang out their homework assignments, and you know, write some hacks on code with some of the open AI technologies, there is a corollary directly related to to the vision side. Can you explain? >> Yeah, the rise of large language models are showing what's possible, especially with text, and I think increasingly will get multimodal as the images and video become ingested. Though there's kind of this still core missing element of basically like understanding. So the rise of large language models kind of create this new area of generative AI, and generative AI in the context of computer vision is a lot of, you know, creating video and image assets and content. There's also this whole surface area to understanding what's already created. Basically digitizing physical, real world things. I mean the Metaverse can't be built if we don't know how to mirror or create or identify the objects that we want to interact with in our everyday lives. And where computer vision comes to play in, especially what we've seen at Roboflow is, you know, a little over a hundred thousand developers now have built with our tools. That's to the tune of a hundred million labeled open source images, over 10,000 pre-trained models. And they've kind of showcased to us all of the ways that computer vision is impacting and bringing the world to life. And these are things that, you know, even before large language models and generative AI, you had pretty impressive capabilities, and when you add the two together, it actually unlocks these kind of new capabilities. So for example, you know, one of our users actually powers the broadcast feeds at Wimbledon. So here we're talking about video, we're streaming, we're doing things live, we've got folks that are cropping and making sure we look good, and audio/visual all plugged in correctly. When you broadcast Wimbledon, you'll notice that the camera controllers need to do things like track the ball, which is moving at extremely high speeds and zoom crop, pan tilt, as well as determine if the ball bounced in or out. The very controversial but critical key to a lot of tennis matches. And a lot of that has been historically done with the trained, but fallible human eye and computer vision is, you know, well suited for this task to say, how do we track, pan, tilt, zoom, and see, track the tennis ball in real time, run at 30 plus frames per second, and do it all on the edge. And those are capabilities that, you know, were kind of like science fiction, maybe even a decade ago, and certainly five years ago. Now the interesting thing, is that with the advent of of generative AI, you can start to do things like create your own training data sets, or kind of create logic around once you have this visual input. And teams at Tesla have actually been speaking about, of course the autopilot team's focused on doing vision tasks, but they've combined large language models to add reasoning and logic. So given that you see, let's say the tennis ball, what do you want to do? And being able to combine the capabilities of what LLM's represent, which is really a lot of basically, core human reasoning and logic, with computer vision for the inputs of what's possible, creates these new capabilities, let alone multimodality, which I'm sure we'll talk more about. >> Yeah, and it's really, I mean it's almost intoxicating. It's amazing that this is so capable because the cloud scales here, you got the edge developing, you can decouple compute power, and let Moore's law and all the new silicone and the processors and the GPUs do their thing, and you got open source booming. You're kind of getting at this next segment I wanted to get into, which is the, how people should be thinking about these advances of the computer vision. So this is now a next wave, it's here. I mean I'd love to have that for baseball because I'm always like, "Oh, it should have been a strike." I'm sure that's going to be coming soon, but what is the computer vision capable of doing today? I guess that's my first question. You hit some of it, unpack that a little bit. What does general AI mean in computer vision? What's the new thing? Because there are old technology's been around, proprietary, bolted onto hardware, but hardware advances at a different pace, but now you got new capabilities, generative AI for vision, what does that mean? >> Yeah, so computer vision, you know, at its core is basically enabling machines, computers, to understand, process, and act on visual data as effective or more effective than people can. Traditionally this has been, you know, task types like classification, which you know, identifying if a given image belongs in a certain category of goods on maybe a retail site, is the shoes or is it clothing? Or object detection, which is, you know, creating bounding boxes, which allows you to do things like count how many things are present, or maybe measure the speed of something, or trigger an alert when something becomes visible in frame that wasn't previously visible in frame, or instant segmentation where you're creating pixel wise segmentations for both instance and semantic segmentation, where you often see these kind of beautiful visuals of the polygon surrounding objects that you see. Then you have key point detection, which is where you see, you know, athletes, and each of their joints are kind of outlined is another more traditional type problem in signal processing and computer vision. With generative AI, you kind of get a whole new class of problem types that are opened up. So in a lot of ways I think about generative AI in computer vision as some of the, you know, problems that you aimed to tackle, might still be better suited for one of the previous task types we were discussing. Some of those problem types may be better suited for using a generative technique, and some are problem types that just previously wouldn't have been possible absent generative AI. And so if you make that kind of Venn diagram in your head, you can think about, okay, you know, visual question answering is a task type where if I give you an image and I say, you know, "How many people are in this image?" We could either build an object detection model that might count all those people, or maybe a visual question answering system would sufficiently answer this type of problem. Let alone generative AI being able to create new training data for old systems. And that's something that we've seen be an increasingly prominent use case for our users, as much as things that we advise our customers and the community writ large to take advantage of. So ultimately those are kind of the traditional task types. I can give you some insight, maybe, into how I think about what's possible today, or five years or ten years as you sort go back. >> Yes, definitely. Let's get into that vision. >> So I kind of think about the types of use cases in terms of what's possible. If you just imagine a very simple bell curve, your normal distribution, for the longest time, the types of things that are in the center of that bell curve are identifying objects that are very common or common objects in context. Microsoft published the COCO Dataset in 2014 of common objects and contexts, of hundreds of thousands of images of chairs, forks, food, person, these sorts of things. And you know, the challenge of the day had always been, how do you identify just those 80 objects? So if we think about the bell curve, that'd be maybe the like dead center of the curve, where there's a lot of those objects present, and it's a very common thing that needs to be identified. But it's a very, very, very small sliver of the distribution. Now if you go out to the way long tail, let's go like deep into the tail of this imagined visual normal distribution, you're going to have a problem like one of our customers, Rivian, in tandem with AWS, is tackling, to do visual quality assurance and manufacturing in production processes. Now only Rivian knows what a Rivian is supposed to look like. Only they know the imagery of what their goods that are going to be produced are. And then between those long tails of proprietary data of highly specific things that need to be understood, in the center of the curve, you have a whole kind of messy middle, type of problems I like to say. The way I think about computer vision advancing, is it's basically you have larger and larger and more capable models that eat from the center out, right? So if you have a model that, you know, understands the 80 classes in COCO, well, pretty soon you have advances like Clip, which was trained on 400 million image text pairs, and has a greater understanding of a wider array of objects than just 80 classes in context. And over time you'll get more and more of these larger models that kind of eat outwards from that center of the distribution. And so the question becomes for companies, when can you rely on maybe a model that just already exists? How do you use your data to get what may be capable off the shelf, so to speak, into something that is usable for you? Or, if you're in those long tails and you have proprietary data, how do you take advantage of the greatest asset you have, which is observed visual information that you want to put to work for your customers, and you're kind of living in the long tails, and you need to adapt state of the art for your capabilities. So my mental model for like how computer vision advances is you have that bell curve, and you have increasingly powerful models that eat outward. And multimodality has a role to play in that, larger models have a role to play in that, more compute, more data generally has a role to play in that. But it will be a messy and I think long condition. >> Well, the thing I want to get, first of all, it's great, great mental model, I appreciate that, 'cause I think that makes a lot of sense. The question is, it seems now more than ever, with the scale and compute that's available, that not only can you eat out to the middle in your example, but there's other models you can integrate with. In the past there was siloed, static, almost bespoke. Now you're looking at larger models eating into the bell curve, as you said, but also integrating in with other stuff. So this seems to be part of that interaction. How does, first of all, is that really happening? Is that true? And then two, what does that mean for companies who want to take advantage of this? Because the old model was operational, you know? I have my cameras, they're watching stuff, whatever, and like now you're in this more of a, distributed computing, computer science mindset, not, you know, put the camera on the wall kind of- I'm oversimplifying, but you know what I'm saying. What's your take on that? >> Well, to the first point of, how are these advances happening? What I was kind of describing was, you know, almost uni-dimensional in that you have like, you're only thinking about vision, but the rise of generative techniques and multi-modality, like Clip is a multi-modal model, it has 400 million image text pairs. That will advance the generalizability at a faster rate than just treating everything as only vision. And that's kind of where LLMs and vision will intersect in a really nice and powerful way. Now in terms of like companies, how should they be thinking about taking advantage of these trends? The biggest thing that, and I think it's different, obviously, on the size of business, if you're an enterprise versus a startup. The biggest thing that I think if you're an enterprise, and you have an established scaled business model that is working for your customers, the question becomes, how do you take advantage of that established data moat, potentially, resource moats, and certainly, of course, establish a way of providing value to an end user. So for example, one of our customers, Walmart, has the advantage of one of the largest inventory and stock of any company in the world. And they also of course have substantial visual data, both from like their online catalogs, or understanding what's in stock or out of stock, or understanding, you know, the quality of things that they're going from the start of their supply chain to making it inside stores, for delivery of fulfillments. All these are are visual challenges. Now they already have a substantial trove of useful imagery to understand and teach and train large models to understand each of the individual SKUs and products that are in their stores. And so if I'm a Walmart, what I'm thinking is, how do I make sure that my petabytes of visual information is utilized in a way where I capture the proprietary benefit of the models that I can train to do tasks like, what item was this? Or maybe I'm going to create AmazonGo-like technology, or maybe I'm going to build like delivery robots, or I want to automatically know what's in and out of stock from visual input fees that I have across my in-store traffic. And that becomes the question and flavor of the day for enterprises. I've got this large amount of data, I've got an established way that I can provide more value to my own customers. How do I ensure I take advantage of the data advantage I'm already sitting on? If you're a startup, I think it's a pretty different question, and I'm happy to talk about. >> Yeah, what's startup angle on this? Because you know, they're going to want to take advantage. It's like cloud startups, cloud native startups, they were born in the cloud, they never had an IT department. So if you're a startup, is there a similar role here? And if I'm a computer vision startup, what's that mean? So can you share your your take on that, because there'll be a lot of people starting up from this. >> So the startup on the opposite advantage and disadvantage, right? Like a startup doesn't have an proven way of delivering repeatable value in the same way that a scaled enterprise does. But it does have the nimbleness to identify and take advantage of techniques that you can start from a blank slate. And I think the thing that startups need to be wary of in the generative AI enlarged language model, in multimodal world, is building what I like to call, kind of like sandcastles. A sandcastle is maybe a business model or a capability that's built on top of an assumption that is going to be pretty quickly wiped away by improving underlying model technology. So almost like if you imagine like the ocean, the waves are coming in, and they're going to wipe away your progress. You don't want to be in the position of building sandcastle business where, you don't want to bet on the fact that models aren't going to get good enough to solve the task type that you might be solving. In other words, don't take a screenshot of what's capable today. Assume that what's capable today is only going to continue to become possible. And so for a startup, what you can do, that like enterprises are quite comparatively less good at, is embedding these capabilities deeply within your products and delivering maybe a vertical based experience, where AI kind of exists in the background. >> Yeah. >> And we might not think of companies as, you know, even AI companies, it's just so embedded in the experience they provide, but that's like the vertical application example of taking AI and making it be immediately usable. Or, of course there's tons of picks and shovels businesses to be built like Roboflow, where you're enabling these enterprises to take advantage of something that they have, whether that's their data sets, their computes, or their intellect. >> Okay, so if I hear that right, by the way, I love, that's horizontally scalable, that's the large language models, go up and build them the apps, hence your developer focus. I'm sure that's probably the reason that the tsunami of developer's action. So you're saying picks and shovels tools, don't try to replicate the platform of what could be the platform. Oh, go to a VC, I'm going to build a platform. No, no, no, no, those are going to get wiped away by the large language models. Is there one large language model that will rule the world, or do you see many coming? >> Yeah, so to be clear, I think there will be useful platforms. I just think a lot of people think that they're building, let's say, you know, if we put this in the cloud context, you're building a specific type of EC2 instance. Well, it turns out that Amazon can offer that type of EC2 instance, and immediately distribute it to all of their customers. So you don't want to be in the position of just providing something that actually ends up looking like a feature, which in the context of AI, might be like a small incremental improvement on the model. If that's all you're doing, you're a sandcastle business. Now there's a lot of platform businesses that need to be built that enable businesses to get to value and do things like, how do I monitor my models? How do I create better models with my given data sets? How do I ensure that my models are doing what I want them to do? How do I find the right models to use? There's all these sorts of platform wide problems that certainly exist for businesses. I just think a lot of startups that I'm seeing right now are making the mistake of assuming the advances we're seeing are not going to accelerate or even get better. >> So if I'm a customer, if I'm a company, say I'm a startup or an enterprise, either one, same question. And I want to stand up, and I have developers working on stuff, I want to start standing up an environment to start doing stuff. Is that a service provider? Is that a managed service? Is that you guys? So how do you guys fit into your customers leaning in? Is it just for developers? Are you targeting with a specific like managed service? What's the product consumption? How do you talk to customers when they come to you? >> The thing that we do is enable, we give developers superpowers to build automated inventory tracking, self-checkout systems, identify if this image is malignant cancer or benign cancer, ensure that these products that I've produced are correct. Make sure that that the defect that might exist on this electric vehicle makes its way back for review. All these sorts of problems are immediately able to be solved and tackled. In terms of the managed services element, we have solutions as integrators that will often build on top of our tools, or we'll have companies that look to us for guidance, but ultimately the company is in control of developing and building and creating these capabilities in house. I really think the distinction is maybe less around managed service and tool, and more around ownership in the era of AI. So for example, if I'm using a managed service, in that managed service, part of their benefit is that they are learning across their customer sets, then it's a very different relationship than using a managed service where I'm developing some amount of proprietary advantages for my data sets. And I think that's a really important thing that companies are becoming attuned to, just the value of the data that they have. And so that's what we do. We tell companies that you have this proprietary, immense treasure trove of data, use that to your advantage, and think about us more like a set of tools that enable you to get value from that capability. You know, the HashiCorp's and GitLab's of the world have proven like what these businesses look like at scale. >> And you're targeting developers. When you go into a company, do you target developers with freemium, is there a paid service? Talk about the business model real quick. >> Sure, yeah. The tools are free to use and get started. When someone signs up for Roboflow, they may elect to make their work open source, in which case we're able to provide even more generous usage limits to basically move the computer vision community forward. If you elect to make your data private, you can use our hosted data set managing, data set training, model deployment, annotation tooling up to some limits. And then usually when someone validates that what they're doing gets them value, they purchase a subscription license to be able to scale up those capabilities. So like most developer centric products, it's free to get started, free to prove, free to poke around, develop what you think is possible. And then once you're getting to value, then we're able to capture the commercial upside in the value that's being provided. >> Love the business model. It's right in line with where the market is. There's kind of no standards bodies these days. The developers are the ones who are deciding kind of what the standards are by their adoption. I think making that easy for developers to get value as the model open sources continuing to grow, you can see more of that. Great perspective Joseph, thanks for sharing that. Put a plug in for the company. What are you guys doing right now? Where are you in your growth? What are you looking for? How should people engage? Give the quick commercial for the company. >> So as I mentioned, Roboflow is I think one of the largest, if not the largest collections of computer vision models and data sets that are open source, available on the web today, and have a private set of tools that over half the Fortune 100 now rely on those tools. So we're at the stage now where we know people want what we're working on, and we're continuing to drive that type of adoption. So companies that are looking to make better models, improve their data sets, train and deploy, often will get a lot of value from our tools, and certainly reach out to talk. I'm sure there's a lot of talented engineers that are tuning in too, we're aggressively hiring. So if you are interested in being a part of making the world programmable, and being at the ground floor of the company that's creating these capabilities to be writ large, we'd love to hear from you. >> Amazing, Joseph, thanks so much for coming on and being part of the AWS Startup Showcase. Man, if I was in my twenties, I'd be knocking on your door, because it's the hottest trend right now, it's super exciting. Generative AI is just the beginning of massive sea change. Congratulations on all your success, and we'll be following you guys. Thanks for spending the time, really appreciate it. >> Thanks for having me. >> Okay, this is season three, episode one of the ongoing series covering the exciting startups from the AWS ecosystem, talking about the hottest things in tech. I'm John Furrier, your host. Thanks for watching. (chill electronic music)

(gentle music) >> Hello everyone. Welcome to this CUBE conversation here in Palo Alto, California. I'm John Furrier, host of theCUBE. We got a great remote guest coming in. Joseph Nelson, co-founder and CEO of RoboFlow hot startup in AI, computer vision. Really interesting topic in this wave of AI next gen hitting. Joseph, thanks for coming on this CUBE conversation. >> Thanks for having me. >> Yeah, I love the startup tsunami that's happening here in this wave. RoboFlow, you're in the middle of it. Exciting opportunities, you guys are in the cutting edge. I think computer vision's been talked about more as just as much as the large language models and these foundational models are merging. You're in the middle of it. What's it like right now as a startup and growing in this new wave hitting? >> It's kind of funny, it's, you know, I kind of describe it like sometimes you're in a garden of gnomes. It's like we feel like we've got this giant headstart with hundreds of thousands of people building with computer vision, training their own models, but that's a fraction of what it's going to be in six months, 12 months, 24 months. So, as you described it, a wave is a good way to think about it. And the wave is still building before it gets to its full size. So it's a ton of fun. >> Yeah, I think it's one of the most exciting areas in computer science. I wish I was in my twenties again, because I would be all over this. It's the intersection, there's so many disciplines, right? It's not just tech computer science, it's computer science, it's systems, it's software, it's data. There's so much aperture of things going on around your world. So, I mean, you got to be batting all the students away kind of trying to get hired in there, probably. I can only imagine you're hiring regiment. I'll ask that later, but first talk about what the company is that you're doing. How it's positioned, what's the market you're going after, and what's the origination story? How did you guys get here? How did you just say, hey, want to do this? What was the origination story? What do you do and how did you start the company? >> Yeah, yeah. I'll give you the what we do today and then I'll shift into the origin. RoboFlow builds tools for making the world programmable. Like anything that you see should be read write access if you think about it with a programmer's mind or legible. And computer vision is a technology that enables software to be added to these real world objects that we see. And so any sort of interface, any sort of object, any sort of scene, we can interact with it, we can make it more efficient, we can make it more entertaining by adding the ability for the tools that we use and the software that we write to understand those objects. And at RoboFlow, we've empowered a little over a hundred thousand developers, including those in half the Fortune 100 so far in that mission. Whether that's Walmart understanding the retail in their stores, Cardinal Health understanding the ways that they're helping their patients, or even electric vehicle manufacturers ensuring that they're making the right stuff at the right time. As you mentioned, it's early. Like I think maybe computer vision has touched one, maybe 2% of the whole economy and it'll be like everything in a very short period of time. And so we're focused on enabling that transformation. I think it's it, as far as I think about it, I've been fortunate to start companies before, start, sell these sorts of things. This is the last company I ever wanted to start and I think it will be, should we do it right, the world's largest in riding the wave of bringing together the disparate pieces of that technology. >> What was the motivating point of the formation? Was it, you know, you guys were hanging around? Was there some catalyst? What was the moment where it all kind of came together for you? >> You know what's funny is my co-founder, Brad and I, we were making computer vision apps for making board games more fun to play. So in 2017, Apple released AR kit, augmented reality kit for building augmented reality applications. And Brad and I are both sort of like hacker persona types. We feel like we don't really understand the technology until we build something with it and so we decided that we should make an app that if you point your phone at a Sudoku puzzle, it understands the state of the board and then it kind of magically fills in that experience with all the digits in real time, which totally ruins the game of Sudoku to be clear. But it also just creates this like aha moment of like, oh wow, like the ability for our pocket devices to understand and see the world as good or better than we can is possible. And so, you know, we actually did that as I mentioned in 2017, and the app went viral. It was, you know, top of some subreddits, top of Injure, Reddit, the hacker community as well as Product Hunt really liked it. So it actually won Product Hunt AR app of the year, which was the same year that the Tesla model three won the product of the year. So we joked that we share an award with Elon our shared (indistinct) But frankly, so that was 2017. RoboFlow wasn't incorporated as a business until 2019. And so, you know, when we made Magic Sudoku, I was running a different company at the time, Brad was running a different company at the time, and we kind of just put it out there and were excited by how many people liked it. And we assumed that other curious developers would see this inevitable future of, oh wow, you know. This is much more than just a pedestrian point your phone at a board game. This is everything can be seen and understood and rewritten in a different way. Things like, you know, maybe your fridge. Knowing what ingredients you have and suggesting recipes or auto ordering for you, or we were talking about some retail use cases of automated checkout. Like anything can be seen and observed and we presume that that would kick off a Cambrian explosion of applications. It didn't. So you fast forward to 2019, we said, well we might as well be the guys to start to tackle this sort of problem. And because of our success with board games before, we returned to making more board game solving applications. So we made one that solves Boggle, you know, the four by four word game, we made one that solves chess, you point your phone at a chess board and it understands the state of the board and then can make move recommendations. And each additional board game that we added, we realized that the tooling was really immature. The process of collecting images, knowing which images are actually going to be useful for improving model performance, training those models, deploying those models. And if we really wanted to make the world programmable, developers waiting for us to make an app for their thing of interest is a lot less efficient, less impactful than taking our tool chain and releasing that externally. And so, that's what RoboFlow became. RoboFlow became the internal tools that we used to make these game changing applications readily available. And as you know, when you give developers new tools, they create new billion dollar industries, let alone all sorts of fun hobbyist projects along the way. >> I love that story. Curious, inventive, little radical. Let's break the rules, see how we can push the envelope on the board games. That's how companies get started. It's a great story. I got to ask you, okay, what happens next? Now, okay, you realize this new tooling, but this is like how companies get built. Like they solve their own problem that they had 'cause they realized there's one, but then there has to be a market for it. So you actually guys knew that this was coming around the corner. So okay, you got your hacker mentality, you did that thing, you got the award and now you're like, okay, wow. Were you guys conscious of the wave coming? Was it one of those things where you said, look, if we do this, we solve our own problem, this will be big for everybody. Did you have that moment? Was that in 2019 or was that more of like, it kind of was obvious to you guys? >> Absolutely. I mean Brad puts this pretty effectively where he describes how we lived through the initial internet revolution, but we were kind of too young to really recognize and comprehend what was happening at the time. And then mobile happened and we were working on different companies that were not in the mobile space. And computer vision feels like the wave that we've caught. Like, this is a technology and capability that rewrites how we interact with the world, how everyone will interact with the world. And so we feel we've been kind of lucky this time, right place, right time of every enterprise will have the ability to improve their operations with computer vision. And so we've been very cognizant of the fact that computer vision is one of those groundbreaking technologies that every company will have as a part of their products and services and offerings, and we can provide the tooling to accelerate that future. >> Yeah, and the developer angle, by the way, I love that because I think, you know, as we've been saying in theCUBE all the time, developer's the new defacto standard bodies because what they adopt is pure, you know, meritocracy. And they pick the best. If it's sell service and it's good and it's got open source community around it, its all in. And they'll vote. They'll vote with their code and that is clear. Now I got to ask you, as you look at the market, we were just having this conversation on theCUBE in Barcelona at recent Mobile World Congress, now called MWC, around 5G versus wifi. And the debate was specifically computer vision, like facial recognition. We were talking about how the Cleveland Browns were using facial recognition for people coming into the stadium they were using it for ships in international ports. So the question was 5G versus wifi. My question is what infrastructure or what are the areas that need to be in place to make computer vision work? If you have developers building apps, apps got to run on stuff. So how do you sort that out in your mind? What's your reaction to that? >> A lot of the times when we see applications that need to run in real time and on video, they'll actually run at the edge without internet. And so a lot of our users will actually take their models and run it in a fully offline environment. Now to act on that information, you'll often need to have internet signal at some point 'cause you'll need to know how many people were in the stadium or what shipping crates are in my port at this point in time. You'll need to relay that information somewhere else, which will require connectivity. But actually using the model and creating the insights at the edge does not require internet. I mean we have users that deploy models on underwater submarines just as much as in outer space actually. And those are not very friendly environments to internet, let alone 5g. And so what you do is you use an edge device, like an Nvidia Jetson is common, mobile devices are common. Intel has some strong edge devices, the Movidius family of chips for example. And you use that compute that runs completely offline in real time to process those signals. Now again, what you do with those signals may require connectivity and that becomes a question of the problem you're solving of how soon you need to relay that information to another place. >> So, that's an architectural issue on the infrastructure. If you're a tactical edge war fighter for instance, you might want to have highly available and maybe high availability. I mean, these are words that mean something. You got storage, but it's not at the edge in real time. But you can trickle it back and pull it down. That's management. So that's more of a business by business decision or environment, right? >> That's right, that's right. Yeah. So I mean we can talk through some specifics. So for example, the RoboFlow actually powers the broadcaster that does the tennis ball tracking at Wimbledon. That runs completely at the edge in real time in, you know, technically to track the tennis ball and point the camera, you actually don't need internet. Now they do have internet of course to do the broadcasting and relay the signal and feeds and these sorts of things. And so that's a case where you have both edge deployment of running the model and high availability act on that model. We have other instances where customers will run their models on drones and the drone will go and do a flight and it'll say, you know, this many residential homes are in this given area, or this many cargo containers are in this given shipping yard. Or maybe we saw these environmental considerations of soil erosion along this riverbank. The model in that case can run on the drone during flight without internet, but then you only need internet once the drone lands and you're going to act on that information because for example, if you're doing like a study of soil erosion, you don't need to be real time. You just need to be able to process and make use of that information once the drone finishes its flight. >> Well I can imagine a zillion use cases. I heard of a use case interview at a company that does computer vision to help people see if anyone's jumping the fence on their company. Like, they know what a body looks like climbing a fence and they can spot it. Pretty easy use case compared to probably some of the other things, but this is the horizontal use cases, its so many use cases. So how do you guys talk to the marketplace when you say, hey, we have generative AI for commuter vision. You might know language models that's completely different animal because vision's like the world, right? So you got a lot more to do. What's the difference? How do you explain that to customers? What can I build and what's their reaction? >> Because we're such a developer centric company, developers are usually creative and show you the ways that they want to take advantage of new technologies. I mean, we've had people use things for identifying conveyor belt debris, doing gas leak detection, measuring the size of fish, airplane maintenance. We even had someone that like a hobby use case where they did like a specific sushi identifier. I dunno if you know this, but there's a specific type of whitefish that if you grew up in the western hemisphere and you eat it in the eastern hemisphere, you get very sick. And so there was someone that made an app that tells you if you happen to have that fish in the sushi that you're eating. But security camera analysis, transportation flows, plant disease detection, really, you know, smarter cities. We have people that are doing curb management identifying, and a lot of these use cases, the fantastic thing about building tools for developers is they're a creative bunch and they have these ideas that if you and I sat down for 15 minutes and said, let's guess every way computer vision can be used, we would need weeks to list all the example use cases. >> We'd miss everything. >> And we'd miss. And so having the community show us the ways that they're using computer vision is impactful. Now that said, there are of course commercial industries that have discovered the value and been able to be out of the gate. And that's where we have the Fortune 100 customers, like we do. Like the retail customers in the Walmart sector, healthcare providers like Medtronic, or vehicle manufacturers like Rivian who all have very difficult either supply chain, quality assurance, in stock, out of stock, anti-theft protection considerations that require successfully making sense of the real world. >> Let me ask you a question. This is maybe a little bit in the weeds, but it's more developer focused. What are some of the developer profiles that you're seeing right now in terms of low-hanging fruit applications? And can you talk about the academic impact? Because I imagine if I was in school right now, I'd be all over it. Are you seeing Master's thesis' being worked on with some of your stuff? Is the uptake in both areas of younger pre-graduates? And then inside the workforce, What are some of the devs like? Can you share just either what their makeup is, what they work on, give a little insight into the devs you're working with. >> Leading developers that want to be on state-of-the-art technology build with RoboFlow because they know they can use the best in class open source. They know that they can get the most out of their data. They know that they can deploy extremely quickly. That's true among students as you mentioned, just as much as as industries. So we welcome students and I mean, we have research grants that will regularly support for people to publish. I mean we actually have a channel inside our internal slack where every day, more student publications that cite building with RoboFlow pop up. And so, that helps inspire some of the use cases. Now what's interesting is that the use case is relatively, you know, useful or applicable for the business or the student. In other words, if a student does a thesis on how to do, we'll say like shingle damage detection from satellite imagery and they're just doing that as a master's thesis, in fact most insurance businesses would be interested in that sort of application. So, that's kind of how we see uptick and adoption both among researchers who want to be on the cutting edge and publish, both with RoboFlow and making use of open source tools in tandem with the tool that we provide, just as much as industry. And you know, I'm a big believer in the philosophy that kind of like what the hackers are doing nights and weekends, the Fortune 500 are doing in a pretty short order period of time and we're experiencing that transition. Computer vision used to be, you know, kind of like a PhD, multi-year investment endeavor. And now with some of the tooling that we're working on in open source technologies and the compute that's available, these science fiction ideas are possible in an afternoon. And so you have this idea of maybe doing asset management or the aerial observation of your shingles or things like this. You have a few hundred images and you can de-risk whether that's possible for your business today. So there's pretty broad-based adoption among both researchers that want to be on the state of the art, as much as companies that want to reduce the time to value. >> You know, Joseph, you guys and your partner have got a great front row seat, ground floor, presented creation wave here. I'm seeing a pattern emerging from all my conversations on theCUBE with founders that are successful, like yourselves, that there's two kind of real things going on. You got the enterprises grabbing the products and retrofitting into their legacy and rebuilding their business. And then you have startups coming out of the woodwork. Young, seeing greenfield or pick a specific niche or focus and making that the signature lever to move the market. >> That's right. >> So can you share your thoughts on the startup scene, other founders out there and talk about that? And then I have a couple questions for like the enterprises, the old school, the existing legacy. Little slower, but the startups are moving fast. What are some of the things you're seeing as startups are emerging in this field? >> I think you make a great point that independent of RoboFlow, very successful, especially developer focused businesses, kind of have three customer types. You have the startups and maybe like series A, series B startups that you're building a product as fast as you can to keep up with them, and they're really moving just as fast as as you are and pulling the product out at you for things that they need. The second segment that you have might be, call it SMB but not enterprise, who are able to purchase and aren't, you know, as fast of moving, but are stable and getting value and able to get to production. And then the third type is enterprise, and that's where you have typically larger contract value sizes, slower moving in terms of adoption and feedback for your product. And I think what you see is that successful companies balance having those three customer personas because you have the small startups, small fast moving upstarts that are discerning buyers who know the market and elect to build on tooling that is best in class. And so you basically kind of pass the smell test of companies who are quite discerning in their purchases, plus are moving so quick they're pulling their product out of you. Concurrently, you have a product that's enterprise ready to service the scalability, availability, and trust of enterprise buyers. And that's ultimately where a lot of companies will see tremendous commercial success. I mean I remember seeing the Twilio IPO, Uber being like a full 20% of their revenue, right? And so there's this very common pattern where you have the ability to find some of those upstarts that you make bets on, like the next Ubers of the world, the smaller companies that continue to get developed with the product and then the enterprise whom allows you to really fund the commercial success of the business, and validate the size of the opportunity in market that's being creative. >> It's interesting, there's so many things happening there. It's like, in a way it's a new category, but it's not a new category. It becomes a new category because of the capabilities, right? So, it's really interesting, 'cause that's what you're talking about is a category, creating. >> I think developer tools. So people often talk about B to B and B to C businesses. I think developer tools are in some ways a third way. I mean ultimately they're B to B, you're selling to other businesses and that's where your revenue's coming from. However, you look kind of like a B to C company in the ways that you measure product adoption and kind of go to market. In other words, you know, we're often tracking the leading indicators of commercial success in the form of usage, adoption, retention. Really consumer app, traditionally based metrics of how to know you're building the right stuff, and that's what product led growth companies do. And then you ultimately have commercial traction in a B to B way. And I think that that actually kind of looks like a third thing, right? Like you can do these sort of funny zany marketing examples that you might see historically from consumer businesses, but yet you ultimately make your money from the enterprise who has these de-risked high value problems you can solve for them. And I selfishly think that that's the best of both worlds because I don't have to be like Evan Spiegel, guessing the next consumer trend or maybe creating the next consumer trend and catching lightning in a bottle over and over again on the consumer side. But I still get to have fun in our marketing and make sort of fun, like we're launching the world's largest game of rock paper scissors being played with computer vision, right? Like that's sort of like a fun thing you can do, but then you can concurrently have the commercial validation and customers telling you the things that they need to be built for them next to solve commercial pain points for them. So I really do think that you're right by calling this a new category and it really is the best of both worlds. >> It's a great call out, it's a great call out. In fact, I always juggle with the VC. I'm like, it's so easy. Your job is so easy to pick the winners. What are you talking about its so easy? I go, just watch what the developers jump on. And it's not about who started, it could be someone in the dorm room to the boardroom person. You don't know because that B to C, the C, it's B to D you know? You know it's developer 'cause that's a human right? That's a consumer of the tool which influences the business that never was there before. So I think this direct business model evolution, whether it's media going direct or going direct to the developers rather than going to a gatekeeper, this is the reality. >> That's right. >> Well I got to ask you while we got some time left to describe, I want to get into this topic of multi-modality, okay? And can you describe what that means in computer vision? And what's the state of the growth of that portion of this piece? >> Multi modality refers to using multiple traditionally siloed problem types, meaning text, image, video, audio. So you could treat an audio problem as only processing audio signal. That is not multimodal, but you could use the audio signal at the same time as a video feed. Now you're talking about multi modality. In computer vision, multi modality is predominantly happening with images and text. And one of the biggest releases in this space is actually two years old now, was clip, contrastive language image pre-training, which took 400 million image text pairs and basically instead of previously when you do classification, you basically map every single image to a single class, right? Like here's a bunch of images of chairs, here's a bunch of images of dogs. What clip did is used, you can think about it like, the class for an image being the Instagram caption for the image. So it's not one single thing. And by training on understanding the corpora, you basically see which words, which concepts are associated with which pixels. And this opens up the aperture for the types of problems and generalizability of models. So what does this mean? This means that you can get to value more quickly from an existing trained model, or at least validate that what you want to tackle with a computer vision, you can get there more quickly. It also opens up the, I mean. Clip has been the bedrock of some of the generative image techniques that have come to bear, just as much as some of the LLMs. And increasingly we're going to see more and more of multi modality being a theme simply because at its core, you're including more context into what you're trying to understand about the world. I mean, in its most basic sense, you could ask yourself, if I have an image, can I know more about that image with just the pixels? Or if I have the image and the sound of when that image was captured or it had someone describe what they see in that image when the image was captured, which one's going to be able to get you more signal? And so multi modality helps expand the ability for us to understand signal processing. >> Awesome. And can you just real quick, define clip for the folks that don't know what that means? >> Yeah. Clip is a model architecture, it's an acronym for contrastive language image pre-training and like, you know, model architectures that have come before it captures the almost like, models are kind of like brands. So I guess it's a brand of a model where you've done these 400 million image text pairs to match up which visual concepts are associated with which text concepts. And there have been new releases of clip, just at bigger sizes of bigger encoding's, of longer strings of texture, or larger image windows. But it's been a really exciting advancement that OpenAI released in January, 2021. >> All right, well great stuff. We got a couple minutes left. Just I want to get into more of a company-specific question around culture. All startups have, you know, some sort of cultural vibe. You know, Intel has Moore's law doubles every whatever, six months. What's your culture like at RoboFlow? I mean, if you had to describe that culture, obviously love the hacking story, you and your partner with the games going number one on Product Hunt next to Elon and Tesla and then hey, we should start a company two years later. That's kind of like a curious, inventing, building, hard charging, but laid back. That's my take. How would you describe the culture? >> I think that you're right. The culture that we have is one of shipping, making things. So every week each team shares what they did for our customers on a weekly basis. And we have such a strong emphasis on being better week over week that those sorts of things compound. So one big emphasis in our culture is getting things done, shipping, doing things for our customers. The second is we're an incredibly transparent place to work. For example, how we think about giving decisions, where we're progressing against our goals, what problems are biggest and most important for the company is all open information for those that are inside the company to know and progress against. The third thing that I'd use to describe our culture is one that thrives with autonomy. So RoboFlow has a number of individuals who have founded companies before, some of which have sold their businesses for a hundred million plus upon exit. And the way that we've been able to attract talent like that is because the problems that we're tackling are so immense, yet individuals are able to charge at it with the way that they think is best. And this is what pairs well with transparency. If you have a strong sense of what the company's goals are, how we're progressing against it, and you have this ownership mentality of what can I do to change or drive progress against that given outcome, then you create a really healthy pairing of, okay cool, here's where the company's progressing. Here's where things are going really well, here's the places that we most need to improve and work on. And if you're inside that company as someone who has a preponderance to be a self-starter and even a history of building entire functions or companies yourself, then you're going to be a place where you can really thrive. You have the inputs of the things where we need to work on to progress the company's goals. And you have the background of someone that is just necessarily a fast moving and ambitious type of individual. So I think the best way to describe it is a transparent place with autonomy and an emphasis on getting things done. >> Getting shit done as they say. Getting stuff done. Great stuff. Hey, final question. Put a plug out there for the company. What are you going to hire? What's your pipeline look like for people? What jobs are open? I'm sure you got hiring all around. Give a quick plug for the company what you're looking for. >> I appreciate you asking. Basically you're either building the product or helping customers be successful with the product. So in the building product category, we have platform engineering roles, machine learning engineering roles, and we're solving some of the hardest and most impactful problems of bringing such a groundbreaking technology to the masses. And so it's a great place to be where you can kind of be your own user as an engineer. And then if you're enabling people to be successful with the products, I mean you're working in a place where there's already such a strong community around it and you can help shape, foster, cultivate, activate, and drive commercial success in that community. So those are roles that tend themselves to being those that build the product for developer advocacy, those that are account executives that are enabling our customers to realize commercial success, and even hybrid roles like we call it field engineering, where you are a technical resource to drive success within customer accounts. And so all this is listed on roboflow.com/careers. And one thing that I actually kind of want to mention John that's kind of novel about the thing that's working at RoboFlow. So there's been a lot of discussion around remote companies and there's been a lot of discussion around in-person companies and do you need to be in the office? And one thing that we've kind of recognized is you can actually chart a third way. You can create a third way which we call satellite, which basically means people can work from where they most like to work and there's clusters of people, regular onsite's. And at RoboFlow everyone gets, for example, $2,500 a year that they can use to spend on visiting coworkers. And so what's sort of organically happened is team numbers have started to pull together these resources and rent out like, lavish Airbnbs for like a week and then everyone kind of like descends in and works together for a week and makes and creates things. And we call this lighthouses because you know, a lighthouse kind of brings ships into harbor and we have an emphasis on shipping. >> Yeah, quality people that are creative and doers and builders. You give 'em some cash and let the self-governing begin, you know? And like, creativity goes through the roof. It's a great story. I think that sums up the culture right there, Joseph. Thanks for sharing that and thanks for this great conversation. I really appreciate it and it's very inspiring. Thanks for coming on. >> Yeah, thanks for having me, John. >> Joseph Nelson, co-founder and CEO of RoboFlow. Hot company, great culture in the right place in a hot area, computer vision. This is going to explode in value. The edge is exploding. More use cases, more development, and developers are driving the change. Check out RoboFlow. This is theCUBE. I'm John Furrier, your host. Thanks for watching. (gentle music)

>>Hey everyone. Welcome back to Chicago. Lisa Martin here with John Furrier. We're live with the Cube at Ansible Fest 2022. This is not only Ansible's 10th anniversary, John Wood. It's the first in-person event in three years. About 14 to 1500 people here talking about the evolution of automation, really the democratization opportunities. Ansible >>Is money, and this segment's gonna be great. Cub alumni are back, and we're gonna get an industry perspective on the automation journey. So it should be great. >>It will be great. We've got two alumni back for the price of wine. Scott Canine joins us, Director of Worldwide Automation at Kendra. A Nelson Shoe is back as well. Product marketing director at Red Hat. Guys, great to have you back on the, on the live cube. >>Oh, thank you for having us. And, and you know, it's really great to be back here live and in person and, and, you know, get a chance to see you guys again. >>Well, and also you get, you get such a sense of the actual Ansible community here. Yeah. And, and only a fraction of them that are here, but people are ready to be back. They're ready to collaborate in person. And I always can imagine the amount of innovation that happens at these events, just like off the show floor, people bumping into each other and go, Hey, I had this idea. What do you think, Scott? It's been just about a, a year since Kenel was formed. Talk to us about the last close to a year and what that's been like. Especially as the world has been so, chops >>The world been Yeah, exactly. Topsy turvy. People getting back to working in person and, and everything else. But, you know, you know, throw on that what we've done in the last year, taking Kendra, you know, outside of being a part of ibm Right. In our own company at this point, you know, and you know, you hear a lot of our executives and a lot of our people when we talk about it, like, Oh yeah, it's, you know, it's a $19 billion startup. We got freedom of action. We can do all these different things. But, you know, one of the ways I look at it is we are a $19 billion startup, which means we've got a lot of companies out there that are trusting us to, no matter what change we're doing, continue to deliver their operations, do it flawlessly, do it in a way so they can continue to, to service their clients effectively and, and don't break 'em. And, and so that to me, you know, the way we do that and the way I focusing on that is automation Ansible, obviously corridor strategy, getting there. >>Yeah. And I'd like to get your thoughts too, because we seeing a trend, we've been reporting on this with the cloud growth and the scale of cloud and distributed computing going cloud native, the automation is the front and piece center of all conversations. Automate this, make developers go faster. And with the pandemic, we're coming out of that pandemic. You post pandemic with large scale automation, system architecture, a lot more like architectural conversations and customers leaning on new things. Yeah. What are you seeing in this automation framework that you guys are talking about? What's been the hot playbook or recipe or, or architecture to, you know, play on words there, but I mean, this is kind of the, the key focus. >>Yeah. I mean, if you, one of the things that I com customer comp talks, I've been pulled into a lot recently, have all been around thinking about security, right? A lot in terms of security and compli, I think, I mean, think about the world environment as a whole, right here, everything that's been going on. So, so people are, are conscious of how much energy that's being used in their data centers, right? And people are conscious of how secure they are, right? Are they, you know, the, their end customers are trusting them with data information about them, right? And, and they're trusting us to make sure that those systems are secure to make sure that, you know, all that is taken care of in the right way. And so, you know that what's hot security and compliance, right? What can we do in the energy space, right? Can we do things to, to help clients understand better their energy consumption as, as, you know, especially as we get now in Europe to the winter months, can we do things there that'll help them also be better in that space, Right? Reduce their >>Costs and a lot more cloud rails obviously right there. You got closer and you got now Ansible, they're kind of there to help the customers put it together at scale. This has been the big conversation last year, remember was automate, automate, automate, right? This year it's automation everywhere, in every piece of the, the landscape edge. It's been big discussion tomorrow here about event driven stuff. This is kind of a change of focus and scope. Can you like, share your thoughts on how you see how big this is in terms of the, the, the customer journey >>In terms, I'm sorry, in terms of, >>In terms of their architecture, how they're rolling out automation, >>What's their Yeah, yeah. So, so in terms of their rolling out arch, arch in terms of them consuming architecture, right? And the architecture or consuming automation. Yeah. And rolling out the architecture for how they do that. You know, again, it, to me it's, it's a lot of, it's been focused around how do we do this in the most secure manner possible? How do we deliver the service to them and the most secure managers possible? How do they understand that it, that they can trust the automation and it's doing the right things on their environments, right? So it's not, you know, we're not pushing out or, or you know, it's not making bad policies >>And they're leaning on you guys. >>It's, it's not being putting malware out there, right? At the same time we're doing different things. And so they really rely on, on our customers, rely on us to really help them with that journey. >>I think a, a big part of that with Kendra as such a great partner and so many customers trusting them, is the fact that they really understand that enterprise. And so as, as Scott talks about the security aspect, we're not just talking to the IT operations people, right? We're talking across the enterprise, the security, the infrastructure, and the automation around that. So when we talk about hybrid cloud, we talk about network and security edge is a natural conversation to that, cuz absolutely at the edge network and security automation is critical. Otherwise, how are you gonna manage just the size of your edge as it grows? >>Yeah. And, and we've been, and that's another area that we've been having a a lot more conversations with clients on, is how do you do automation for IOT and edge based devices, right? We, you know, traditionally data center cloud, right? Kind of the core pieces of where we've been focusing on, but I, you know, recently I've been seeing a lot more opportunities and a lot more companies coming forward saying, you know, help us with the network space, help us with the iot space. We really wanna start getting to that level of automation and that part of our environments. And what >>Are some of the key barriers that customers are coming to you with saying, help us overcome these so that they can, you're smiling so that they can, can obviously attract and retain the right talent and also be able to determine what processes to automate to extract the most value and the most ROI for the organization. >>Yeah. And, and, and you know, that's, that's an interesting, the ROI conversation's always an interesting one, right? Because when you start having that with customers, some of the first things they think about, or the first, the natural place people go is, >>Oh, >>Labor takeout. I can do this with less people. Right? But that's not the end all be all of automation. In fact, you know, my personal view is that's, you know, maybe the, the the bottom 30%, right? That's kind of, then you have to think about the value you get above and beyond that standard operations, standardized processes, right? How are you gonna able to do those faster? How's that enabling your business, right? What's all the risks that's now been taken out by having these changes codified, right? By having them done in a manner that is repeatable, scalable, and, and, and really gets them to the point of, you know, what their business needs from an operational standpoint and >>Extracting that value. Nelson, talk about the automation journey from your perspective, How have you seen that evolve from your lens, especially over the last couple of years? >>It's a great question. You know, it's interesting because obviously all of our customers are at different stages of their automation journey. We have someone that just beginning looking at automation, they've been doing old scripts, if you will, the past. And then we have more that are embracing it, right? As a culture. So we have customers that are building cultures of automation, right? They have standups, they have automation guilds. It's, it's kind of a little bit of a, of a click. It's kind of, you know, building up steam in that momentum. And then we have, you know, the clients that Kindra works with, right? And they're very much focused on automation because they understand that they have a lack of resources, they don't have the expertise, they don't have the time to be able to deliver all this. Yeah. And that's really, Kendra really comes into effect to really help those customers accelerate their automation. Yeah. Right. And to that point, you know, we're doing a lot of innovation work with Kendra and we lean on them heavily because, you know, they're willing to make that commitment as a partner both on the, the, the day to day work that we do together as well as Ford looking at different architectures. >>Yeah. And, and the community aspect from our side internally has been tremendous in terms of us being able to expand what we'll be doing with automation and, and what a's been able to do with that community to get there. Right? Yeah. So to last month we did about 33 million day one, day two operations through automation, right? So that's what we've done. If you look at it, you know, if I break it down, it's really 80% of that standard global process stuff that we bring to the table. 20% of that is what our, our account teams are bringing specifically to their clients based on their needs and what they need to get done. Right. You know, one of my favorite examples of of, of this, right? We have a automation example out there for a, a client we've got in Japan, right? They tie, you know, they're, they're obviously concerned, you know, security a everything else that we've been talking about. >>They're also concerned about resiliency, right? In the face of natural disasters. Yeah. So they took our automation, they said, Okay, we're gonna tie your platform to seismic data that's coming through, and we understand what seismic data's happening. Okay, it's hitting a certain event. Let's automatically start kicking off resiliency operations so we can be prepared and thus keeps serving our clients when that's happening. Right? And that's not something like when you talk about a global team coming in and, and saying, we're gonna do all this. It's that community aspect, getting, getting the account focus, getting to that level, right? That's really brings value to clients. And that's one of the use cases, you know, and aaps enabled us to do with the a the community approach. We've got >>Now talk about this partnership. I think earlier when we were talking to Stephanie and Tom, the bottoms up Ansible community with top down kind of business objectives kind of come into play. You guys have a partnership where it's, there's some game changing things happening because Ansible's growing, continuing to have that scope grow from a skill set standpoint, expand the horizons, doing more automation at scale, and then you got business objectives where people wanna move faster in their, in their digital transformation. So to me, it's interesting that this part kind of hits both. >>It does really hit both. I mean, you know, the community cloud that Kendra has is so critical, right? Because they build that c i CF architecture internally, but they follow that community mantra, if you will. And community is so important to us, right? And that's really where we find innovation. So together with what we were call discussing about validated content earlier today becomes critical to build that content to really help people get started, Right? Validated content, content they can depend on and deliver, right? So that becomes critical on the other side, as you mentioned, is the reality of how do we get this done? Yeah. Right? How do we mature, how do we accelerate? And without the ability to drive those solutions to them to fix, if you are the problems that the line of business has. Well, if you don't answer those questions with the innovation, with the community, and then with the ap, it's, it, it does, it's gotta all come >>Together as, I mean, that community framework is interesting. I think we hear a lot in the cube, you know, Hey, let's do this. Sounds good. Who's gonna do it? Someone who's the operator. So there's a little skills gap going on. It's also a transformation in the roles of the operators in particular, and the dev, So the DevOps equation's completely going to the next level, right? And this is where people wanna move faster. So you're seeing a lot more managed services, a lot more Yes. Services that's, I won't say so much top down, but more like, let's do it and here's a play to get it done, right? Then backfill on the hiring, whether it's taking on a little bit of technical debt or going a little faster to get the proof points, >>Right? And I think one of the critical aspects is, you know, Ansible has it certified collections, right? And oftentimes we, we don't, I don't, I meet with customers two, three times a week, right? There's not a single one that doesn't emphasize the importance of partners and the importance of certified collections, Right? And kindra is included in that, right? Because they bring a lot of those certified collections. Use them, leverage them, it's helps customers get a jumpstarter, right? It's a few, it's their easy button, right? But they only get that and they value that because of the support that's there. >>Yeah. Right? They get the with >>The cert. Yeah. I was gonna say, just adding on the certified collections, right? We, so, you know, it was, it was great to see the hub come out with those capabilities because, you know, as we've gone through the last 12 months and, and change, one of the things that we focused more in on is network devices, network support, right? And, and so, you know, some of the certified collections out there for Cisco for F five, right? Some of those things we've been able to take back in and now build on top of with the expertise that we, we have in that space as well. And then use that as a starting point to more value for our clients. >>How is Kentrell working together with, with Red Hat and with Ansible to help organizations like you mentioned Nelson, they're on the journey varies considerably. Some are well on their way, others aren't. But for those to really start developing an automation, first culture, we talked a lot about cultural ship, we talked about it this morning. You can feel the power of that community and driving it, but how do you guys work together to help companies and any industry kind of really start understanding what an automation first culture is and then building it internally and getting some grounds? Well, >>Well, it's interesting, right? One of the, one of the things that really is we found really helpful is assessments, right? So you have silos and pockets of automation, and that's that challenge, right? So to be able to bring that, if you are automation community within an enterprise together, we often go out and we'll do an assessment, right? An automation assessment to really understand holistically how the enterprise could leverage automation not just in the pockets, but to bring it together. And when they bring that automation together, they can share, playbooks can share their experiences, right? And with Kindra and the multiple and the practices they have, right? They really bring that home from an industry perspective. They also bring that home, if you will, from a technology perspective. And they bring that together. So, you know, Kindra in that respect is the glue for our customer success. >>What's news? What's the next big thing that you guys see? Because if this continues down the road, this path, people are gonna get, the winds gonna get the successes. The new beachhead, if you will, is established. You got the edge around the corner. What's next for you guys in the partnership? How do you see it developing? >>No, we're looking at >>No, it's all good. So really, you know, I, I mentioned it earlier and, and the jour the automation journey paralleled by innovation, right? Customers today are automating, they're doing a great job. There's multiple tools out there. We understand we're not gonna be the only tool in the shed, but Ansible can come in and integrate that entire environment. And in a hybrid cloud environment, you want that there, right? I think what next is obviously the hybrid cloud is critical. The edge is critical, right? And I think that, you know, the needs and the requirements that Kindra hears that we have is kind of that future. And, you know, we, we often, often in, in Red Hat, we talk about a north star, right? And when I work with partners, ikin, do we talk about the North Star, where we want to get to? And that is the acceleration of automation. And I think both by the practical aspect of working with our customers and the innovation as partners, as business partners, technology partners will help accelerate >>That. Yeah. Scott, your perspective to bridge to the future is obviously hybrid and edge, how you bringing your customers along? >>Yes. So, so we see, you know, when we talk about my, when I talk about my automation strategy, our automated strategy, right? It's about being automated, orchestrated and intelligent, right? Kind of those, those three layers of the stack. We've been building out a lot of work, what we call our integrated AIOps layer for actionable insights, right? We've got a, you know, a goal to integrate that and, and we have integrated into our automation service for how we're delivering the whole package to our clients so they can better see opportunities for automation. What's the best way to go about it? You know, what are the, what are some of the, the issues they have, vulnerabilities they have in their environment and really bringing it to them in, in a real holistic manner. In fact, we internally, we call it our F five steering wheel, right? Based on the, the race thing, right? >>Because you think about the, the racing cars, f fives know they're right there, right? They got everything they need in front of 'em. Yeah. So our goal is been to, to include that into our automation view and service and build that out, right? So that's one way we're doing it. The additional way is, is through some announcements you probably heard, hopefully heard the last couple weeks through something called Kendra Bridge, right? Kendra Bridge is more the digitization of, of the way we deliver services for our clients to make it easier for them to consume and, and to, to make the barrier to entry for things like getting automation, getting it more in their environment, right? Lower as much as possible, right? So really integrated AIOps kind bridge. Those are really the two ways we see it as, as going forward. >>It's interesting, you know, we live through a lot of these different inflection points in the industry. Every time there's a big inflection point, there's more complexity that needs to be tamed, you know? And so you got innovation. If you got innovation coming and you got the clients wanna simplify and tame the complexity, this is a big part of what you guys do. >>Absolutely. Yeah. I mean, how do we, you know, most, when the clients come to us, right? Like I said, one, it's about trust. They trust us to do it because we can make it easy for them to not have to worry about that, right? Yeah. They don't have to worry about what it takes to secure the environment, manage it, run it, design it, build it for the, the cloud. We give 'em the ability, we give them the ability to focus on their core business while we do the stuff that's important to them, which >>Is absolutely critical that you, you can't emphasize trust in this relationship enough. I wish we had more time, guys, you're gonna have to come back. I think that's basically what this is boil down to. But thanks so much guys for talking with John and me about how Kendra and and Ansible are working together, really enabling your customers to, to unlock the value of automation across their organization and really make some big business changes. We appreciate your insights and your time. Fantastic. Thank you. Happy to do it and happy to do it any time. All right. Our pleasure. Thank you so much for our guests and John Furrier. I'm Lisa Martin. You're watching The Cube Live from Chicago. This is day one of our coverage of Ansible Fest 22. Don't go anywhere. Our next guest joins us in just a minute.

>> Hello everyone, we're here at the Snowflake Data Cloud Summit. This is the Tech Titans panel. We're going to explore some of the trends that are shaping new data capabilities and specifically how organizations are transforming their companies, with data and insights. And with me are three amazing guest panelists. Christian Kleinerman is the senior vice president of product at Snowflake. He's joined by Mark Nelson, who's the EVP of product development at Salesforce/Tableau and Mai-Lan Thompson Bukovec, who's the vice president of Block and Object Storage at Amazon web services. Folks, thanks so much for coming on the program. Great to see you all. >> Thanks for having us. >> Nice to see you. >> Glad to be here. >> Excellent, so here in this session, you know, we have the confluence of the data cloud. We have simple and cost effective storage repositories and the visualization of data. These are three ingredients that are really critical for quickly analyzing and turning data into insights and telling stories with data. So, Christian, let me start with you. Of course, this is all enabled by the Cloud and Snowflake. You're extending that to this data cloud. One of the things that we can do today with data that we say weren't able to do maybe five years ago. >> Yeah, certainly I think there is lots of things that we can integrate specific actions but if you were to zoom out and look at the big picture, our ability to reason through data to inform our choices to date with data is bigger than ever before. There are still many companies that have to decide to sample data or to throw away older data, or they don't have the right data from external companies to put their decisions and actions in context. Now we have the technology and the platforms to bring all that data together, tear down silos and look a 360 of a customer or entire action. So I think it's reasoning through data that has increased the capability of organizations dramatically in the last few years. >> So Mai-Lan, when I was a young pup, at IDC, I started the storage program there, many, many moons ago. And so I always pay attention to what's going on in storage, back of my mind. And S3 people forget, sometimes, that was actually the very first cloud product announced by AWS, which really ushered in the cloud era. And that was 2006, it fundamentally changed the way we think about storing data. I wonder if you can explain how S3 specifically in an object storage generally, you know, with get put really transformed storage from a blocker to an enabler of some of these new workloads that we're seeing. >> Absolutely, I think it has been transformational for many companies in every industry. And the reason for that is because in S3, you can consolidate all the different data sets that today are scattered around so many companies, different data centers. And so if you about it, S3 gives the ability to put unstructured data which are video recordings and images. It puts semi structured data which is the CSV file, which every company has lots of. And that has also support for structured data types like parquet files, which drive a lot of the business decisions that every company has to make today. And so if you think about S3, which launched on Pi day in March of 2006, S3 started off as an object store, but it has evolved into so much more than that, where companies all over the world, and every industry are taking those different data sets, they're putting it in S3, they're growing their data and then they're growing the value that they capture on top of that data. And that is the separation we see that snowflake talks about and many of the pioneers across different industries talk about, which is a separation of the growth of storage and the growth of your computer applications. And what's happening is that when you have a place to put your data like S3, which is secure by default and has the availability and the durability and the operational profile you know, and can trust, then the innovation of the application developers really take over, and you know, one example of that is where we have a customer in the financial sector and they started to use S3 to put their customer care recordings. And they were just using it for storage because that obviously dataset grows very quickly. And then somebody in their fraud department got the idea of doing machine learning on top of those customer care recordings. And when they did that they found really interesting data that they could then feed into their fraud detection models. And so you get this kind of alchemy of innovation that happens when you take the datasets of today and yesterday and tomorrow you put them all in one place which is the history and the innovation of your application, developers just takes over and builds, not just what you need today but what you need in the future as well. >> Thank you for that. Mark, I want to bring you into this panel. It's great to have you here. So thank you. I mean, Tableau has been a game changer for organizations. I remember my first, Tableau conference, passionate customers and really bringing cloud-like agility and simplicity to visualization just totally changed the way people thought about data and met with massive data volumes and simplified access. And now we're seeing new workloads that are developing on top of data and Snowflake data and the cloud. Can you talk about how your customers are really telling stories and bringing to life those stories with data on top of things like S3, which Mai-Lan was just talking about? >> Yeah, for sure. Building on what Christian and Mai-Lan have already said our mission at Tableau has always been help people see and understand data. And you look at the amazing advances that are happening in storage and data processing. And now, the data that you can see and play with is so amazing, right? Like at this point in time, it's really nothing short of a new microscope or a new telescope that really lets you understand patterns. They were always there in the world, but you literally couldn't see them because of the limitations of the amount of data that you could bring into the picture, because of the amount of processing power and the amount of sharing of data that you could bring into the picture. And now like you said, these three things are coming together and this amazing ability to see and tell stories with your data combined with the fact that you've got so much more data at your fingertips, the fact that you can now process that data, look at that data share that data in ways that was never possible. Again, I'll go back to that analogy. It feels like the invention of a new microscope, a new telescope a new way to look at the world and tell stories and get to insights that were just, were never possible before. >> So thank you for that, and then Christian I want to come back to this notion of the data cloud and, you know, it's a very powerful concept and of course it's good marketing, but I wonder if you could add some additional color for the audience. I mean, what more can you tell us about the data cloud, how you're seeing it evolving and maybe building on some of the things that Mark was just talking about just in terms of, you know, bringing this vision into reality? >> Certainly, yeah. Data cloud for sure, is bigger and more concrete than just the marketing value of it. The big insight behind our vision for the data cloud is that just the technology, a capability, just a cloud data platform is not what gets organizations to be able to be a data driven, to be able to make great use of data or be highly capable in terms of data ability. The other element beyond technology is the access and availability of data to put their own data in context or enrich based on the knowledge or data from other third parties. So the data cloud, the way to think about it is, is a combination of both technology, which for Snowflake is our Cloud Data platform in all the workloads, the ability to do data warehousing and queries and speeds and feeds fit in there and data engineering, et cetera. But it's also, how do we make it easier for our customers to have access to the data that they need or they could benefit to improve the decisions for their own organizations. Think of the analogy of a set top box. I can give you a great technically set top box but if there's no content on the other side, it makes it difficult for you to get value out of it. That's how we should all be thinking about it, the data cloud, it's technology, but it's also seamless access to data. >> And Mai-Lan, can you give us a sense of the scope and what kind of scale are you seeing with Snowflake on AWS? >> Well, Snowflake has always driven as Christian as a very high transaction rate to S3. And in fact, when Christian and I were talking just yesterday, we were talking about some of the things that have really been remarkable about the long partnership that we've had over the years. And so I'll give you an example of how that evolution has really worked. So as you know, S3 has, is, you know, the first AWS services that is launched and we have customers who have petabytes, hundreds of petabytes and exabytes of storage on history. And so from the ground up S3 has been built for scale. And so when we have customers, like Snowflake that have very high transaction rates for requests, for S3 storage, we put our customer hat on and we ask customers like Snowflake, how do you think about performance? Not just what performance do you need but how do you think about performance? And you know, when Christian and his team were working through the demands of making requests to their S3 data, they were talking about some pretty high spikes over time and just a lot of volume. And so when we built improvements, into our performance over time, we put that hat on for work, you know, Snowflake was telling us what they needed. And then we built our performance model not around a bucket or an account. We built it around a request rate per prefix, because that's what Snowflake and other customers told us they needed. And so when you think about how we scale our performance, we scale it based on a prefix and not a bucket in our account, which other cloud providers do. We do it in this unique way because 90% of our customer roadmap across AWS comes from customer requests. And then that's what Snowflake and other customers were saying is that, "Hey, I think about my performance based on a prefix and of an object and not some, you know, arbitrary semantic of how I happened to organize my buckets." I think the other thing I would also throw out there for skill is, as you might imagine, S3 is a very large distributed system. And again, if I go back to how we architected for our performance improvements, we architected in such a way that a customer like Snowflake, could come in and they could take advantage of horizontally scaling. They can do parallel data retrievals and puts in gets for your data. And when they do that they can get tens of thousands of requests per second because they're taking advantage of the scale of S3. And so, you know, when we think about scale it's not just scale which is the growth of your storage, which every customer needs. IDC says that digital data is growing at 40% year over year. So every customer needs a place to put all of those storage sets that are growing. But the way we also have worked together for many years is this, how can we think about how Snowflake and other customers are driving these patterns of access on top of the data, not just the last history of the storage, but the access and then how can we architect often very uniquely as I talked about with our request rate in such a way that they can achieve what they need to do not just today, but in the future. >> I don't know, three companies here that don't often take their customer hats off. Mark, I wonder if we could come to you, you know, during the Data Cloud Summit, we've been exploring this notion that innovation in technology is really evolved from point products you know, the next generation of server or software tool to platforms that made infrastructure simpler or called functions and now it's evolving into leveraging ecosystems. You know, the power of many versus the resources of one. So my question is, you know, how are you all collaborating and creating innovations that your customers can leverage? >> Yeah, for sure, so certainly, you know Tableau and Snowflake, you know, kind of where were dropped at natural partners from the beginning, right? Like putting that visualization engine on top of Snowflake to, you know, combine that processing power and data and the ability to visualize it was obvious. As you talk about the larger ecosystem now of course, Tableau is part of Salesforce. And so there's a much more interesting story now to be told across the three companies, one in two and a half maybe as we talk about Tableau and Salesforce combined together of really having this full circle of Salesforce you know, with this amazing set of business apps that so much value for customers and getting the data that comes out of their Salesforce applications, putting it into Snowflake so that you can combine that, share that, you process it combine it with data, not just for across Salesforce, but from your other apps in a way that you want. And then put Tableau on top of it. Now you're talking about this amazing platform ecosystem of data, you know, coming from your most valuable business applications in the world with the most, you know, sales opportunity objects, marketing, service, all of that information flowing into this flexible data platform and then this amazing visualization platform on top of it. And there's really no end of the things that our customers can do with that combination >> Christian we're out of time, but I wonder if you could bring us home and I want to end with, you know let's say, you know, people, some people here maybe they don't, maybe they're still struggling with the cumbersome nature of let's say their on-prem data, warehouses. You know, the kids just unplugged them because they rely on them for certain things like reporting but let's say they to raise the bar on their data and analytics, what would you advise for a next step for them? >> Yeah I think the first part or first step to take is around embrace the cloud and the promise on the abilities of cloud technology. There's many studies where relative to peers, companies that are embracing data are coming out ahead and outperforming their peers. And with traditional technology on-prem technology, you ended up with a proliferation of silos and copies of data. And a lot of energy went into managing those on-prem systems and making copies and data governance and security and cloud technology and the type of platform that the Snowflake has brought to market enables organizations to focus on the data, the data model, the data insights, and not necessarily on managing the infrastructure. So I think that will be the first recommendation from our end. Embrace cloud, get onto a modern cloud data platform, make sure that you're spending your time on data, not managing infrastructure and seeing what the infrastructure lets you do. >> It makes a lot of sense, guys. Thanks, thanks so much. We'll have to end it there and thank you everybody for watching. Keep it right there. We'll be back, with the next segment, right after this short break.

>>from around the globe. It's the Cube with coverage of Susic on digital brought to you by Susan. >>Welcome back. I'm stew minimum in coming to you from our Boston area studio. And this is the Cube's coverage of Silicon Digital 20. Happy to welcome to the program. Two of the keynote president presenters. First of all, we have Dr Mr Giacomo. He is the president of engineering and innovation and joining him, his presenter on the keynote stage, Daniel Nelson, who is the Vice president of Product solutions. Both of you with Souza. Gentlemen, thanks so much for joining us. >>Thank you. Thank you for having us. >>All right, So? So, Dr T let let's start out. You know, innovation, open source. Give us a little bit of the message for our audience that Daniel are talking about on stage. You know how you know we've been watching for decades the growth in the proliferation of open source and communities. So give us the update there, >>Andi. It's not stopping. It's actually growing even more and more and more and more innovations coming from open source. The way we look at it is that our customers that they have their business problems have their business reality. Andi s So we we have to curate and prepare and filter all the open source innovation that they can benefit from because that takes time to understand that. Match your needs and fix your problems. So it's Susa. We've always done that since 27 per sales. So working in the open source projects innovating they are, but with customers in mind. And what is pretty clear in 2020 is that large enterprises, small startups. Everybody's doing software. Everybody's doing, I t. And they all have the same type of needs in a way. They need to simplify their landscape because they've been accumulating investments all the way. Our infrastructure Joseph well, different solutions, different platforms from different bundles. They need to simplify that and modernize and the need to accelerate their business, to stay relevant and competitive in their own industries. And that's what we're focusing on. >>Yeah, it's interesting. I completely agree. When you say simplify thing, you know, Daniel, I I go back in the communities about 20 years, and in those days, you know, we were talking about the operating clinic was helping to, you know, go past the proprietary UNIX platforms. Microsoft, the enemy. And you were talking about, you know, operating system server storage, the application that it was a relatively simple environment and inherited today's, you know, multi cloud ai in your based architecture, you know, applications going through this radical transformation growth, though, give us a little bit of insight as to, you know, the impact this is having on ecosystems. And of course, you know, Susie's now has a broad portfolio that at all >>it's a great question, and I totally get where you're coming from. Like if you look 20 years ago, the landscape is completely different that the technologies were using or you're completely different. The problems were trying to solve with technology are more and more sophisticated, you know, at the same time that you know, there's kind of nothing new under the sun, every company, every technology, you know, every you know, modality goes through. This expansion of capabilities and the collapse around simplification is the capabilities become more more complex, manageable. And so there's this continuous tension between capabilities, ease of use, consume ability. What we see with open source is that that that that's kind of dynamic that still exist, but it's more online of like. Developers want easy to use technologies, but they want the cutting edge. They want the latest things. They want those things within their packets. And then if you look at operations groups or or or people that are trying to consume that technology, they want that technology to be consumable simple. It works well with others. People tend to pick and choose and have one pane of glass field operate within that. And that's where we see this dynamic. And that's kind of what the Susan portfolio was built. It's like, How do we take, you know, the thousands and thousands of developers that are working on these really critical projects, whether it's Linux is like you mentioned or kubernetes or for cloud foundry? And how do we make that then more consumable to the thousands of companies that are trying to do it, who may even be new to open source or may not contribute directly but have all the benefits that are coming to it. And that's where Susan fits and worse. Susan, who's fits historically and where we see us continuing to fit long term, is taking older is Legos. Put it together for companies that want that and then allow them a lot of autonomy and choice and how these technologies are consumed. >>One of the themes that I heard you both talked about in the keynote it was simplifying modernized. Telerate really reminded me of the imperatives of the CIO. You know, there's always run the business they need to help grow the business. And if they have the opportunity, they want to transform the business. I think you know, you said run improve in scale scale. Absolutely. You know, a critical thing that we talk about these days when I think back to the Cloud Foundry summit. You know, on the keynote stage, it was in the old way. If I could do faster, better, cheaper. Ah, you could use two of them today. We know faster, faster, faster is what you want. So >>it was a >>little bit of insight as to who you know, you talked about, you know, cloud foundry and kubernetes application modernization. You know, what are the imperatives that you're hearing from customers? And how are we with all of these tools out there? Hoping, You know, I t not just be responsive to the business, but it actually be a driver for the transformation of the business. >>It's a great question. And so when I talk to customers and Dr T feel free to chime in, you talked. You know, as many or more customers than then Ideo. You know they do have these these what are historically competing imperatives. But what we see with the adoption of some of these technologies that that faster is cheaper, faster is safer, you know, creating more opportunities to grow and to innovate better is the business. It's not risk injection when we change something, it's actually risk mitigation when we get good and changing. And so it's kind of that that that modality of moving from, um, you know, a a simplify model or very kind of like a manufacturing model of software so much more organic, much more permissive, much more being able to learn with an ecosystem style. And so that's how we see companies start to change the way they're adopting the technology. What's interesting about them is that same level of adoption that seemed thought of adoption is also how open source is is developed open source is developed organically is developed with many eyes. Make shallow bots is developed by like, Let me try this and see what happens right and be able to do that in smaller and smaller recommends. Just like we look at red Green deployments or being able to do micro services or binary or any of those things. It's like let's not do one greatly or what we're used to in waterfall, cause that's actually really risk. Let's do many, many, many steps forward and be able to transform an iterative Lee and be able to go faster iterative Lee and make that just part of what the business is good at. And so you're exactly right, like those are the three imperatives of the CIO. What I see with customers is the more that they are aligning those three areas together and not making them separate. But we have to be better at being faster and being transformed. And those are the companies that are really using I t. As a competitive advantage within the rich. >>Yeah, because most of the time they're different starting points. They have a history. They have different business strategy and things they've done in the past, you need to be able to accommodate all of that and the faster micro service, that native developments for sure, for the new APS. But they're also coming from somewhere on diff. You don't take care of that. You get are you can just accelerates if you simplify your existing because otherwise you spend your time making sure that your existing it's still running. So you have to combine all of that together. And, yeah, do you mentioned about funding and communities? And that's really I love those topics because, I mean, everybody knows about humanities. Now. It's picking up in terms of adoption in terms of innovation, technology building ai ml framework on top of it now, what's very interesting as where is that cloud? Foundry was designed for fast software development until native from the beginning, that 12 factor app on several like 45 years ago. Right? What we see now is we can extract the value that cloud foundry brings to speed up and accelerate your stuff by the Romans hikers, and we can combine that very nicely on very smoothly, simple in a simple way, with all the benefits you get from kubernetes and not from one communities from your communities running in your public clouds because you have records. They are. You have services that you want to consume from one public clouds. We have a great silicon fireside chat with open shot from Microsoft Azure actually discussing those topics. You might have also communities clusters at the edge that you want to run in your factory or close to your data and workloads in the field. So those things and then you mentioned that as well, taking care of the I T ops, simplify, modernize and accelerate for the I T ops and also accelerates forward their local themselves. We're benefiting from a combination of open source technologies, and today there's not one open source technology that can do that. You need to bundle, combine them, get our best, make sure that they are. They are integrated, that they are certified to get out of their stable together, that the security aspects, all the technology around them are integrating the services as well. >>Well, I'm really glad you brought up, you know, some of those communities that are out there, you know, we've been saying for a couple of years on the Cube. You know, Kubernetes is getting baked in everywhere. You know, Cisco's got partnerships with all the cloud providers, and you're not fighting them over whether to use a solution that you have versus theirs. I worry a little bit about how do I manage all of those environments. You end up with kubernetes sprawl just like we have with every other technology out there. Help us understand what differentiates Tuesday's, you know, offerings in this space. And how do you fit in with you know, the rest of that very dynamic and defer. >>So let me start with the aspect of combining things together on and Danielle. Maybe you can take the management piece. So the way we are making sure that Sousa, that we don't also just miles into a so this time off tools we have a stack, and we're very happy if people use it. But the reality is that there are customers that they have. Some investments have different needs. They use different technologies from the past. But we want to try different technologies, so you have to make sure that's for communities. Like for any other part of the stack. The I T stock of the stack. Your pieces are model around that you can accommodate different. Different elements are typically at Susa. We support different types off hyper visors. Well, that's focused on one. But we can support KPMG's and I probably this way, all of the of the Nutanix, hyper visor, netapp, hyper visors and everything. Same thing with the OS. There's not only one, we know that people are running, and that's exactly the same. Which humanities? And there's no one, probably that I've seen in our customer base that will just need one vendor for communities because they have a hybrid needs and strategy, and they will benefit from the native communities they found on a ks e ks decay. I remember clouds, you name them Andi have vendors in Europe as well. Doing that so far for us, it's very important that we bring us Sutro. Custom. Males can be combined with what they have, what they want, even if it's from the circle competition. And so this is a cloud. Foundry is running on a case. You can find it on the marketplace of public clouds. It could run on any any any communities. He doesn't have to be sitting on it. But then you end up with a lot of sales, right? How do we deal with that? >>So it's a great question, and I'll actually even broaden that out because it's not like we're only running kubernetes. Yes, we've got lots of clusters. We've got lots of of containers. We've got lots of applications that are moving there, but it's not like all the V M's disappear. It's not like all the beige boxes, like in the data center, like suddenly don't exist. You know, we we we all bring all the sense and decisions in the past word with us wherever we go. And so for us, it's not just that lens of how do we manage the most modern, the most cutting edge? That's definitely a part of it. But how do you do that? Within the context of all the other things you have to do within your business? How do I manage virtual virtual machines? How do I manage bare metal? How do I manage all those? And so for us, it's about creating a presentation layer on top of that where you can look at your clusters. Look at your V EMS. Look at all your deployments and be able to understand what's actually happening with the fire. We don't take a prescriptive approach. We don't say you have to use one technology. You have to use that. What we want to do is to be adaptive to the customer's needs. And so you've got these things here, some of our offerings. You've got some legacy offerings to Let's show you bring those together. Let's show you how you modernize your viewpoints, how you simplify your operational framework and how you end up accelerating what you can do with the staff that you've got in place. >>Yeah, I'm just on the management piece. Is there any recommendation from your team? You know, last year at Microsoft ignite, there was the launch of Azure are on. And, you know, we're starting to see a lot of solutions come out. There are concerns. Is that any of us that live through the multi vendor management days, um, you know, don't have good memories from those. It is a different discussion if we're just talking about kind of managing multiple kubernetes. But how do we learn from the past and you know, What do you recommend for people in this, you know, multi cloud era. >>So my suggestion to customers is you always start with what are your needs? What is strategic problems you're trying to solve, and then choose a vendor that is going to help you solve those strategic problems? So is it going to take a product centric view Isn't gonna tell you use this technology and this technology and this technology, what is going to take the view of, like, this is the problem you're gonna solve? Let me be your advisor within that and choose people that you're going to trust within that, um, that being said, you wanna have relationships with customers that have been there for a while that have done this that have a breath of experience in solving enterprise problems because everything that we're talking about is mostly around the new things. But keep in mind that there are there are nuances about the enterprise. There are things that are that are intrinsically bound within the enterprise that it takes a vendor with a lot of enterprise experience to be able to meet customers where they are. I think you've seen that you know in some of the some of the real growth opportunities with them hyper scaler that they've kind of moved into being more enterprise view of things, kind of moving away from just an individual bill perspective, enterprise problems. You're seeing that more and more. I think vendors and customers need to choose companies that meet them where they are that enable their decisions. Don't prescribe there. >>Okay, go ahead. >>Yes, Sorry. Yeah. I also wanted to add that I would recommend people to look at open source based solutions because that will prevent them to be in a difficult situation, potentially in the three years from now. So there are open source solutions that can do that on book. A viable, sustainable, healthy, open source solutions that are not just one vendor but multi vendor as well, because that leaves those open options open for you in the future as well. So if you need to move for another vendor or if you need to implement with an additional technology, you've made a new investment or you go to a new public clouds. If you based Duke Tracy's on open source, you have a little chance but later left >>I think that's a great point. Dr. T and I would you know, glom onto that by saying customers need to bring a new perspective on how they adjudicate these solutions, like it's really important to look at the health of the open source community. Just because it's open source doesn't mean that there's a secret army of gnomes that, you know in the middle of the night going fixed box, like there needs to be a healthy community around that. And that is not just individual contributors. That is also what are the companies that are invested in this, where they dedicating resources like That's another level. So what level of sophistication that a lot of customers need to bring into their own vendor selection? >>Excellent. Uh, you know, speaking about communities in open source. Want to make sure you have time share a little bit about the AI platform discussed in your >>Yeah, it's very, very interesting. And something I'm super excited about it, Sousa. And it's kind of this this, uh, we're starting to see ai done in these really interesting problems to solve and like, I'll just give you one example is that we're working with um uh, Formula One team around using AI to help them actually manage in car mechanics and actually manage some of the things that they're doing to get super high performance out of their vehicles. And that is such an interesting problem to solve. And it's such a natural artificial intelligence problem that even when you're talking about cars instead of servers or you're talking about race tracks, you know instead of data centers, you still got a lot of the same problems. And so you need an easy to use AI stack. You need it to be high performance. You needed to be real time. You need to be able to decisions made really quickly, easy, the same kinds of problems. But we're starting to see them in all these really interesting wheels in areas, which is one of the coolest things that I've seen in my career. Especially is in terms of I T. Is that I t is really everywhere. It's not. Just grab your sweater and go to the data center because it's 43 degrees in there. You know, it's also getting on the racetrack. It's also go to the airfield. It's also go to the grocery store and look at some of the problems being being being addressed himself there. And that is super fascinating. One of the things that I'm super excited up in our industry in total. >>Alright, well, really good to discussion here, Daniel. Dr B. Thank you so much for sharing everything from your keynote and been a pleasure washing. >>Thank you. >>Alright, Back with lots more coverage from Susan Con Digital 20. I'm stew minimum. And as always, Thank you for watching. >>Yeah, yeah, yeah.

(upbeat music) >> From around the globe, it's theCUBE with coverage of SUSECON Digital. Brought to you by SUSE. >> Welcome back. I'm Stuart Miniman coming to you from our Boston area studio and this is theCUBE coverage of SUSECON Digital 20. Happy to welcome to the program two of the keynote present presenters. First of all, we have Dr. Thomas Giacomo. He is the President of Engineering and innovation and joining him his co presenter from Makino state, Daniel Nelson, who is the Vice President of Product Solutions, both of you with SUSE. Gentlemen, thanks so much for joining us. >> Thank you. >> Thank you for having us. >> All right. So, Dr. T, Let's start out, innovation, open source, give us a little bit of the message for our audience that you and Daniel were talking about on stage. We've been watching for decades, the growth in the proliferation of open source communities, so give us the update there. >> Yeah. And then it's not stopping, it's actually growing even more and more and more and more innovations coming from open source. The way we look at it is that our customers there, they have their business problems, they have their business reality. And so we, we have to curate, and prepare and filter all the open source innovation that they can benefit from, because that takes time to understand how that can match your needs and fix problems. So at SUSE, we've always done that, since 27 plus years. So, working in the open source projects, innovating there but with customers in mind, and what is pretty clear in 2020 is that large enterprises, more startups, everybody's doing software, everybody's is doing IT and they all have the same type of needs in a way they need to simplify their landscape, because they've been accumulating investments all the way or infrastructure or software, different solutions, different platforms from different vendors. They need to simplify that. They need to modernize, and they need to accelerate their business stay relevant and competitive in their own industries. And that's what we are focusing on. >> Yeah, it's interesting, I completely agree when you say simplify thing, you know, Daniel, I go back in the opportunities about 20 years. And in those days, we were talking about the operating Linux was helping to go past the proprietary Unix platform, Microsoft, the big enemy. And you were talking about operating system, server storage, the application that on, it was a relatively simple environment in there compared to today's multi cloud, AI, container based architecture, applications going through this radical Information broke, though, gives a little bit of insight as to the impact this is having on ecosystems and, of course SUSE now has a broad portfolio that at all? >> It's a great question and I totally get where you're coming from, like, if you look 20 years ago, the landscape is completely different, the technologies we're using are completely different, the problems we're trying to solve with technology are more and more sophisticated. At the same time, though, there's kind of nothing new under the sun. Every company, every technology, every modality goes through this expansion of capabilities and the collapse around simplification as the capabilities become more and more complex and more manageable. So there's this continuous tension between capabilities, ease of use consume ability. What we see with open source is that, that kind of dynamic still exists, but it's more online of like developers want, easy to use technologies, but they want the cutting edge. They want the latest things. They want those things within their packages. And then if you look at operations groups or people that are trying to consume that technology, they want that technology to be consumable simple, works well with others be able to pick and choose and have one pane of glass to be able to operate within that. And that's where we see this dynamic. And that's kind of what the SUSE portfolio was built upon. It's like, how do we take the thousands and thousands of developers that are working on these really critical projects, whether it's Linux like you mentioned, or Kubernetes, or or Cloud Foundry? And how do we make that then more consumable to the thousands of companies that are trying to do it, who may even be new to open source or may not contribute directly, but when you have all the benefits that are coming to it, and that's where SUSE fits and where SUSE has fits historically, and where we see us continuing to fit long term is taken all those Legos, put into together for companies that want that, and then allow them a lot of autonomy and choice and how those technologies are consumed. >> Right, one of the themes that I heard you both talk about, in the keynote, it was simplifying, modernize, celebrate, really reminded me of the imperatives of the CIO. There's always run the business, they need to help grow the business, and if they have the opportunity, they want to transform the business. I think you said, run improve in scale. Scale absolutely a critical thing that we talk about these days, when I think back to the Cloud Foundry summit, in the keynote stage, it was the old way if I could do faster, better, cheaper, you could do them today. We know Faster, faster, faster is what you want. So give us a little bit of insight as to, you talked about Cloud Foundry and Kubernetes, application, modernization, what are the imperatives that you're hearing from customers and how are we, with all of these tools out there helping, IT, not just be responsive to the business but actually be a driver for that transformation of the business? >> It's a great question. And so when I talk to customers, and Dr. T, feel free to chime in, you talk to as many or more customers than I do. They do have these what are historically competing imperatives. But what we see with the adoption of some of these technologies is that faster is cheaper, faster is safer, creating more opportunities to grow and to innovate betters the business. It's not risk injection, when we change something, it's actually risk mitigation, when we get good at changing. And so it's kind of that modality of moving from, a simplified model or a very kind of like a manufacturing model of software to a much more organic, much more permissimuch more being able to learn within ecosystems model. And so that's how we see companies start to change the way they're adopting this technology. What's interesting about them is that same level of adoption. That same thought of adoption, It's also how open sources is developed. Open Source has developed organically, it's developed with many eyes make shallow bugs, it's developed by like, let me try this and see what happens, right? And be able to do that in smaller and smaller increments just like we look at Red Green deployments or being able to do micro services, or Canary or any of those things. It's like, let's not, do one greatly for what we're used to and waterfall is that's actually really risky. Let's do many, many, many steps forward and be able to transform it iteratively and be able to go faster iteratively and make that just part of what the business is good at. And so you're exactly right. Like those are the three imperatives of the CIO. What I see with customers is the more that they are aligning those three imperatives together and not making them separate, but we have to be better at being faster and being transformative. Those are the companies that are really using IT as a competitive advantage within their reach. >> Yeah, because most of the time they have different starting points. They have a history. They have different business strategy and things they've done in the past. So you need to be able to accommodate all of that and the faster microservice, native development posture for the new apps, but they're also coming from somewhere, and if you don't take care of that together, you can just accelerate if you simplify your existing because otherwise you spend your time making sure that your existing is running. So you have to combine all of that together, and the two, you mentioned Cloud Foundry and Kubernetes and I love those topics because, I mean, everybody knows about Kubernetes. Now it's picking up in terms of adoption, in terms of innovation technology, uilding AI ML framework on top of it. Now, what's very interesting as well is that, Cloud Foundry was designed for fast software development, and cloud native from the beginning that by the factor apps, and several like four or five years ago, right? What we see now is we can extract the value that Cloud Foundry brings to speed up and accelerate our software development cycles, and we can combine that very nicely and very smoothly simple in a simple way, with all the benefits you get from Kubernetes, and not from one Kubernetes. From your Kubernetes running in your public clouds because you have workloads there, you have services that you want to consume from one public clouds. We have a great SUSECON fireside chat with open shot from Microsoft. Asia, we're actually discussing those topics. Or you might have also Kubernetes clusters at the edge that you want to run in your factory or close to your data and workloads in the field. So those things and Daniel mentioned that as well taking care of the IT ops, like simplify, modernize and accelerate for the IT ops and also accelerate for the developers themselves, we benefiting from a combination of open source technologies. And today, there's not one open source technology that can do that. You need to bundle combine them together and best make sure that they are integrated, hat they are certified together, that they are stable together, that the security aspects, all the technology around them are deeply integrated into services as well. >> Well, I'm really glad you brought up some of those Kubernetes that are out there. We've been saying for a couple years on theCUBE, Kubernetes is getting baked in everywhere. SUSE's got partnership with all the cloud providers and you're not fighting them over whether to use a solution that you have versus theirs. I worry a little bit about, how do I manage all those environments? Do I end up with Kubernetes sprawl just like we have with every other technology out there? Help us understand what differentiates SUSE's offerings in this space? And how do you fit in with the rest of that very dynamic and diverse. >> So, let me start with the aspect of combining things together. And Daniel, maybe you can take the management piece. So, first of all, we are making sure at SUSE that we don't force our customers into a SUSE stack. Of course we have a SUSE stack, and we're very happy people use it. But the reality is that the customer knows that they have some investments, they have different needs, they use different technologies from the past, or they want to try different technologies. So you have to make sure that for Kubernetes like for any other part of the stack, the IT stack or the developer stack, your pieces are our modular that you can accommodate different different elements. So typically, at SUSE, we support different types of hypervisors We're not like focused on one but we can support KVM, Xen, Hyper-V, vSphere, all of the nutanix hypervisor, NetApp hypervisors and everything. Same thing with the OS, there's not only one Linux that people are running, and that's exactly the same with kubernetes. There's no one probably that I've seen in our customer base that will just need one vendor for Kubernetes because they have a hybrid cloud needs and strategy and they will benefit from the native Kubernetes they found on AKA, CKA, SDK, Alibaba clouds, you name them and we have cloud vendors in Europe as well doing that. So for us, it's very important that what we bring as SUSE to our customers can be combined with what they have, what they want, even if it's from the so called competition. And so the SUSE Cloud Foundry is running on. I guess, you can find it on the marketplace of public clouds. It could run on any Kubernetes. It doesn't have to be SUSE Kubernetes. But then you end up with a lot of cells, right? So how do we deal with that then? >> So it's a great question. And I'll actually even broaden that out because it's not like we're only running Kubernetes. Yes, we've got lots of clusters, we've got lots of containers, we've got lots of applications that are moving there. But it's not like all the VMs disappeared. It's not like all the beige boxes, like in the data center, like suddenly don't exist. We all bring all the sins and decisions of the past board with us wherever we go. So for us, it's not just that lens of how do we manage the most modern, the most cutting edge? That's definitely a part of it. But how do you do that within the context of all the other things you have to do within your business? How do I manage virtual machines? How do I manage bare metal? How do I manage all those. And so for us, it's about creating a presentation layer. On top of that, where you can look at your clusters, look at your VMs, look at all your deployments, and be able to understand what's actually happening within your environment. We don't take a prescriptive approach. We don't say you have to use one technology or have to use that technology. What we want to do is to be adaptive to the customer's needs. And say you've got these things. Here's some of our offerings. You've got some legacy offerings too. Let's show you how to bring those together. Let's show you how you modernize your viewpoints, how you simplify your operational framework and how you end up accelerating what you can do with the stuff that you've got in place. >> Yeah, I'm just on the management piece. Is there any recommendations from your team? Last year at Microsoft Ignite, there was a launch of Azure Arc, and, we're starting to see a lot of solutions come out there. Our concern is that any of us that live through the multi vendor management days, don't have good memories from those. It is a different discussion if we're just talking about kind of managing multiple Kubernetes. But, how do we learn from the past? And, what, what are you recommending for people in this multi cloud era? >> So my suggestion to customers is you always start with what are your needs, what is strategic problems you're trying to solve. And then choose a vendor that is going to help you solve those strategic problems. So isn't going to take a product centric view. Isn't going to tell you, use this technology and this technology and this technology, but it's going to take the view of like, this is the problem you're going to solve. Let me be your advisor within that and choose people that you're going to trust within that. That being said, you want to have relationships with customers that have been there for a while that have done this that have a breadth of experience in solving enterprise problems. Coz, I mean, everything that we're talking about, is mostly around the new things. But keep in mind that there are nuances about the enterprise, there are things that are that are intrinsically found within the enterprise, that it takes a vendor with a lot of experience to be able to meet customers where they are. I think you've seen that in some of the real growth opportunities within the hyper scalars. They've kind of moved into being more enterprise, view of things, kind of moving away from just an individual bill perspective to enterprise problems. You're seeing that more and more. I think vendors and customers need to choose companies that meet them where they are, that enable their decisions, not prescribe their decision. >> Okay. Oh-- >> Let me just add to that. >> Please go ahead. >> Yeah, sorry. Yeah. I also wanted to add that I would recommend people to look at open source based solutions because that will prevent them to be in a difficult situation potentially, in a few years from now. So there are open source solutions that can do that. And look at viable, sustainable, healthy open source solutions that are not just one vendor, but multi vendor as well, because that leaves doors options open for you in the future as well. So if you need to move for another vendor, or if you need to complement with an additional technology, or you've made a new investment or you go to a new public cloud, if you base your choices on open source, you have a better chance but from a data. >> I think that's a great point, Dr. T, and I would glom on to that by saying, customers need to bring a new perspective on how they adjudicate these solutions. Like it's really important to look at the health of the open source community. Just because it's open source doesn't mean that there's a secret army of gnomes that you know, in the middle of the night go and fix box, like there needs to be a healthy community around that. And that is not just individual contributors. That is also what are the companies that are invested in this? Where are they dedicating resources? Like that's another level of sophistication that a lot of customers need to bring into their own vendor selection process. >> Excellent. Speaking about communities and open ports, want to make sure you have time to tell us a little bit about the AI platform discussed. >> Yeah, it's it's very, very interesting and something I'm super excited about it SUSE. And it's kind of this, we're starting to see AI done and it's really interesting problems to solve. And like, I'll just give you one example, is that we're working with a Formula One team around using AI to help them actually manage in car mechanics and actually manage some of the things that they're doing to get super high performance out of their vehicles. And that is such an interesting problem to solve. And it's such a natural artificial intelligence problem that even then you're talking about cars instead of servers or you're talking about racing stack instead of data centers, you still got a lot of the same problems. And so you need an easy to use AI stack, you need it to be high performance, you need it to be real time, you need to be able to get decisions made really quickly. These are the same kinds of problems. But we're starting to see them in all these really interesting real world scenarios, which is one of the coolest things that I've seen in my career, especially as it turns of IT, is that IT is really everywhere. It's not just grab your sweater and go to the data centre, because it's 43 degrees in there, it's also get on the racetrack, it's also go to the airfield, it's also go to the grocery store and look at some of the problems being addressed and solved there. And that is super fascinating. One of the things that I'm super excited about in our industry in total. >> All right, well, really good discussion here. Daniel, Dr. T, thank you so much for sharing everything from your keynote and been a pleasure watching. >> Thank you. >> All right back with lots more covered from SUSECON Digital 20 I'm Stuart Miniman and as always, thank you for watching theCUBE. (upbeat music)

(upbeat electronic music) >> Announcer: From the CUBE Studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Hey welcome back already, Jeff Frick here with theCUBE. We're in our Palo Alto studios today having a Cube conversation. You know nobody can really travel, conference seasons are all kind of on hold, or going to digital, so there's a lot of interesting stuff going on. But thankfully we've got the capability to invite some of our community in. We're really interested in hearing from some of the leaders that we have in the community about what's going on in their world and you know, what they're telling their people. And what can we learn. So we're excited to have a good friend of mine who went to business school together, God it seems like it was over 20 years ago. He's Ben Nelson, the chairman and CEO of the Minerva Project. Ben great to see you and welcome. >> Thanks so much, great to be here. >> Yeah. So, you have always been kind of a trailblazer, I mean way back in the day I think that you've only had like two jobs in all this time, you know. (laughing) You know kind of changing the world of digital photography. >> Yeah three or four, three or four. >> Three or four. >> Yeah. (laughing) >> And after a really long run, you made this move to start something new in education. >> Yeah. >> Education's a big hairy monster. There's a lot of angles. And you started the Minerva Project, and I can't believe I looked before we got on today that that was nine years ago. So tell us about the Minerva Project, how you got started, kind of what's the mission, and then we'll get into it. >> Yeah so Minerva exists and it sounds somewhat lofty for an organization, but we do exist to serve this mission which is to nurture critical wisdom for the sake of the world. We think a wiser world is a better world. We think that really wisdom is the core goal of education and we decided that higher education is the area that is both most in need of transformation and also one that we're most capable of influencing. And so we set about actually creating our own university demonstrating an example of what a university can do. And then, helping tool other institutions to follow in those footsteps. >> Yeah it's a really interesting take. There's often times we're told if a time traveler came here from 1776, right, and walked around and would look at the way we drive, look at the way we communicate, look at the way we transact business. All these things would be so new and novel inventive. If you walked them over to Stanford or Harvard he'd feel right at home, you know. >> Yeah. >> So the education is still kind of locked in to this way that it's always been. So for you to kind of take a new approach, I mean I guess it did take actually starting your own school to be able to execute and leverage some of these new methods and tools, versus trying to move what is a pretty, you know, kind of hard to move institutional base. >> Yeah absolutely. And it's also you know, because we have to remember that universities as an institution started before the printing press. So if you go and talk to pretty much any university president, and ask him or her what is the mission of a university, generically, forget you know your university or what have you. They'll say, "Well generically universities exist "to create and disseminate knowledge." That's why they've been founded 1000 years ago and that's why they exist today. And you know, creation of knowledge I think there's a good argument to be made that the research mission of a university is important for the advancement of society and that it needs to be supported. Certainly directly in that regard. So much of you know the innovation that we benefit from today came from university labs and research. That's an important factor. But the dissemination of knowledge is a bit of an odd thing. I guess before the printing press, sure, yeah, I mean kind of hard to disseminate knowledge except for if you gather a whole bunch of people in a room and talk at them. Maybe they scribble notes very quickly. Well that's a decent way of disseminating knowledge because they can you know, one mouth and many pieces of paper and then they can read it later or study it. I guess that makes sense it's somewhat efficient. But after the printing press and certainly after the internet, the concept of a university needing to disseminate knowledge as it's core mission seems kind of crazy. It can't be that that's what universities are for. But effectively they're still structured in that way. And I don't think any university president when actually challenged in that way would argue the point. They would say, "Oh yes of course, "well what we really need to do is teach people "how to use knowledge or evaluate knowledge "or make sure that we communicate effectively "or understand how that knowledge can interact "with other pieces of knowledge and you know, "create new ways of thinking, et cetera." But that isn't the dissemination of knowledge. And that isn't the way that universities are actually structured. >> But it's funny that you say that. Even before you get to whether they should be still trying to disseminate knowledge, they're not even using the new tools now that they had the printing press that come along. (laughing) To disseminate knowledge. You know it's really interesting as we're going through this time right now with the coronavirus and a lot of things that were kind of traditional are moving in to digital and this new tool called Zoom which never fails to amaze me how many people are having their first Zoom call ever, right. >> Right, right. >> Ever, right I mean how long ago was Skype, how long ago was WebX. These tools have been around for a really interesting time, a long time. But now, you know, kind of a critical mass of technology that anybody can flip their laptop up, or their phone and go. You know you guys just in terms of a pure kind of tools play you know took advantage of the things that are available here in 2020 and 2019. So I wonder if you can share with the folks that don't have experience kind of using remote learning and remote access, you know what are some of the lessons you learned what are some of the best practice. What should people kind of think about what's capable and the things you can do with digital tools that you can't do when you're trying to get everybody in a classroom together at the same time. >> Right, so I think first and foremost, there's kind of the nuts and bolts. The basics. Right. So one of the things that you know, education environments have always been able to get away with is when you've got everyone in a room and you know, you're kind of cutting them off from the rest of life, you sometimes don't realize that you're talking into thin air, right. That maybe speaking students are not listening, they're not absorbing what you're saying. But you know they have to show up, at least in K 12, and higher ed they don't bother showing up and so the 15 people who do wind up showing up from the 100 person lecture I guess you do you say, "Oh at least they're listening." But the reality is that when you're online, you're competing with everything. You're competing with the next tab, you're competing with just not showing up. It's so much easier to just, you know, open up some game or something, some YouTube video. And so you've got to make this engaging. And making it engaging isn't about being entertaining. And that's actually one of the major problems of assessing who is a good professor and who isn't. You know people look at student reviews, right. They say, "Oh, you know such and such "was such a great professor." But when you actually track student reviews of professors to learning outcomes, there's a slight negative correlation. Right which means that the better the students believe the professor is actually that is an indicator that they've learned a little bit less. >> Right. >> That's really bizarre, intuitively. But when you actually think about it deeply, you realize that entertaining students isn't the job of a professor. It's actually teaching them. It's actually getting them to think through the material. And learning is hard, it's not easy. So you have to bring some of those techniques of engagement into online. And you can do that but it requires a lot of interactivity. So that's aspect number one. But really the much bigger idea isn't that you just do what you do offline and then put it online, right. Technology isn't at it's best when it mimics what you do without it, right. Technology didn't build an exact replica of the horse. >> Right, right. >> And said you know, ride that. Right. It doesn't make any sense, right. Instead, what technology should do is things you cannot do offline. One of the things that worked 300, 400 years ago is that you could study a subject matter in full. One professor, one teacher could teach you pretty much everything that people needed to know in a given field. In fact, the fields themselves were collapsed, right. Science, mathematics, you know, ethics were all put under this idea called philosophy. Philosophy was everything. Right. And so there's really we didn't have much to learn. But today, because we have so much information and so many tools to be able to process through that information, what happens is that education gets atomized. And you know you go through a college education you're you know, being taught by 25, 30 some different professors. But one professor really has no idea what you've learned previously. Even when they're in a 101, 102 sequence. How many times have we been in kind of the 102 class where in the first month all the professor did was repeat what happened in the 101 class because they didn't feel comfortable that you actually learned it. Or if the professor before them taught it the way they wanted it taught. >> Right, right. >> And that's because education is done offline with no data. If you actually have education in a data rich environment you can actually design cross cutting curriculum. You can shift the professor's role from disseminating knowledge to actually having students or mentoring students and guiding them in how to apply that knowledge. And so, once you have institutional views of curricula, you can use technology to deliver an institution wide education. Not by teaching you a way of thinking or a set of content, but giving you a set of tools that broadly any professor can agree on, and then apply them to whatever context professors want to present. And that creates a much more holistic education, and it's one that only can be done using technology. >> Ben that was a mouthful. You got into all kinds of good stuff there. (laughing) So let's break some of it down. That was fascinating. I mean I think you know the asynchronous versus synchronous opportunity if you will, to as you said kind of atomize education to the creation of content right the distribution of content and more importantly the consumption of content. Because why should I have to change my day if the person I want to hear is only available next Tuesday at noon pacific, right. It makes no sense anymore. And the long tail opportunities for this content that lives out there forever is pretty interesting. But it's a very interesting you know, kind of point of view if you assume that all the knowledge is already out there and now your job as an educator is to help train people to critically think about what's out there. How do I incorporate that, what are the things I should be thinking about when I'm integrating that into my decision. It's a very different way. And as you said, everything is an alt tab away. Literally the whole world is an alt tab away from that webinar. (laughing) Very good stuff. >> Exactly right. >> And the other piece I want to get your take on is really kind of lifetime learning. And I didn't know that you guys were you know kind of applying some of your principles oh my goodness where you actually measure effectiveness of teaching. And measure how long people hang out in the class. And measure whether it's good or not. But you're applying this really now in helping companies do digital transformation. And I think, you know, coming at that approach from a shift in thinking is really a different approach. I was just looking at an Andy Jassy keynote from a couple years ago yesterday, and he talked about the A number one thing in digital transformation is a buy in at senior leadership and a top down priority. So you know, what do you see in some of your engagements, how are you applying some of this principles to help people think about change differently? >> Yeah you know I think recessions are a very telling time for corporate learning. Right. And if you notice, what is the first budget that gets cut when economic times get tough it's the you know employee learning and development. Right. Those budgets just get decimated. Right off the bat. And that's primarily because employees don't see much value out of it, and employers don't really measure the impact of those things. No one's saying, "Oh my God, 'this is such an incredible program. "My employees were able to do x before this program, 'and then they were able to do one point five x afterward." You know, if people had that kind of training program in the traditional system, then they would be multi-billion dollar behemoths in the space. And there really are not. And that's because again, most of education is done in content land. And it's usually very expensive, and the results are not very good. Instead, if you actually think about learning tools as opposed to information, and then applying those tools in your core business, all of a sudden you can actually see transformation. And so when we do executive education programs as an example, you know we ask our learner how much of what you've learned can you apply to your job tomorrow? Right. And we see an overwhelming majority of our students are saying something like more than 80 to 90% of what they learned they can apply immediately. >> Wow, that's impressive. >> That's useful. >> Right. And why do you think is it just kind of institutional stuck in the mud? Is it the wrong incentive structure? I mean why you're talking about very simple stuff right. >> Yeah. >> Why don't you actually measure outcomes and adjust accordingly, you know. Use a data centric methodology to improve things over time, you know. Use digital tools in way that they can get you more than you can do in a physical space. I mean is it just inertia? I mean I really think this is a watershed moment because now everybody is forced into using these tools. Right. And there's a lot of, you know kind of psychology around habits and habit forming. >> Right, exactly. >> And if you do something for a certain amount of time every single day you know it becomes a habit. And if these stay in place orders which in my mind I think we are going to be doing it for a while, kind of change people's behavior and the way they use technology to interact with other folks. You know it could be a real, you know, kind of turning point in everyone's opening eyes that digital is different than physical. It's not exactly the same. There are some things in physical that are just better, but, you know there's a whole realm of things in digital that you cannot do when you're bound by time, location, and space. >> Exactly right. That's right. And I think the reason that it's so difficult to shift the system is because the training of people in the system, and I'm speaking specifically about higher education, really has nothing to do with education. Think about how a university professor becomes a university professor. How do they show up in a classroom? They get a bachelor's degree, where they don't learn anything about how to teach or how the mind works. They get a PhD, in which they learn nothing about how to teach or how the mind works. They do a post-doctoral research fellowship where they research in their field, right. Then they become an associate professor or an assistant professor and non-tenure, right. And in order to get tenure they've got seven years in order to make it on a publishing track, because how they teach is irrelevant. And they don't get any formal training on how to teach or how the brain works, right. Then they become you know, a junior tenured professor. A full tenured professor, right. And then maybe they become an administrator. Right. And so if you think about it, all they know is their field. And I've had conversations with academics which are to me befuddling, in the sense that you know they'll say, "Oh, you know, "everyone should learn how to think "like a historian. "Oh no everybody should learn to think "like an economist. "Everyone should learn to think "like a physicist." And you kind of unpack it, you say, "Well why?" And it's, "Oh well because we deploy pools "that nobody else deploys and it's so great." Right. And so it's OK give me an example. I had this conversation with a university president who was a historian. And that president said, "Look, you know, "what we do is we look at you know, "primary source materials hundreds of years ago "and learn to interpret what they say to us "and ascertain truth from that. "That's an incredibly important skill." I said, "OK, so what you're saying is you "examine evidence and evaluate that evidence "to see what it can actually tell you. "Isn't that what every single scientist, "social scientist, no matter what field they're in does? "Isn't that what a physicist does? "Isn't that what an economist does? "Isn't that what a psychologist does? "Right, isn't that what an English professor does?" Right actually thinking about I remember I took a mini module when I was an undergraduate with Rebecca Bushnell who is a literature professor, eventually became the dean of the college of arts and science at the University of Pennsylvania. And, we basically looked at a text written 400 years before, and tried to figure out what parts of the text were written by the author, what were transcription errors, and what was censored. That's looking at evidence. >> Right, right. >> This was an English professor. It's the exact same process. But because people know about it in their field and they think the only way to get to it is through their field, as opposed to teaching the tool, it can't get out of their own way. >> Yeah. >> And that's why I think education is so stuck right now. >> Yeah. That's crazy. And you know we're all victims of kind of the context in which we look through everything, and the lens in which we apply to everything that we see which is you know one of my things that there isn't really a kind of a truth it's what is your interpretation. And that's really you know, what is in your head. But I want to close it out. And Ben I really appreciate your time today. It's been a great conversation. And really kind of take it back to your mission which is around critical thinking. You know there's a lot of conversation lately, you know, this kind of rush to STEM as the thing. And there's certainly a lot of great job opportunities coming out of school if you're a data scientist and you can write in R. But what I think is a more interesting conversation is to get out of your own way. You know is the critical thinking as you know the AI and RPA and all these other things kind of take over more of these tasks and really this higher order need for people to think through complex problems. >> Right. >> I mean like we're going through today. Thank God people who are qualified and can see ahead and saw an exponential curve potential just really causing serious damage when we're still to head into this thing to take aggressive action. Dr. Sarah Cody here locally here you know, telling the San Jose Sharks you can't play. You know that is not an easy decision. But thankfully they did and they had the data. But really just your kind of thoughts on why you prioritize on critical thinking and you know can what you see with your students when they get out into the real world applying critical thinking not necessarily equations. >> Yeah look I think there's no better demonstration of how important critical thinking is than when you look at the kinds of advances that STEM is trying to make. Right. What happens any time we get a demonstration of the power of artificial intelligence, right. You remember a few years ago when Microsoft released it's AI engine. Right. Smartest engineers working on it, and all of a sudden it you know spat back misogynist racist types of perspectives. Why? The training set was garbage. It wasn't that the technology was bad, actually it was amazing technology. But the people who were writing it couldn't think. They didn't know how to think two steps ahead and say, "Wait a second, if we train "the information, kind of the random comments "we see on the internet, you know, "who bothers to write anonymomys comments?" Trolls, right. And so if we train it on a troll data set, it'll become an artificial intelligent troll. Right. It doesn't take a lot of critical thinking to actually realize that, but it takes some. >> Right. >> Right. And when you focus merely on those technical skills what you wind up doing is wasting it. Right. And so if you ground people in critical thinking, and we see this with our graduate. You know we graduated our very first class in May. And we had what as far as I can tell is the best graduate school placement of any graduating class in the country. As far as the quality of offers they got. We had a 94% placement rate in jobs in graduate positions. Which I think is tied with the very best ivy league institutions. And the kinds of jobs that the students are getting and six months into them the kinds of reviews that their employers are giving us looks nothing like a recent undergraduate. These are oftentimes types of jobs that are unavailable to recent undergraduates. And you know we had one student recently actually just told me, fresh in my mind, even though he was the youngest person in his company, when the CEO of his company has a strategic question he comes to him. And when he's in a meeting, full of PhDs, everybody looks to him to run the meeting and set the agenda. He's six months out of undergrad, right. And you know I can give you story after story after story about each and every one of these graduate. And it's not because they were born with it. They actually had a wise education. >> Yeah. Ben well that's a great story. And we'll leave it there. Congratulations again to you and the team at Minerva and what you've built and your first graduating class. Great accomplishment and really great to catch up, it's been too long. And when this is all over we'll have to get together and have an adult beverage. >> That would be wonderful. >> All right Ben thanks a lot. >> Thanks so much Jeff. >> All right. You've been watching theCUBE. Great check in with Ben Nelson. Thanks for watching. Everybody stay safe and we'll see you next time. (upbeat electronic music)

from the silicon angle media office in Boston Massachusetts it's the queue now here's your host Stu minimun hi and welcome to a special cube conversation here in our Boston area studio I am Stu minimun and we're digging in with Dell EMC on data protection in the multi cloud where era happy to join welcome to the program first time guest Nelson Nelson Hsu who is the director of solutions marketing with Dell EMC Nelson great to see you great to be here thank you sir all right so you and I were both at Q con plus cloud native con with about 12,000 of our friends in the open-source community down in San Diego California you know when you bring us in first it's probably not the first place that people think of when they think of Delhi MC so explain a little bit what the team was doing the announcements there and what you're seeing at the show sure no I appreciate that it was a first time for for Dell technologies it was kind of our coming-out party if you all went into the cloud native realm we've got a tremendous amount of momentum especially OCR on kubernetes between what we've done in the data protection space with our power protect software for kubernetes we've done in our storage room in the work that we've done around container storage interfaces so a lot of that was coming out in introducing that to the Keuka and cognitive count attendees I think it was a really good timing though yeah Elson we've been watching you know the role the developers the discussion of DevOps of course is central what's happening not only at cube con but many of the cloud shows there I know at VMworld you know you see what's happening on with the VMware code team so explain how a kind of the the Dell technologies cloud partnership with vm or how about all that all pulls together for activities that the your organization's doing with that within the DevOps well you know you you know they were right they're right it's all about DevOps it's about the developers it's about the the new world of bringing cloud native applications and driving them into the production environment I think that you know we heard that at vmworld with pack L singer and we're his his pillars of you know build run protect connect are key aspects so you know if you look at that man component protect falls right into that area right because with the growth of data as we're seeing it today the need to manage that in the cloud native realm becomes even more prevalent and important you know we've seen DevOps mature over the last couple years where you see you know we had 8,000 people in Seattle right now we had 12,500 of your best friends and just gonna go out right I'm sure you saw that yeah absolutely huge growth there and I'm glad you brought up to protect thing because when I think about developers we want to reduce the friction for developers to be able to build their apps you think about DevOps is you know keeping agility going but you know where is the data and how do I make sure that you know we know when we go to a cloud world we still need to things about security we still need to think about data management and data protection there so explain for audience how that protect piece fits into the DevOps world well you know for first we should clarify a little bit right because like over the last two years everything's been about security within containers right and that's great because you're protecting the applications and people are worried about about penetration there and and it's been fantastic and I think that today specifically around the aspect of securing the application and now securing the infrastructure is key you know storage has become a very very relevant topic whether it's like persistent volumes taking center stage right when it comes to claim a vApps movie into production because it's about protecting those mission critical workloads and as you just stated you have your applications but at the end of the day your data right is really at the capital right and that's what you really need to focus on it becomes greater and greater importance when you have that holistic discussion about DevOps right and so now we have the aspect of the kubernetes administrator meets the IT administrator all right and having to be able to protect through this application transformation that's being driven by cloud native complexity and that you know tradition was disaggregated from the infrastructure but now as you mature and you look at those production and mission-critical environments you really have to pay attention to how am I going to protect my data the edge to core to cloud and in that cloud native world yeah definitely is one of those areas we found at the conference for many it's a steep learning curve to try to understand you know kubernetes all these cloud native architectures if you come in there with the traditional infrastructure role I was actually something we were discussed more a couple of years ago was they've some of the basic blocking and tackling of networking and storage inside of a container environment but now a lot of discussion is around that application development and therefore we need to make sure that we're having not only the app dev but the infrastructure team all understanding how everything goes together and you know protection of course a critical piece there oh absolutely and and you know if we look at all the different projects that are underway under C and C F I mean it's fantastic right I mean there's so much momentum everyone's now also looking at that infrastructure right I mean last year was all about the surface mesh right so I think that we're at that inflection point and now it's going to be a lot about the storage and protecting that storage if you look at Project Valero right so project Valero wasn't as an open source project under C and C F right being driven by the work that was done by the the you know the the the active form enormous hefty oh right so I got Joe Bereta right you got Greg Milwaukee and the work that they done in the starter house arc well now WMC in specificity of the data protection team is working and contributing hand in hand with the vmware team on velaro and i think you'll see that resonate through the future of tansu and pacific as we go forward great let's connect the dots now between what we're doing is the CMC F cube con show and now we've got AWS reinvented coming up so Amazon might now let us use the word multi-cloud that that context there but absolutely that was the conversation at many of the other shows this year is you know hybrid cloud multi-cloud how customers get their arms around all these environments so you know help us understand how this story that we were just talking about for cloud native environment fits into the broader kind of public cloud discussion oh absolutely so you I think one of the key aspects to that is around consistency right so being able from a data protection perspective be able to protect all that valuable data that you have whether it's in premises where it's in cloud with its multi cloud or hybrid and you want to be able to protect that holistically using the same capability you have from your premises base into or out of or within cloud all right so I want to be able to within AWS be able to protect my data from region to region right so we've got a great offering for VMware cloud on AWS it allows you to protect into and within the cloud itself so you can protect in and extend out to the cloud yeah definitely probably one of the most interesting partnerships I think the industry's been watching the last two years is you know VMware and AWS now you know the dominant virtualization you know in your data center environment and you know the leader in public clouds so looking forward to hearing some proof points at the conference and he gives a little bit of hint as to what we'll be seeing in hearing about at the show well I think you'll hear a lot about that consistency with regards to you know observability orchestration automation automation becomes so key that you take your workflows for data protection from premises to the cloud and having that consistency I think you'll also see some pretty pretty significant numbers coming forth with regards to how much data is being protected in in AWS ok definitely looking forward to that always love looking forward to the customers all right Nelson I want to give you the last word what else should we be looking for your team kind of end of 2019 it going into 2020 well you know I think it all starts with cloud and multi clock all right that's our core focus that's what we're driven to I think you'll see innovation especially in the cloud native space that we have I think you will see further innovation in in the in the cybersecurity in the cyber recovery space around data protection so I think those are really key elements that that you'll see more from yeah absolutely super important discussions around data around security and everything there Nelson thank you so much for joining us here in the cube sue thank you all right be sure to check out silicon angle for exclusive content leading up to and after AWS reinvent of course and check out the cube net if you're not at the table if you are at the show come to the center of the show floor at the Venetian inside the Sands Convention Center you can find myself Dave Volante John Ferrier and our whole team there for three days water wall coverage for our last big show of the year and I'm Stu minimun thank you for watching the Q

>> (enchanted music) >> Hi, I'm Peter Burris and welcome to another CUBE Conversation from our awesome Palo Alto Studios. We've got a great conversation today. We're going to be talking about flash memory, other types of memory, classes of applications, future of how computing is going to be made more valuable to people and how it's going to affect us all. And to do that we've got Scott Nelson who's the Senior Vice President and GM of the memory unit at Toshiba Memory America. And Doug Wong who's a member of the technical staff also at Toshiba Memory America. Gentlemen, welcome to the CUBE >> Thank you >> Here's where I want to start. That when you think about where we are today in computing and digital devices, etc., a lot of that has been made possible by new memory technologies, and let me explain what I mean. For a long, time storage was how we persisted data. We wrote transactions to data and we kept it there so we could go back and review it if we wanted to. But something happened in the last dozen years or so, it happened before then but it's really taken off, where we're using semi-conductor memory which allows us to think about how we're going to deliver data to different classes of devices, both the consumer and the enterprise. First off, what do you think about that and what's Toshiba's association with these semi-conductor memories been? Why don't we start with you. >> So, appreciate the observation and I think that you're spot on. So, roughly 35 years ago Toshiba had the vision of a non-volatile storage device. So, we brought to market, we invented NOR flash in 1984. And then later the market wanted something that was higher density, so we developed NAND flash technology, which was invented in 1987. So, that was kind of the genesis of this whole flash revolution that's really been disruptive to the industry as we see it today. >> So, added up, it didn't start off in large data centers. It started off in kind of almost unassuming devices associated with particular classes of file. What were they? >> So, it was very disruptive technology. So the first application for the flash technology was actually replacing audio tape and the phone answering machine. And then it evolved beyond that into replacing digital film. Kept going replacing cassette tapes and then if you look at today it enabled the thin and light that we see with the portability of the notebooks and the laptops. The mobility of content with our pictures, and our videos and our music. And then today, the smart phone, that wouldn't really be without the flash technology that's necessary that gives us all of the high density storage that we see. >> So, this suggests a pretty expansive role of semi-conductive related memory. Give us a little sense of where is the technology today? >> Well, the technology today is evolving. So, originally floating-gate flash was the primary type of flash that we created. It's called two-dimensional, cleaner, floating-gate flash. And that existed from the beginning all the way through maybe to 2015 or so. But, it was not possible to really shrink flash any further to increase the density. >> In the 2D form? >> In the 2D form, exactly. So, we to move to a 3D technology. Now Toshiba presented the world's first research papers on 3D flash back in 2007, but at that time it was not necessary to actually use 3D technology at that time. When it became difficult to increase the density of flash further that's when we actually moved to production of our 3D flash memory which we call BiCS flash. And BiCS stands for bit column stacked flash and that's our trade name for our 3D memory. >> So, we're now in 3D memory technology because we're creating more data and the applications are demanding more data, both for customer experience and new classes of application. So, when we think about those applications Toshiba used to have to go to people and tell them how they could use this technology and now you've got an enormous number of designers coming to you. Doug, what are some of the applications that you're anticipating hearing about that's driving the demand for these technologies? >> Well, beyond the existing applications, such as personal information appliances like laptops and portables, and also in data centers which is actually a large part of our business as well. We also see emerging technologies as becoming eventual large users of flash memory. Things like autonomous vehicles or augmented or virtual reality. Or even the emerging IOT infrastructure and that's necessary to support all these portable devices. So these are devices that currently aren't using large amounts of flash, but are going to be in the future. Especially as the flash memory gets more dense, and less expensive. >> So there's an enormous range of applications on the horizon. Going to drive greater demand for flash, but there's some business challenges of achieving that demand. We've seen periodic challenges of supply, price volatility. Scott, when we think about Toshiba as a leader in sustaining a kind of good flow of technology into these applications, what is Toshiba doing to continue to satisfy customer demand, sustain that leadership in this flash marketplace? >> So, first off as Doug had mentioned the floating-gate technology has reached its ability to scale in a meaningful way. And so the other part of that also, is the limitation on the dye density so the market demand for these applications are asking for a higher density, higher performance, lower latency type of applications. And so because floating-gate has reached the end of its usefulness in terms of being able to scale, that brought about the 3D. And so the 3D, that gives us our higher density and then along with the performance it enables these applications. So, from Toshiba's point, we are seeing that migration that is happening today. So, the floating-gate is migrating over to the 3D. It's not to say that floating-gate demand will go away. There's a lot of applications that require the lower density. But certainly the higher density where you need a dye level 256 512 giga bit even up to terabit of data. That's where the 3D's go into play. Second to that really goes into the cap back. So, obviously that requires a significant amount of cap backs not only on the development but also in terms of capacity. And that, of course, is very important to our customers and to the industry as a whole for the assurance of supply. >> So, we're looking so Toshiba's value to the marketplace is both in creating these new technologies, filling out a product line, but also stepping up and establishing the capacity through significant capital investments in a lot of places around the globe to ensure that the supply is there for the future. >> Exactly right. You know, Toshiba is the most experienced flash vendor out there and so we led the industry in terms of the floating-gate technology and we are technology leaders; industry's migrating into the 3D. And so, with that, we continue with a significant capital investment to maintain our presence in the industry as a leader. >> So, when we think about leadership, we think about leadership both in consumer markets, because volume is crucial to sustaining these investments, generating returns, but I also want to spend just a second talking about the enterprise as well. What types of enterprise relationships do you guys envision? And what types of applications do you think are going to be made possible by the continued exploitation of flash in some of these big applications that we're building? Doug, what do you think? >> Well, I think that new types of flash will be necessary for new, emerging applications such as AI or instant recognition of images. So, we are working on next generation flash technology. So, historically flash was designed for lowest cost per bit. So that's how flash began to take over the market for storage from hard drives. But there are a class of applications that do require very low latencies. In other words, they want faster performance. So we are working on a new flash technology that actually optimizes performance over cost. And that is actually a new change to the flash memory landscape. And as you alluded to earlier there's a lot of differentiation in flash now to address specific market segments. So that's what we are working on, actually. Now, generically, these new non-volatile memory technologies are called storage class memories. And they include things like optimized flash or potentially face change memories resistive memories. But all these memories, even though they're slower than say the volatile memories such as D-ram and S-ram they are, number one they're non-volatiles which means they can learn and they can store data for the future. So we believe that this class of memory is going to become more important in the future to address things like learning systems and AI. >> Because you can't learn what you can't remember. >> Exactly. >> I heard somebody say that once. In fact, I've got to give credit. That came straight from Doug. So, if we think about looking forward the challenges that we face ultimately is have the capital structure necessary to build these things. The right relationships with the designers necessary to provide guidance and suggest about the new cost of applications, and the ability to consistently deliver into this. Especially for some of these new applications as we look forward. Do you guys anticipate that there will be in the next few years, particular moments or particular application forms that are going to just kick a lot of or further kick some of the new designs, some of the new technologies into higher gear? Is there something autonomous vehicles or something that's just going to catalyze a whole new way of thinking about the role that memory plays in computing and in devices? >> Well, I think that building off of a lot of the applications that are utilizing NAND technology that we're going to see now we have the enterprise, we have the data center that's really starting to take off to adopt the value proposition of NAND. And as Doug had mentioned when we get into the autonomous vehicle we get into AI or we get into VR a lot of applications to come will be utilizing the high-density, low-latency that the flash offers for storage. >> Excellent. Gentlemen, thanks very much for being on the CUBE. Great conversation about Toshiba's role in semi-conductor memory, flash memory, and future leadership as well. >> Thank you, Peter. >> Scott Nelson is the Senior Vice President and GM of the memory unit at Toshiba Memory America. Doug Wong is a member of the tactical staff at Toshiba Memory America. I'm Peter Burris. Thanks once again for watching the CUBE. (enchanted music)

>> Narrator: Live from Nashville, Tennessee, it's theCUBE covering COMMVAULT GO 2018 brought to you by COMMVAULT. >> Welcome back to Nashville, Tennessee. This is COMMVAULT GO and you're watching theCUBE. I'm Stu Miniman with my co-host Keith Townsend. Happy to welcome to the program Lauren Nelson who's a principal analyst with Forrester. Thank you so much for joining us. >> My pleasure! >> Alright, our first time doing theCUBE in Nashville. COMMVAULT goes third year at the show. Have you been to the COMMVAULT shows in the past? >> I have. Not last year but the year prior, first year. >> So you skipped a year. From two years ago, just from my own knowledge COMMVAULT's changed quite a bit. I mean, they had some pressures on them. They've been going through a lot of pricing models. What's your take on what COMMVAULT's been doing in the last couple years? >> Yeah, a lot of focus on the show on pricing model changes, packages changes, emphasis on selling through partners and how to try and foster that in the actual village space. So, how do you bring partners into conversations early on and in a smoother way and just looking around, you've got white boards on tables, you've got spaces very changed in terms of collaborative space right in the middle of the booths rather than around the perimeter. So, very very interesting layout. >> Alright, so Lauren, your research, one of the things that you focus on is cloud and cloud migrations which is, of course, one of the key messages COMMVAULT talks about. When I talk to users, the multi-cloud strategy that they're figuring out, as I say, the ink's still drying and it's one of those things that's revisited quite often. What are you hearing from customers? What's your research showing of the state of cloud and how cloud migration fits in to it? >> Yeah, so you've got two really big topics right now on the cloud space. You've got the migration side and you've got the multi-cloud side and they are the conversations going on right now. First of all, you've got migration which is not SAS migration but migrating an app to an infrastructure as a service environment and that is something that is difficult. And a lot of companies that are talking about cloud migration don't always recognize that. They sum it up to SAS migration and often find that they are going to have higher performance when they move. They don't look at the challenges or the architectural changes from moving to vertical scaling to horizontal scaling and it's this conversation where, well, if we hear one company doing it, then maybe that should be our entire cloud strategy, And for a number of organizations, it is. Especially if you look at small mid-size organizations, they're looking at cloud migration as a data center replacement method. If you look at the average enterprise, that's not the typical story unless they're in a unique moment of change. Typically, what they're looking at is a few apps typically driven by location or app specific support that they're planning to get from their service provider of choice and they're moving this app for specific reasons. If you look holistically at our latest stats, survey taken in August, shows that 69 percent of enterprises in North America and Europe are actually migrating workloads to the cloud. If you then ask about how, they're methods are all over the map, the number of total apps they're moving is far less than what we're made to believe, in some of the more sensationalized or exaggerated stories or kind of unique use cases. When you look at the other side of that, you've got the multi-cloud story which is, all of a sudden with this migration topic, you get organizations questioning, "Should I be multi-cloud? "Am I doing this wrong? Am I adding needless complexity?" And we saw a bit of a drop in 2016, after GE presented at Amazon Re: Event, on associating with the term multi-cloud, that that was their specific strategy. And we've actually seen that come back in spades. In fact, we've seen a number of organizations actually say they are multi-cloud and then they have various definitions of what multi-cloud means for them. Multi-cloud is either public and private, private and some public cloud usage, multiple public, non cloud plus cloud. It's all over the map. And so, I think organizations are starting to take a step back, starting to think about, "How does my cloud strategy map to my larger organizational values?" Should seem obvious that should've been done that way from the start but for a lot of organizations it's a unique step that they're doing this year of being more pragmatic on how they should approach cloud. Not trying to force fit deployment models and look at the real opportunities that are there. And that includes all of these things. It includes private cloud and your on-prime data center for using it for specific workloads, for use cases that it just does not make sense to have that much change at once or to force the economics. The other side, you do see cloud migration. That is very much a real thing. It is not the most exciting use case to cloud. You're moving an app that you're not changing at all in terms of customer experience to an environment that is not meant to handle it that may suffer from performance. So it tends to be the less sexy side of the cloud strategies that we see today but very much apart. And it is the use of infrastructures of service for just that cheap infrastructure rather than some of it's more compelling past services. So it's an interesting time! All of those conversations are emerging, in fact, I can't tell you how many research reports we have on queue on these very topics around just the one-on-one level which I think is what we've primarily covered. Just the grazing the surface versus some of the latest conversations around, how do we have cloud neutral pass services that are obstructed from the underlying platform. Who will deliver those? What technologies will be standardized beneath that? How will we leverage kubernetes and the many different types. So, It's an interesting thing that I'm pretty passionate about as you can see. >> Well Lauren, let's talk about that step to when I'm out talking to customers about multi-cloud. One, multi-cloud or even cloud migration, that you're absolutely right, I see the same thing. They need to answer the why question like, "why are we even considering that?" And for multi-cloud I think customers are starting to look back and really wonder, "do I need that complexity?" "Is it really worth the effort?" But the second thing that I think customers underestimate the complexity of is having a data strategy unique to cloud versus their data strategy on PRIM. Are you seeing the same, that customers are realizing, wow, that I can't just take wholesale of my data strategy, my own PRIM data strategy and take that to the cloud. >> Yup, and it's the classic analyst response is, it depends. There's some organizations that are literally creating two different data hubs where they're having different access levels and different apps that you're allowed to connect to based on what classification is for that particular data. It's this theme of zero trust model of, how do you secure from the data out? And simply put, some data sets are more valuable than others. Some have security requirements that are far more expensive to meet and when you try and problem solve for this, some people try and think, well, is the easiest way just to completely segment the two, and be able to have control of how you access these two, Or, is it more complex than that? Do we need different databases that we're leveraging? Do we need to look at migrating some days or replicating some data to avoid data out fees from some of the major service providers? Do we leverage a third party like a collocation provider that can provide some ease for us in terms of movement of data? So, I think that's one of the big topics in the upcoming year is if I'm going to to multi-cloud, if I'm going to take on that effort, why am I doing that? You know, why am I taking on this as an important app? Is it because of fear of lock-in and flexibility long term? And if I do that, what are the implications in terms of data both security wise and cost wise. And a lot of organizations' challenge is, you know, you write out your cloud security, or your actual cloud strategy map and you don't really understand the proof points or where that strategy breaks until you start getting in to the details, testing out and trying to do this to try and figure out what are the actual costs of this scenario. So it's a challenging problem but it's one that I think organizations are going to be facing for the next few years. >> Yeah, it's interesting and I'm thinking through some of this. A lot of what we're talking about is infrastructure. And it's like, my private cloud, how do I modernize it? How do I simplify it? The reason we have infrastructure is to run my applications and the most important piece there is the data. How much does the cloud strategies that the data companies are working on look at data and how they are going to take advantage of data even more in the future? >> Yeah, it's a great question and there's so many different sides of this. There's one, you've got GDPR which is making them think about data in a whole new level just from a security and compliance perspective and how quickly they can react to requests that are put in by a particular citizen about their knowing what's on file for them as well as requesting it to be deleted and being held accountable for ability to complete that. On the other hand, you've got organizations that are trying to draw insights that are trying to change customer experience, design new products, market to their organizations more effectively leveraging data that they don't know where it is, they don't know how to use it or even how to start thinking about it together. They have a shortage of data scientists. They have a shortage of ... tools and solutions that can help them try and piece together this challenge. So, I would say companies have done very little of this so far. It is something that's on the road map. You get some organizations that are in the lead, that are testing the waters right now but if you look at the average organizations, they haven't even gotten to this. They're trying to use public cloud for very low end use cases at this point. >> Alright. So, I want to ask you when you talk about your research, of course, it depends. The customer by customer what they're having, but what differentiates a customer that can really start kind of moving up the stack, being more strategic, having IT really answering to the business and doing things with their data versus the laggards out there? Is there anything that you're finding in your research as to what separates those leaders from laggards? >> There's a few things and I'm not the first. A lot of vendors look for changes in executive boards. So, typically new folks coming in trying to change the way things are being done. Kind of, fresh innovative talent. That's a good flag that they're ready for it. Other organizations, there's the classic, they're threatened by others in their industry or believe they'll be threatened soon. For some organizations, it happens to be the right place, the right time. They have a leader they can trust that they believe will head this cloud strategy and that can tackle some of these challenges. For a lot of organizations, there's the desire, there is some action but often times the action is delayed. They face very stagnant cloud strategies. They face very stagnant data problems cause they're either missing cash, they're missing the ear of their executive board, or they have the wrong individuals that are trying to take the torch forward. So it takes a lot of critical self reflection as an organization that isn't easy. >> So let's talk a little about that (mumbles) within organizations. A lot of times, I'm seeing multi-cloud, cloud migration, cloud efforts being driven by, not just IT but infrastructure organizations within IT. Are you seeing successful efforts being driven from, I call that bottom up, versus whether if it's evens some VP or above, driving the effort from a business perspective? >> I think it varies. So, there some organizations where it's, our VPs are bringing a cloud first policy to our organization and then you have to figure out what that means to that particular executive. It may mean, we are going to go AWS completely all now! For a lot of organizations, it's a progress of, we're going to include all things that could be possibly labeled as cloud technologies, which, doesn't really describe much. If it does anything, it's to describe change is on the horizon. We are going to do things differently than we previously did. For a lot of companies, they have been tasked by their executives of, you need to tell me what to do for the cloud strategy and so they're trying to figure out, how do we problem solve for cloud from our perspective. I think that's been on of the biggest problems of the cloud industry for the last seven years, is this bottom up approach or top down approach with very little thinking about where's the value. So, bottom up, typically you get folks thinking about how to we have more efficient infrastructure, how do we modernize, how do we add in automation and build the best private cloud in the world? And often times over spending, often times delaying and having no self service access for developers for five years into their strategy. From that you see a lot of organizations trying to redesign their cloud strategies to deliver real business value up front early on and then look to see, how do they modernize, how do they mature, to help support and scale that effort, where as, top down approaches are very froofie, high level, shiny objects and so a lot of organizations that are trying to change that are trying to get individuals in the same room together, figure out, let's start from our business strategy and work our way in to how cloud serves that greater purpose. >> Lauren, when Forrester talks to customers, this space of data protection, secondary storage, whatever you call it, is pretty crowded. Are there specific things that, customers are calling out or that Forrester gives advices to how to differentiate in the market place or what they're looking for? And along those lines, what're customers looking for down the road to even expand more and get more value from vendors in the future? >> Yeah, I tend to focus on the cloudy questions. So, when I'm looking at data protection, I'm looking at it as a module of, how you manage multiple clouds. And when you look at the hyper cloud management space, it's a very busy space. It tends to focus on orchestration, template building, occasionally some compliance and policy, tags that can be applied, and then cost optimization. Very little has been done on the security side so far and very little has been done on data production side. So, when you look at, how do you differentiate, there's not a lot of players in this market yet. For as many security players as there are, it has not been aggressively tackled as heavily as the classic management providers have been attacking other management challenges. You look at the cloud costs optimization space, it's done very well. It's the first cloud challenge that organizations are trying to solve is, how much am I spending? Who's spending it? How do I integrate with my billing system? The next challenge is, how do I problem solve for data. They see that as a potentially huge cost escalator if they don't get this under control for many reasons, compliance, from data out costs. So, that's the next beach head. So, I would actually argue that there's not a lot that's been done specifically around cloud in this space yet. >> Lauren Nelson, we really appreciate you sharing your insight in customer views with us. >> My pleasure! Thank you. >> Alright, for Keith Townsend, I'm Stu Miniman, we'll be back with more coverage here from COMMVAULT GO, in Nashville, Tennessee, Thanks for Watching theCUBE.

>> Narrator: From San Francisco, it's theCUBE. Covering OSIsoft PI World 2018. Brought to you by OSIsoft. >> Hey welcome back everybody, Jeff Frick here at theCUBE. We're in downtown San Francisco at OSIsoft's conference called PI Word, they've been doing it for at least 15 years. I see the pins walking around the hallways. Our first time here and it's pretty interesting because we talked about marriage of IT and OT all the time, and kind of the industrial internet of things, these guys having been coming out of it from the OT space for over 15 years, almost 20 years or no, 40 years, 1980 right? >> About 40 years. >> A long time so they even had it for a long time and we're excited to be here, 3,000 people. And we're joined in our next segment by Chris Nelson, he's a VP of software development for OSIsoft. And Penny Gunterman, group lead of product marketing. So welcome. >> Welcome, glad to be here. >> Thank you. >> So how many of these shows have you guys been to? >> I've been to every one since 1996 except for two when my two daughters were born. >> Pretty good, I've only been to fewer than 10 so that's make me a young. >> Well you're just a rookie in this crowd. >> Still wet behind the ears. >> Yes so for the folks that aren't familiar, give us a little bit more detail 'cause you're at a really interesting space. You're pulling all this data off sensors. You know, we talk about this all the time as if it's kind of a new and interesting and evolving thing, but you guys have been at it for decades and decades. >> Yeah, it's really just been kind of the press and some new players have grabbed on to it, but we've been doing this for 30 years and, you know, our goal is to collect operational data wherever it exists, reliably and securely persist that and deliver it to whoever or whatever needs it. We don't pretend to know how our customers and users are going to use the data. We just take care of that data flow and we really light them up by giving them their data, they can use it to drive outcomes for their companies and they are our data heroes. >> What's interesting, too, is a lot of times the sensor data gets tied back to big data and fast data and Hadoop and kind of all these technologies that are evolving around that type of data. You guys have been doing it long before there was you know, Hadoop out in the public sector, Flink or Spark or kind of all these new technologies and I think it's interesting because you're showing that you don't have to have big data for regular people to see trends and get value and get some real business benefits. >> Oh yeah, absolutely. I mean, really, when you think about it, it's like driving your car. In order to operate that car, you want to be able to get that information in, you need to make sense of it, and then you move forward with it, right? Now after the fact, you're going to do some analysis, maybe you want some other things, but in your day-to-day operations, when you're making sure that things are running, you want that dashboard, you want that real time visibility, and we've got folks out there that if they see a trend, they could tell you exactly what's wrong, they can tell you exactly where to pinpoint those issues. So what's interesting is seeing, finally, this emphasis on data and people kind of catching up, seeing what they could do, but now you take that natural intelligence that people have always had, pushing that into some of those advanced tools, doing what they couldn't do before, and that's what's really exciting. >> So are you integrating now more with some of the newer tools that are hitting the marketplace, as opposed to just kind of, I assume you're way tied into ERP and some of those type systems. >> It's really cool because we're in this technology and market change around digital transformation is the buzz word, but we can take everything that we've done in the past and then overlay some of these new technologies that are coming from, you know, the giants of Google and Amazon. We can take advantage of a lot of those tools with the data we've collected for 30 years, that really drive outcomes. I think the important part of the outcomes is we're really reducing a lot of the resources that are scarce in the world. You know, water, power, carbon footprint. That are the outcomes that, you know, people are trying to reduce with the data and it's really impactful in the world today. >> And it's funny too, you know, start ups often begin because somebody sees inefficiency, whether it's car ownership then you have Uber or it's, you know, empty rooms in a city like San Francisco and you have Airbnb. But you guys and your customers specifically, there's all types of inefficiencies still in old line industries, old line systems, old line infrastructure, that you're helping ring out all kinds of efficiency out of things that some people aren't paying attention, probably thought was already done. But there's still a lot of opportunity. >> Oh absolutely, you see it all the time especially with the older industries. They've been operating for hundreds, some even older, number of years, and so when you think about normalizing failure, a lot of them have just kind of, well, assumed that well, we're always going to have 30% loss or well, we're always going to have a 10% inefficiency. But I think we're really challenging some of that paradigm by being able to look at information and seeing well wait a minute, no we don't have to have that 30% lead, wait no, we can improve our goal extraction efficiency just with this simple tweak in the process. So I think what's exciting with conferences like these is you realize that you can challenge what used to be possible with these new tools, using that tribal knowledge that people have always had. >> And I think what, again hitting on what Penny said, the power of this conference is, especially today, we have industry tracks. So all colleagues across an industry will get together, share their success stories, and that will help those success stories get out to other customers, really helping the overall industry. So today is critical as that industry day where they all get together, share their expertise, and the other one is I've always found it interesting, I grew up in life sciences, you know, pharmaceuticals, going to other industries, seeing what they're focused on, you can learn from them and bring it back to your industry. So the idea doesn't have to generate in your industry. It could generate somewhere else, and you can bring it back and that's what this conference really helps our customers do, share those success stories. >> I can't help but think of a bourbon or scotch commercial where they talk about you know, the angels share. When they take it out of the barrel after so many years, there's some percentage, which is kind of cute and quaint for a commercial, not necessarily if it's a big municipal water district. Somebody said in the hall, some of these big ones are losing as much as 50% of the water leaks out of the system. That's crazy. So this is the type of tool, what, or how, do they use it? So they're just, you're looking for inconsistencies in the data, is it just kind of classic pattern recognition? How are you helping the people find these inefficiencies so they can bring new solutions? >> Yeah, it's a little bit of both. Some of it is just surfacing that data. It's almost like I said, if you never even knew on your car dashboard that your oil was looking low, you wouldn't even know to go in and service it. So level one is just surfacing that information. I would say that's going from zero to 50. But if you go from 50 to 100, you talked about whiskey, I'm a beer fan, so we've got customers like Deschutes, who were going through and they were trying to figure out when the fermentation was done. They just have to go around and pipette when it was completed. The problem was you get your rounds maybe once an hour maybe fewer, less than that and by the time you get back around to that batch, you could have lost long passed that fermentation point where that beer needed moved on to the next process. It could mean either bad beer or it could mean that you reduce the amount of throughput that you could have. So they've used the data that they were collecting from the PI system, trained their models, be able to predict when that fermentation was going to be complete, and know exactly when they should be moving over to their next batch. >> Right, and I'll share one from my knowledge that I worked on from pharmaceuticals where, just creating a new drug, there's lots of iterative processes that goes through that. We monitor that manufacturing process to give that data to the process engineers so as that iterative processes, they know exactly what they're building is according to how they filed to their regulatory companies. So that's all great and they use the PI system to do that and they've been doing that for 20 years. This one particular drug that this manufacturer was making, they wanted to go into a new market. And that new market was they had to provide enough yield product to the whole population. And they couldn't make enough. So then they took and applied Big Data Analytics and they found a process problem that they could optimize which allowed them to get enough product to go into this new population. So it's really, like Penny said, from zero to 100, just getting the data unlocked and providing it to these companies it's valuable right now. So we believe the PI system once you install it brings value to those customers and then you can overlay projects over time and really drive the value up over time. So like you said, a customer that's had PI for 30 years is still going through optimizations. They're still bringing value to their company through those optimization techniques. >> I'm curious, how many of these kind of opportunities for say the individual you just mentioned, did they know or there they just couldn't they just couldn't put a data point on it, they couldn't put their finger on it? Versus how many of them are oh my gosh I had no idea this complete green field opportunity for efficiency that we never even thought of. How does that kind of break down? >> I definitely think it's 50/50, it varies by customer. You'll see a lot of customers that start off with a very known problem. So let's say they know they've been having challenges with transformer failures, right? So they go in, they look at the data, they can find a signature, and they deploy it. But then the next group comes along and say oh hey wait I could use that data, too. I could use that to prevent parallel cycles so we could improve the efficiency of that conversion. And it becomes almost more this culture to say well wait a minute, if I could do that, actually you know we're collecting information from our security substations. We could compare logs of who's entering against who's supposed to be in there. So I say that first one tends to be very directed, but then it becomes contagious and people realize that, what else could I doing? >> And you really see it just spread through so at our conference last year in London, a water company deployed the PI system to basically manage how the water was flowing throughout their utility. Once they finished that project, the customer was so happy he goes, why am I not utilizing this to monitor my network? So a secondary project that he did not have funding for he deployed it to monitor his network the same way he's monitoring water flow throughout his complex and he was able to say wow, I love it as a network monitoring tool. It really speaks to the approach that we take which is this infrastructure approach. We focus on moving the data and marry that with our customers' creativity to use that data for things we never even thought they could do. So it's this infrastructure approach where we take care of the data flow and then marry our customers on top of that where they just light up that creativity. >> So speak a little bit about the opportunity and the challenge that now all this stuff's going to be connected. It's all going to be IP based. We're going to have PHI-g coming out over the next couple of years so the speed and the quantity of the data that's now available. So huge opportunity for you guys but obviously a huge change in the marketplace as well, where you've been dealing with, I assume, a whole lot of proprietary and you know, individual systems for all these sensors that weren't necessarily built to IP protocol. So great opportunity, got to be a little scary as well, I imagine. >> Oh absolutely and you see definitely industries that are on different parts of that spectrum. So let's say you think about shipping. When that ship is out at sea, they've got maybe satellite and that's it. But the people on shore still want to be able to monitor, right? So you have to get very diligent about what pieces of information you're going to send over while you're in that constraint of being out at sea. Now once you come into port, no problem, hook right up, and you can do that full dump and come back out. So I think what we're going to see in the next five, 10 years is a very deliberate selection of what we send and what we decide to move on with. >> I'll add on top of this is our CEO and founder, Patrick Kennedy, has very much kept us focused on this data infrastructure approach. And the reason why I bring that up is we're always looking several years out. In order to provide this robust infrastructure, we're constantly looking at the market and technology and trying to project where out customers are going to be so that we can provide them the tools. So right now, absolutely. We see lots of challenges, or maybe opportunities, coming into the market. >> Same coin, right? Same coin, different sides of the same coin. >> Yeah, as everybody connects, let's say cyber security has got to be forefront in everybody's mind, right? How do we secure all this data so that our customers can really trust that their IP is being protected? One, data ownership, right? So that's another one that's coming out is as everybody shares this data, right, sometimes companies buy companies. Who owns that data? So data ownership is going to be critical and these are the things that internally we are already trying to, you know, build solutions for because of our singular focus on this data infrastructure around the PI system. So it's really that approach of our job is to collect this data and share it with everybody. It's fantastic. Me and Penny often say, there's no better time to be in the operation space with all this new technology and also the disruption in a lot of the business models that these companies are going through, right? Deregulation, a lot of the things that are happening in business are directly related to a need for data and really driving value from that data. >> Well it's just so interesting, we cover a lot of big tech shows and everyone's so excited for the marriage of IT and OT and you know, we've covered GE. We've covered Ford, so we've covered some of the, more of the industrial side as well but it's just funny that you guys have been kind of silently doing your thing for years and years. But I would imagine the opportunities now to integrate with, I see the SAP, as a gold sponsor and some of the classic big IT companies love to get connected with you guys and have you feed all their analytic system and all this stuff they're working on as well. 'Cause it is a marriage of these two systems which is so important. >> Oh, absolutely and I mean you think about how dirty a lot of this sensor data is, right? It's coming raw, it's real time. There are no do-overs. There's communication gaps and so how do you prepare that, cleanse it, because I think a lot of times the operational environment, you think about dusty, dirty. It kind of matches the type of data, right? And you think of IP systems and they're nice, clean, temperature controlled server rooms so somehow, you're going from this really dusty, dirty data to something that needs to be able to be brought into it a very sanitized environment. So a lot of what we've been focusing on is around being able to clean that data and massage it, take the gaps out. That's where the PI integrators have worked out really well, I mean we have customers that have been able to get value out of these big data projects six months faster than what they would have done otherwise. And it's really then when the data scientists pick up. Picking up at a point that now they're doing the stuff you paid them to do, right? They're not cleaning, they're not doing the janitorial work, they're actually creating the models, training it, and helping drive forward. So I think it's an interesting dichotomy to see and I think IT folks are also starting to get excited because finally this dirty, dusty data is now becoming accessible to them and I've talked to a couple of folks that get really excited when they look at the PI system and they see how the PI system can help also reference all these other data sources they are dealing with. We can touch into ERP but we don't have to fully expose that. They look at the PI system as almost a data directory, that switchboard that allows people to come in, one-stop-shop, and get everything they need. For IT that means that they just have to manage that one point of entry, not the 10, 20 that they would otherwise be dealing with. >> Yeah, and if we look at it as let's put the customer at the front and center, right? They are trying to do something to drive value. We don't determine their partners or who they use or what technology they use, so we want to bring a rich infrastructure of partnerships to really go to the user, focus on the user, right? So whether or not that be SAP, Microsoft, Google, all these ones, whatever the customer wants to use, we want to light up. And that's really our partner strategy and it's again, us being the technology guy, I get excited because these partners are also bringing their expertise to the table. So some of the technology that they're working on we just love because we can apply it against the data. It really is this rich ecosphere where we're putting the customer at the center so they can drive a lot of this value. You can see my energy. >> Yeah, no it's a cool story and all the use cases, you know, are just fantastic. There's so, so many they're household names. They're doing really simple things in terms of being able to recognize the value you know reducing loss in the water system you know increasing efficiency in the gold output and it's all very discrete and easy to understand stuff. So exciting times and congratulations to you both. >> All right, thank you. >> Thank you. >> All right, so Chris and Penny, thanks for stopping by. I'm Jeff, you are watching theCUBE from OSIsoft in downtown San Francisco. Thanks for watching.

>> Announcer: Live from Las Vegas, it's the CUBE, covering AWS re:Invent 2017, presented by AWS, Intel, and our ecosystem of partners. >> Hey, welcome back to the CUBE. We are live on day one of AWS re:Invent 2017. This is their sixth event, our fifth time here with the CUBE. I'm Lisa Martin, along with Justin Warren, my co-host. There are upwards of 40,000 plus, I've heard even 50,000 people are here, incredible three day event. And we are excited to be joined by another guest from BMC, Daniel Nelson, AVP of Product Management, Security, Compliance and Automation, welcome to the CUBE. >> Thank you so much for having me, I'm excited to be here. >> We're excited to have you here. So one of the things that I'd love to understand is when you talk to customers who are in the enterprise, on this journey to cloud as you know, that term is used a lot, what are some of the biggest challenges that they face knowing they have no choice but to do this? What are some of the biggest challenges that they face that BMC can help to mitigate on this journey? >> Oh, I'd be happy, absolutely. So one of the things about us is that for the past twenty years, we've been helping large enterprises help keep their environment secure, fully automated, be able to have greater efficiencies within their data centers. And as our customers are transitioning to a multi-cloud world, everything that they had to do back at the data center, they still have to do in the public cloud, it still has to be compliant, it still has to be secure, it still has to be governed. And so what we help our customers do is to make that transformation and be able to bring together those two worlds so while they currently are looking as a goal to use AWS, use public cloud, use private cloud, they still have to manage their internal systems and be able to provide one platform to do that is what BMC's all about. >> Yeah, I've been a longtime user of BMC products, back in the day, you know Control-M and some of the things-- >> Still a great product, lots of people use it. >> Absolutely, it was a great product and we used it a lot. So I know that BMC has that rich history and experience of being able to automate things, particularly in scale, so how is that translating across into the world of cloud? 'Cause to me it actually seems like it's basically the same problem. >> Oh, and it is, absolutely. So what it used to be, scale was the measure of number of servers that you have. Now it's much more number of applications that you have, the number of developers you have, the number of configurations you have to keep in touch with, the number of policies you have to enforce, so the scale problem's exactly the same, just the physical mechanism of what's scaling has changed and that is an added complexity to it. >> Yeah, so given that level of similarity and what you've been able to translate from the inside world across into the cloud, what is it that's different? What is the thing that people are struggling with and the customers are really challenged by in this journey to cloud? >> Well, in one word it's speed. So everything that you had to do in the past was at a particular cadence. And so if you're releasing applications once a year, once every six months, even once a quarter, there was a certain amount of slack in the system where if something went wrong, you had time to adjust, you had time to keep up with it. Well now that you're down to hours, minutes, sometimes even seconds, pushing out code all the time, updating your applications all the time, you can't operate, it's beyond human scale and so that's where things like automation being able to tie back to your core systems, be able to have all that automated governance control really helps, you know, all of our customers. >> Speed is one of the things that AWS has done extremely well continuing to-- what? Last year I think it was 1,017 new features and services. This year it's over 1,100 already and you know, Andy Jassy has been very vocal about speed and customer focus is what's helping them. So with that focus on speed and accelerating pace of innovation, how is BMC alike AWS in getting what customers need faster than your competitors? >> You know, absolutely. And so what AWS does really well is providing the core preeminence that the underlying, you know, building blocks of what you need and allowing you to assemble those very quickly to have you realize your own vision and your own dreams. What we do very well is keeping some guardrails on those building blocks and making sure that, you know, we've seen it all over the place. One developer makes a mistake and suddenly, you've got a data breach. Uh, you know, one piece of code doesn't get updated the way it should be or you have a password in GitHub somewhere and now all of a sudden, you know, all your data's out there and you're on the front page of Wall Street Journal. What we help our customers do is to keep out of that news and into the news of satisfying their customers and going fast. So while AWS helps you build things really quickly, we help you do that in the right way, that keeps you safe, keeps you compliant, and keeps you you know, within the normal, corporate governance. >> So what's your favorite example of a customer doing that, where they had this issue and then they came to BMC and you were able to help them to actually solve that problem; what's a great example? >> Well we obviously do a lot of business with a lot of big banks and we have one of our customers, is a very large bank, was hesitant about the cloud, was experimenting with it, and they started with just five projects and within six months that five, those five projects had ballooned up to 65 projects, and all without really governance control oversight. And then WannaCry hit and our customer was so nervous, so scared about it, that their only response was, since they didn't know what their exposure was, they just shut 'em all down, they just pulled the plug, and says, "We're not gonna do anything." And so what we did is we came in and provided them the ability to do that, to revive those innovation products, to provide the ability to build quickly, but also know where you are, how to be safe, and can continue to update, you know, your compliance and security posture with new information as it comes in. So it gives them that safety factor that they can feel safe. One of my favorite examples and one of the best metaphors I've had is one of my customers from Savience said, "You know, Daniel, look I love to go fast, but the last thing I want to do is put my problems on roller skates, like that doesn't do any good." And I was like , "That's what we're here to do. We're here to provide you, you know, those bumper rails on the bowling alley so you can go fast." >> I do love that problems on roller skates idea. >> I'm gonna use that. >> Yeah, I was feeling that one. >> Go ahead, I use it all the time. >> So you know, we talk a lot about a lot of buzzwords, a lot of hot terms, right? Uh, multi-cloud. I'm curious about what BMC is doing in multi-cloud. How does an enterprise understand what multi-cloud is? What's hybrid cloud? How do you guys help sort of break down some of these buzzwords into actions for your customers so they can be fast and competitive? >> So for me, if I were to sound out what multi-cloud really means is that you're choosing the best technology at the best price point for what the need of the business is. And sometimes that means running of the data center. And there are a lot of things in the data center that run, you know, more cheaply, more efficiently, but at a much more cost effective basis than they ever will in the cloud. And those things belong in the data center. And I think over time, you'll see the data center loads will actually increase, as well. There's some things that you have to go very quickly, you can be experimental with it, you have to have the DevOps team attached to, and the public cloud is great for those things. And then even within the public cloud space, there are things that Azure does well, there's things that AWS does well, and individual enterprises, especially large enterprises, which is our constituency, need to be able to make those choices and be able to do that for the best underlying reason of their technology. What BMC then provides you is ability to say whether it's OnPrem, whether it's in Azure, whether it's AWS, wherever you wanna run that, you know, we can provide you the controls and the compliance and the governance that you can be safe regardless. You get the same policies in place regardless of where that individual technology's targeted. >> Yeah, absolutely. And when talking with large, particularly large customers as you've point out, you only have to buy one other company and all of a sudden, you're multi-cloud. You might've decided, "You know what, we're all in on AWS." A different company that you'd buy for business reasons may have decided, "You know what, I wanna have some Azure, I wanna have some Google Cloud." It's like kaboom, you buy them and now all of a sudden, IT has this multi-cloud issue and they need someone who can help them to manage that. And really, you wanna be able to manage that in the same way across all of the different environments and I can see that that's where BMC would be really strong. >> You know, you're exactly right. Give me one of the great things, like this is a great show, and there's so many vendors and there's so much great technology here, but if you talk to Gardner or Forester or ADC or 451, one of the main things they'll tell you is you've got to have not individual tools for every individual problem, you need to have a platform in place that provides you the breadth of coverage where you have the ability to be flexible across those technologies. And that's another thing that BMC is offering in the market. >> Yeah, so one of the challenges of building that platform, though, is that you've got all of these little different silos that tend to just sort of build up all by themselves. And then when you come and try like the central IT comes along and says, "No, thall shalt use the one true solution." How do you actually provide the right level of flexibility for individual solutions that can be tailored in need, but still provide that scalability and sameness across everything that gives you those efficiencies in scale? How does BMC help you manage that? >> Well that's one of BMC's historical strongest parts of the offering, is the breadth of content, being able to support, you know, in the data center all of the different operating systems, all of the different applications. We do the same thing now by us forwarding all the different microservices within AVDS, all of the different microservices within Azure, being able to then provide that breadth of content so that the developer, himself, can choose whatever and then from a central IT standpoint, you know you've got the policies in place to be able to make sure that they're safe. Another one of my favorite expression is that developers will argue with people but they won't argue with systems. And so if you then being able to incorporate that, the compliance and control into the DevOps pipeline, into the DNAP driven-approach, where a developer does something that's outside of those guidelines and they just get an immediate response back saying, "No, I'm sorry, that's not allowed." or you know, "There's an air message in law." they're like, "Okay, well I gotta go fix that." verus being on the phone or having to go through any of that process. Developers are very argumentative about that. So what we do is be able to take that corporate IT perspective and just be able to eject it programmatically across all the different dev teams. >> I think our question we wanna pivot on the developer role for a second, you know, AWS has done a great job of attracting a lot of awareness in the developer community for a long time now. They've never really had to advertise, because this awareness was so strong, very sticky. We've seen them this year, sort of advertising, which as a marketer kinda signaled to me, interesting. We know that their massive growth rate isn't predicated upon us, you know, startups alone. That the enterprise is also a major play for AWS and they need to get to now, the CEO, the corporate board. I'm just curious, is BMC seeing in like a customer, like a large bank or an insurance company for example, where are you seeing the C-Suite help influence product development? How influential is that higher tier of management now as this transition becomes an absolute business imperative? >> Well, it's interesting because you see not only the rise of the CIO as a digital transformer within the business, you also see the CEO being more and more involved with us. And you also have the rise of the CSO. So being able to inject security into this conversation, and so you've got a monopoly of different voices that are all happening at the board level and that there's board visibility in the center of these things as well. But the board now pays attention to, "How are we developing our applications? Are they safe? Are they secure? You know, is there an existential risk to our business by the way that we're conducting ourselves from an information technology standpoint?" So those conversations are obviously happening. You know, we see them happening all the time, it's been really great for our business, because we've been working with these companies for years and years and years to help them be safe and compliant, to keep their banking licenses in order, things of that nature, and now we're just extending that to the cloud, as well. So we definitely see it and honestly, it's one of the things that we feel like is a core competitive advantage for us, is we have those relationships in place today and have for decades. >> Yeah, do you see yourselves going into customers in sort of a partnering relationship with AWS, particularly for those enterprises? I can see that, I mean IT has been wanting a seat at this table for so, so long. It's like, "Well, you've got one now. It happened to come from security which is possibly not the best introduction ever." But now that they have their seat at the table, how are you finding to manage that conversation to influence board level, which is a far different conversation than what it would be when you're talking about technical things? And even from developer land, it's like, "API's and so on", that's not really a board level conversation or is it? >> Well AWS is one of our strategic partners and so it's very easy for us to go into customers together, and be able to tell that message of, "Go safe but be fast at the same time." And so we're much more of an and-world now than an or-world, you know, that we were in the past. And the ability to make trade-offs with somebody that we all kinda took for granted, but now we really don't have that ability anymore, like we have to be all things to all people and that forces a lot of innovation. And it forces a lot of the kind of the new things that you're seeing everyday, no matter of AWS and other vendors as well. It's really an exciting time to be in information technology. >> Never a dull moment. And yeah I wanted to kinda pivot on it, symbiosis. Like how much business do you drive for AWS, but also conversely, how much does AWS sorta push BMC to innovate at their pace? >> Right, so you know, just being a AWS partner pushes you. Because you're now along for the ride and wherever they go, whatever they're doing, you know, our customers are looking at us and saying, "When do you support that? And how are you gonna support that?" You know, we want to be easing into these things and so we've had to put on ourselves, a very strict SLA that as soon as AWS gets someone new, we have to support it with our very breviated time, 'cause that's what our customers have had it and that's great 'cause it enforces us to innovate, forces us to do things in new ways and be able to you know, actually have a lot of the technologies, a lot of the processes in place that our customers, themselves are trying to emulate. So that's been wonderful. In addition to that, if you look at you know, how we're pushing AWS, AWS is definitely you know, is already in the enterprise, there's a lot of enterprises that already used us but being able to think about things from an enterprise standpoint is different than a developer bottom-up standpoint and so we've always been a lot more holistic about understanding what are the needs of the business? And especially from a C-Suite communication perspective, like how do we articulate and how do we do that well? And that's part of what we bring to the relationship. >> You mentioned a lot of customers are banks and insurance companies, I'm curious about healthcare. There's sort of an anticipation that Andy Jassy might be announcing a broader partnership with Cerner, who has 25% market share in electronic health records. Healthcare being historically slower to adopt cloud, massive security challenges there. What are you guys seeing in the healthcare space? What are some of the primary concerns there that you're helping to mitigate? >> Well so if you talk about healthcare, the first thing that everybody will talk about, especially in the IT space is HIPAA, right? So it's you know, what am I doing with my private data? If you talk about it from an AMIA perspective, you know, it's GDPR, you know, what are we gonna do about private data, how do we keep it segregated? You know, how do we not only have those mechanisms in place, but how do we ensure that they're in place, be able to prove that they are in place? And when our auditors come to us, we can provide them all that data. And that's exactly what BMC provides. So we have out of the box content for HIPAA compliance, for SOX, for PCI, for anything that you want to do. And so we can just look at your systems or they're in the data center or in the cloud, tell you exactly how they need to be configured, and then also I'll remediate them for you. So we can take that next step and provide the automation in place for you, so that you can actually then just worry about running your business. So it's a really, really interesting vertical for us to go into 'cause of our history and 'cause of our background. >> Yeah, there's gonna be so much growth in that area. I mean, even from my part of the world, down in Australia. We've got our electronic health records is a big, big thing with the whole program of work that's involved in putting that in, being able to keep that data safe, but also useful. It's gonna be a big challenge and I can only see it getting larger. >> Oh right, absolutely. And it's important for us not to lose sight that the end person we're protecting is the consumer. The end person we're protecting is the individual who that's their data, like they own that, and so it's our job and our duty to do the best we can for our customers to protect that. And ultimately, that's the value. >> Last question for you, some of the things that have come out already in the last day and a half or so, from AWS on AI, what are you seeing in terms of customers' comprehension of machine learning and what the potential is for them to truly become data driven, leveraging advanced technologies like that? >> So we're definitely in the hype cycle with AI, right? I mean and I think we all kinda know that. I think when you talk about machine learning and basing and reasoning and-- it's all part of the cape on having the data in place to do the analysis on. And so just like we saw with the data, it's like, "Oh I want big data, but then now what do I do with it?" Now, we have AI machine learning for the people that do have large data sets, they can start to do some interesting analysis, they can start to do some interesting things. But you have to have the data first, before you start to apply the actual algorithms to it. 'Cause the algorithm, you know, just give it two data points, it's not gonna be very smart. Give it two trillion and it's gonna be able to do some really interesting things. >> So what can people see and learn and touch and feel at the BMC booth here? >> So just this week, we launched a new product called policy service, which is policy and compliance for public cloud and for DevOps pipelines, so we'd love to show anybody who wants to come by a demo of that, we're very excited about it. Also it ties back to our core automation and so if you have to do something also in the data center, we can bring those two worlds together for you. >> Excellent. Well Daniel Nelson, thank you so much for joining us. You're now in the CUBE alumni. >> Alright, that's exciting, I appreciate it. >> And I'm Lisa Martin, for my co-host Justin Warren, we are live from day one of our three day coverage at AWS re:Invent 2017, stick around, we'll be right back. (techno music)

(upbeat techno music) >> Announcer: Live from Las Vegas, it's the Cube. Covering VMworld 2017. Brought to you by VMware and its ecosystem partners. >> Welcome back to the Cube's continuing coverage of VMworld 2017. We are on day three, I'm Lisa Martin with the Cube. My cohost is John Troyer. We're very excited to be welcoming a Cube alumni, the CEO of Zadara storage Nelson Nahum, welcome back to the Cube, Nelson. >> Hi, thank you Lisa, how are you? >> Good, good to have you here. So Nelson, from your website you say scalable, elastic, cost-effective enterprise storage as a service. What is this, how does this make Zadara unique? >> Yeah, we are unique because of what we provide as a cluster solution that has all the capability of an enterprise storage. The security, performance insulation, fully management by the customer, block, file, object storage, all these as a service at the customer's size. So we are the only one company that can send all the equipment to the customer data center and the customer don't pay for anything, only for what they use. If we ship 200 drives and they start using ten, they pay for ten, and then they start using another ten, they will pay for 20. And if they want at some point to shrink, they shrink so it's totally elastic, in the sense of the agility for the provisioning, but at the same time, it's elastic in the price so the customer pays only for what they use. >> So this your fifth or sixth time at VMworld, with Zadara, you guys have a booth here on your website you're inviting people, hey, come by our booth. Talk to us about the Zadara storage cloud. What is the Zadara storage cloud. How do VMware users benefit from it? >> Yeah so VMware users, as you know, they use a lot of storage and enterprise storage capability, performance is very important, reliability is very important. This show is the most popular storage show, I would say, because of the need of enterprise storage in a VMware environment. But the reason why they are coming to our booth is because they want to move to a OpEx model even if they want to have this in their own data center we provide that. OpEx model is not only the financing of the equipment, it's also the fact that we manage the equipment remotely so customers don't need to use their own ingenious to manage and learn new equipment and so on and if there's a failure we cover with a team that can do everything remotely and immediately act on a issue. As well, one of the important things that happen with this new model is that customer is not locked with the technology. We as a service provider have the incentive to have the best technology the best price performance for the customer at any time. >> Well let me jump in there and let's talk about hardware for a second. The one thing about storage is it keeps advancing, it keeps getting cheaper. A lot of people out there still spinning discs, Flash and now NVMe. The generations are changing, the densities are getting higher, you have to invest now to get the next generation of performance out of it for your workloads. Talk a little bit about how you, as the service provider, who basically takes on the hardware costs, how are you looking at this, where we are on the curve, are there shortages in production, what is that doing to pricing? What is the risk model both for you and for the enterprise then in this equation as we move more to Flash? >> Yeah, really good question. So actually this is basically we take out from the customer not only the risk, the fact that very quickly the system becomes obsolete. So Flash is a good example, right there all the time, new innovations out on Flash, people predicted a few years ago that Flash would be extremely cheap, actually it's going up, the price. So people that buy today, an all-Flash array, and they calculate the loi over five to seven years they are overspending because at some point the Flash will go down and there is a new technology and higher capacity drives, et cetera, et cetera, that they are paying today for what this cost today. So our customers for example rely on us. So they say okay, Zadara is my storage expert and storage outsourcing and what we do is we provide because we are as a service and our incentive is to have the customer forever with us it's not a three years lease or forty year lease, it's a day to day, working together with the customer and being their provider forever so we have the incentive to provide the best technology at the right price to the customer. A good example I can tell you last year at our company summit we used to have Asustek, krpm drive, were the Enterprise drives, and people that have our systems with those drives may started two years ago, maybe started one year ago, maybe six months ago, or even three months ago, at some point we say, you know what, we will provide Flash at lower cost per gigabyte than spinning disc, and not only that, we will do the data migration online, the customer will not feel anything and at the end of the migration, the customer will have higher performance Flash and guess what, the next invoice will go down. And this is the kind of thing that we do that when you buy Capex you can not do. >> Well Nelson talk a little bit about maybe about to the customer is this consumption model, as a service, OpEx, that translates very well to the cloud cloud consumption models. You partner with all the major clouds, you could have data up there as well. You're customer base, do you see people adopting the hybrid model, where are we in that journey, to the public cloud, where are people keeping their eggs in which basket, these days? >> So yeah, as you know our system can be on premise and can be consumed from the major public cloud, Amazon, Azure, Google, we will announce next month another one-- >> There are more than just the three, yeah, yeah. >> There is another one. So they can consume as in both. I will say until two years ago the market was divided between the people that want to be all cloud or private cloud or inside a data center. Today a hundred percent of our customers have both really, except for the very small startup that may start in the cloud. Even after the ones getting bigger they have portable environments so I would say today the landscape is that people have both because they are seeing that it's better to be in the public cloud for bursting and for scalability. But they are seeing that it's better to be on premise or, it's no longer a private versus public, it's hybrid, I guess. >> And speaking of that, and you mentioned that Zadara partners with some big cloud providers, Amazon, Google, Azure, and then there's a to-be-announced-soon, what have the announcements that you've heard at VMworld this year, what have those meant to you with the VCF on AWS that was announced Monday, then we heard yesterday about the pivotal container service, with Google, what does that signify to you, in terms of the market trends, is it in line with what you expected? >> Yeah, so actually we were I guess the first company to partner with Amazon and cross-connect to Amazon, first storage company, okay, not the first company, the first storage company, and people would say, why you will do that it is better to sell the box and thing like that, so our product was built with cross-connectivity and integration with Amazon and Google and the Azure cloud immediately, right, so we are multi-tenant that is necessary for the cloud but the main thing that we do is that we do a really good job of separating the tenants between them that is unique and this is why Enterprisers prefer to use our storage even in the cloud. So customer in fact has, they get the drive, they get the controller, they get the networking even and can attach the cloud storage solution to their own active directory and cover all this capabilities that they would have on-premise. So we started in this way, and I think that now we see more and more even VMware and NetApp and those that they were classical on-premise that they were going in the same direction. It's actually very good for us. >> I'm a little interested in the container story, here, right, lot of talk about containers here at the show. On prem, even the hyper-converge story, talk about containers, you're running workloads and storage, containers super-useful for that, obviously in the cloud. What's the Zadara service with containers and how does that work? >> Yeah, definitely, so we launch it two years ago, a very unique service that allow a customer to run a Linux container inside the storage so inside the cloud storage solution they can run their own core. Why we did that, there were many customers that have millions of files, and they're reading and writing from a VM outside the storage, introduce latency, one of our customer have billion files and billion files if every time you cut one millisecond it translate into long period of time. The other thing that is interesting with our service is that storage, by definition, you can write or read data. You cannot get an occurrence notification hey, a file was changed. But if you run a application a Linux container inside of this storage, then we can provide this type of notification and say each time that a new file jpeg come to the file system, we will notify the Linux container and the customer can trigger many application or many service that will do something with the data. At zero latency, so zero latency, nothing to manage, they don't need external VM's, and also they get the capability of notifications from inside the file system. >> On the container front, I'm glad you brought that up John, are you seeing any industries in particular that are early adopters of this, or are coming to Zadara saying hey, we want to go the container direction, we want some advice here. You talked about a customer with billions of files, are you seeing any sort of industry specificity or is it more size of company where container technology's work would be leading. >> Yeah, most of our customer that use container they use for a specific service, they will not run out full-blown application in a container, they'll still run VM's but there are some services that benefit from zero latency and from having this notification that they can run in our side. So I think that it's not per industry, but more per type of service within the industry. That they need to do. >> Okay. Another thing that I saw on your website is that Zadara was chosen as a 2017 Red Herring top 100 North American winner. You join fellow winners Google, Skype, Twitter, innovation, it's a really not topic, so I'm sure that was pretty cool. Michael Dell talked about innovation yesterday and how important it is to innovate with customers. Tell us a little bit as we close up here about how Zadara is innovating together with your customers. >> Yeah, great question. So we started, I am an engineer from my background, so I like to innovate and have a new product so I'm also a firm believer that specially a company like us that is not the big companies, we need to really differentiate the product. We don't want to compete on the cheapest thing and we found out some for example. We need to provide really high value. So in order to provide high value we need to innovate. I believe that as the time goes on the more important it is for innovation and this is what you keep seeing with new stuff that's coming along because it's a non-stop journey and getting the best value for the customer. Nothing is more rewarding when you have an idea and people start developing that and suddenly customers are using it and they like that. This is the best reward that we can have. >> And it's probably rewarding for them I would imagine that they are able to be influencing to you. >> In our case because we are a smaller company we do summits every year, we invite customers a little bit lower among the VMworld but we invite customers and we have interaction with the customers and they can strongly influence the roadmap. We typically ask about what are the problems? What are the main issues and we try to innovate in this way. It's a good thing to have a really good roster of, we have worldwide customers today and everywhere in the world and when you put everybody together it's a really good experience. >> So as we wrap here, where can folks go I imagine the Zadara website to learn more in about a month of this new announcement that Zadara's going to be making. >> Yeah Zadarastorage.com is the place. >> Zadarastorage.com >> Yes. >> Excellent. >> And you know, we offer free trials and the nice thing about our service is that we're very easy to try you just go register and there's no moving parts it's just, we have a one week free trial and you like, you stay, you don't like you don't stay. >> Fantastic, well Nelson thanks so much for coming back to the Cube and sharing with us some of the great things that are going on. >> Thank you very much, it was a pleasure again. >> And we'll be watching to see what comes out in about a month's time Zadarastorage.com. >> Definitely. It will be very important. >> All right, you heard it here first. (guest laughing) All right from my cohost, John Troyer, for our guest, Nelson Nahum, I'm Lisa Martin, you're watching the Cube, live continuing coverage from VMworld 2017. Stick around we'll be right back. (techno music)

>> Narrator: Live from Boston, Massachusetts, it's theCUBE covering OpenStack Summit 2017. Brought to you by the OpenStack Foundation, Red Hat, and additional ecosystem support. (techno music) >> And we're back. I'm Stu Miniman joined by my co-host, John Troyer. Happy to welcome to the program two of the keynote speakers this morning, worked on some of the container activity, Kendall Nelson, who's a Upstream Developer Advocate with the OpenStack Foundation. >> Yep. >> And John Griffith, who's a Principal Engineer from NetApp, excuse me, through the SolidFire acquisition. Thank you so much both for joining. >> Kendall Nelson: Yeah. Thank you. >> John Griffith: Thanks for havin' us. >> Stu Miniman: So you see-- >> Yeah. >> When we have any slip-ups when we're live, we just run through it. >> Run through it. >> Kendall, you ever heard of something like that happening? >> Kendall Nelson: Yeah. Yeah. That might've happened this morning a little bit. (laughs) >> So, you know, let's start with the keynote this morning. I tell ya, we're pretty impressed with the demos. Sometimes the demo gods don't always live up to expectations. >> Kendall Nelson: Yeah. >> But maybe share with our audience just a little bit about kind of the goals, what you were looking to accomplish. >> Yeah. Sure. So basically what we set out to do was once the ironic nodes were spun up, we wanted to set up a standalone cinder service and use Docker Compose to do that so that we could do an example of creating a volume and then attaching it to a local instance and kind of showing the multiple backend capabilities of Cinder, so... >> Yeah, so the idea was to show how easy it is to deploy Cinder. Right? So and then plug that into that Kubernetes deployment using a flex volume plugin and-- >> Stu Miniman: Yeah. >> Voila. >> It was funny. I saw some comments on Twitter that were like, "Well, maybe we're showing Management that it's not, you know, a wizard that you just click, click, click-- >> John Griffith: Right. >> Kendall Nelson: Yeah. >> "And everything's done." There is some complexity here. You do want to have some people that know what they're doing 'cause things can break. >> Kendall Nelson: Yeah. >> I love that the container stuff was called ironic. The bare metal was ironic because-- >> Kendall Nelson: Yeah. >> Right. When you think OpenStack at first, it was like, "Oh. This is virtualized infrastructure." And therefore when containers first came out, it was like, "Wait. It's shifting. It's going away from virtualization." John, you've been on Cinder. You helped start Cinder. >> Right. >> So maybe you could give us a little bit about historical view as to where that came from and where it's goin'. Yeah. >> Yeah. It's kind of interesting, 'cause it... You're absolutely right. There was a point where, in the beginning, where virtualization was everything. Right? Ironic actually, I think it really started more of a means to an end to figure out a better way to deploy OpenStack. And then what happened was, as people started to realize, "Oh, hey. Wait." You know, "This whole bare metal thing and running these cloud services on bare metal and bare metal clouds, this is a really cool thing. There's a lot of merit here." So then it kind of grew and took on its own thing after that. So it's pretty cool. There's a lot of options, a lot of choices, a lot of different ways to run a cloud now, so... >> Kendall Nelson: Yeah. >> You want to comment on that Kendall, or... >> Oh, no. Just there are definitely tons of ways you can run a cloud and open infrastructure is really interesting and growing. >> That has been one thing that we've noticed here at the show. So my first summit, so it was really interesting to me as an outsider, right, trying to perceive the shape of OpenStack. Right? Here the message has actually been very clear. We're no longer having to have a one winner... You know, one-size-fits-all kind of cloud world. Like we had that fight a couple of years ago. It's clear there's going to be multiple clouds, multiple places, multiple form factors, and it was very nice people... An acknowledgement of the ecosystem, that there's a whole open source ecosystem of containers and of other open source projects that have grown up all around OpenStack, so... But I want to talk a little bit about the... And the fact that containers and Kubernetes and that app layer is actually... Doesn't concern itself with the infrastructure so much so actually is a great fit for sitting on top of or... And adjacent to OpenStack. Can you all talk a little bit about the perception here that you see with the end users and cloud builders that are here at the show and how are they starting to use containers. Do they understand the way these two things fit together? >> Yeah. I think that we had a lot of talks submitted that were focused on containers, and I was just standing outside the room trying to get into a Women of OpenStack event, and the number of people that came pouring out that were interested in the container stack was amazing. And I definitely think people are getting more into that and using it with OpenStack is a growing direction in the community. There are couple new projects that are growing that are containers-focused, like... One just came into the projects, OpenStack Helm. And that's a AT&T effort to use... I think it's Kubernetes with OpenStack. So yeah, tons. >> So yeah, it's interesting. I think the last couple of years there's been a huge uptick in the interest of containers, and not just in containers of course, but actually bringing those together with OpenStack and actually running containers on OpenStack as the infrastructure. 'Cause to your point, what everybody wants to see, basically, is commoditized, automated and generic infrastructure. Right? And OpenStack does a really good job of that. And as people start to kind of realize that OpenStack isn't as hard and scary as it used to be... You know, 'cause for a few years there it was pretty difficult and scary. It's gotten a lot better. So deployment, maintaining, stuff like that, it's not so bad, so it's actually a really good solution to build containers on. >> Well, in fact, I mean, OpenStack has that history, right? So you've been solving a lot of problems. Right now the container world, both on the docker side and Kubernetes as well, you're dealing with storage drivers-- >> John Griffith: Yeah. >> Networking overlays-- >> Right. >> Multi-tenancy security, all those things that previous generations of technology have had to solve. And in fact, I mean, you know, right now, I'd say storage and storage interfaces actually are one of the interesting challenges that docker and Kubernetes and all that level of containers and container orchestration and spacing... I mean, it seems like... Has OpenStack already solved, in some way, it's already solved some of these problems with things like Cinder? >> Abso... Yeah. >> John Troyer: And possibly is there an application to containers directly? >> Absolutely. I mean, I think the thing about all of this... And there's a number of us from the OpenStack community on the Cinder side as well as the networking side, too-- >> Yeah. >> Because that's another one of those problem spaces. That are actually taking active roles and participating in the Kubernetes communities and the docker communities to try and kind of help with solving the problems over on that side, right? And moving forward. The fact is is storage is, it's kind of boring, but it's hard. Everybody thinks-- >> John Troyer: It's not boring. >> Yeah. >> It's really awesomely hard. Yeah. >> Everybody thinks it's, "Oh, I'll just do my own." It's actually a hard thing to get right, and you learn a lot over the last seven years of OpenStack. >> Yeah. >> We've learned a lot in production, and I think there's a lot to be learned from what we've done and how things could be going forward with other projects and new technologies to kind of learn from those lessons and make 'em better, so... >> Yeah. >> In terms of multicloud, hybrid cloud world that we're seeing, right? What do you see as the role of OpenStack in that kind of a multicloud deployments now? >> OpenStack can be used in a lot of different ways. It can be on top of containers or in containers. You can orchestrate containers with OpenStack. That's like the... Depending on the use case, you can plug and play a lot of different parts of it. On all the projects, we're trying to move to standalone sort of services, so that you can use them more easily with other technologies. >> Well, and part of your demo this morning, you were pulling out of a containerized repo somehow. So is that kind of a path forward for the mainline OpenStack core? >> So personally, I think it would be a pretty cool way to go forward, right? It would make things a lot easier, a lot simpler. And kind of to your point about hybrid cloud, the thing that's interesting is people have been talking about hybrid cloud for a long time. What's most interesting these days though is containers and things like Kubernetes and stuff, they're actually making hybrid cloud something that's really feasible and possible, right? Because now, if I'm running on a cloud provider, whether it's OpenStack, Amazon, Google, DigitalOcean, it doesn't matter anymore, right? Because all of that stuff in my app is encapsulated in the container. So hybrid cloud might actually become a reality, right? The one thing that's missing still (John Troyer laughs) is data, right? (Kendall Nelson laughs) Data gravity and that whole thing. So if we can figure that out, we've actually got somethin', I think. >> Interesting comment. You know, hybrid cloud a reality. I mean, we know the public cloud here, it's real. >> Yeah. >> With the Kubernetes piece, doesn't that kind of pull together some... Really enable some of that hybrid strategy for OpenStack, which I felt like two or three years ago it was like, "No, no, no. Don't do public cloud. >> John Griffith: Yeah. >> "It's expensive and (laughter) hard or something. "And yeah, infrastructure's easy and free, right?" (laughter) Wait, no. I think I missed that somewhere. (laughter) But yeah, it feels like you're right at the space that enables some of those hybrid and multicloud capabilities. >> Well, and the thing that's interesting is if you look at things like Swarm and Kubernetes and stuff like that, right? One of the first things that they all build are cloud providers, whether OpenStack, AWS, they're all in there, right? So for Swarm, it's pretty awesome. I did a demo about a year ago of using Amazon and using OpenStack, right? And running the exact same workloads the exact same way with the exact same tools, all from Docker machine and Swarm. It was fantastic, and now you can do that with Kubernetes. I mean, now that's just... There's nothing impressive. It's just normal, right? (Kendall Nelson laughs) That's what you do. (laughs) >> I love the demos this morning because they actually were, they were CLI. They were command-line driven, right? >> Kendall Nelson: Yeah. >> I felt at some conferences, you see kind of wizards and GUIs and things like that, but here they-- >> Yeah. >> They blew up the terminal and you were typing. It looked like you were actually typing. >> Kendall Nelson: Oh, yeah. (laughter) >> John Griffith: She was. >> And I actually like the other demo that went on this morning too, where they... The interop demo, right? >> Mm-hmm. >> John Troyer: They spun up 15 different OpenStack clouds-- >> Yeah. >> From different providers on the fly, right there, and then hooked up a CockroachDB, a huge cluster with all of them, right? >> Kendall Nelson: Yeah. >> Can you maybe talk... I just described it, but can you maybe talk a little bit about... That seemed actually super cool and surprising that that would happen that... You could script all that that it could real-time on stage. >> Yeah. I don't know if you, like, noticed, but after our little flub-up (laughs) some of the people during the interop challenge, they would raise their hand like, "Oh, yeah. I'm ready." And then there were some people that didn't raise their hands. Like, I'm sure things went wrong (John Troyer laughs) and with other people, too. So it was kind of interesting to see that it's really happening. There are people succeeding and not quite gettin' there and it definitely is all on the fly, for sure. >> Well, we talked yesterday to CTO Red Hat, and he was talking same thing. No, it's simpler, but you're still making a complicated distributed computing system. >> Kendall Nelson: Oh, definitely. >> Right? There are a lot of... This is not a... There are a lot of moving parts here. >> Kendall Nelson: Yeah. >> Yeah. >> Well, it's funny, 'cause I've been around for a while, right? So I remember what it was like to actually build these things on your own. (laughs) Right? And this is way better, (laughter) so-- >> So it gets your seal of approval? We have reached a point of-- >> Yeah. >> Of usability and maintainability? >> Yeah, and it's just going to keep gettin' better, right? You know, like the interop challenge, the thing that's awesome there is, so they use Ansible, and they talk to 20 different clouds and-- >> Kendall Nelson: Yeah. >> And it works. I mean, it's awesome. It's great. >> Kendall Nelson: Yeah. >> So I guess I'm hearing containers didn't kill OpenStack, as a matter of fact, it might enable the next generation-- >> Kendall Nelson: Yeah. >> Of what's going on, so-- >> John Griffith: Yeah. >> How about serverless? When do we get to see that in here? I actually was lookin' real quick. There's a Functions as a Service session that somebody's doing, but any commentary as to where that fits into OpenStack? >> Go ahead. (laughs) >> So I'm kind of mixed on the serverless stuff, especially in a... In a public cloud, I get it, 'cause then I just call it somebody else's server, right? >> Stu Miniman: Yeah. >> In a private context, it's something that I haven't really quite wrapped my head around yet. I think it's going to happen. I mean, there's no doubt about it. >> Kendall Nelson: Yeah. >> I just don't know exactly what that looks like for me. I'm more interested right now in figuring out how to do awesome storage in things like Kubernetes and stuff like that, and then once we get past that, then I'll start thinking about serverless. >> Yeah. >> Yeah. >> 'Cause where I guess I see is... At like an IoT edge use case where I'm leveraging a container architecture that's serverless driven, that's where-- >> Yeah. >> It kind of fits, and sometimes that seems to be an extension of the public cloud, rather than... To the edge of the public cloud rather than the data center driven-- >> John Griffith: Yeah. >> But yeah. >> Well, that's kind of interesting, actually, because in that context, I do have some experience with some folks that are deploying that model now, and what they're doing is they're doing a mini OpenStack deployment on the edge-- >> Stu Miniman: Yep. >> And using Cinder and Instance and everything else, and then pushing, and as soon as they push that out to the public, they destroy what they had, and they start over, right? And so it's really... It's actually really interesting. And the economics, depending on the scale and everything else, you start adding it up, it's phenomenal, so... >> Well, you two are both plugged into the user community, the hands-on community. What's the mood of the community this year? Like I said, my first year, everybody seems engaged. I've just run in randomly to people that are spinning up their first clouds right now in 2017. So it seems like there's a lot of people here for the first time excited to get started. What do you think the mood of the user community is like? >> I think it's pretty good. I actually... So at the beginning of the week, I helped to run the OpenStack Upstream Institute, which is teaching people how to contribute to the Upstream Community. And there were a fair amount of users there. There are normally a lot of operators and then just a set of devs, and it seemed like there were a lot more operators and users looking that weren't originally interested in contributing Upstream that are now looking into those things. And at our... We had a presence at DockerCon, actually. We had a booth there, and there were a ton of users that were coming and talking to us, and like, "How can I use OpenStack with containers?" So it's, like, getting more interest with every day and growing rapidly, so... >> That's great. >> Yeah. >> All right. Well, want to thank both of you for joining us. I think this went flawless on the interview. (laughter) And yeah, thanks so much. >> Yeah. >> All these things happen... Live is forgiving, as we say on theCUBE and absolutely going forward. So thanks so much for joining us. >> John Griffith: Thank you. John and I will be back with more coverage here from the OpenStack Summit in Boston. You're watching theCUBE. (funky techno music)

>> And who we are today as a country, as a universe. >> Announcer: Congratulations, Reggie Jackson. You are Cube alumni. (gentle music) Live from Silicon Valley, it's theCube, covering Google Cloud Next '17. (techno music) >> Hi, and welcome back to theCube's coverage of Google Next 2017. We're at the heart of Silicon Valley in our great studio in Palo Alto. We've got a team of reporters and analysts up in San Francisco with the 10,000 people attending, really, Google's enterprise show. It's got Cloud, it's got G Suite. It's got a little bit of the devices. I'm happy to welcome back to the program, someone we've had on many times, Nelson Nahum, the CEO of Zadara Storage. Really a company that's in there, understanding this cloud transition from, kind of, how the enterprise gets in multicloud and all those pieces. Nelson, welcome back to the program. >> Thank you, sir, how are you? >> All right, it's good. So, you know, you and I last time we talked was at another cloud show, a little bigger cloud show, one that's been going on for a number of years, but let's start talking about Google. What's, you know, we think a lot of progress over the last couple of years. I mean, Diane Green definitely has put her stamp on this company. A ton of people that have been brought in, many of them, those of us in the industry, we know these people. We've seen them grow these businesses, understand how to talk to the enterprise, so, what's your take on the show so far and how Google's doing as a company? And we'll get into how they are as a partner soon, too. >> So this is our first Google Cloud show for us, for Zadara. We've been in, I think, since 2011, in Amazon Prime event, and we like it. This is going very well. We have a lot of conversations. We saw that there are many, many customers looking to have multicloud strategy. That actually works very well for us, because we can provide storage that can be accessible at the same time concurrently from Amazon, and from Google and Azure and others. So, yeah, it's a good trend. I think most of the people we found, they're either already on Amazon and looking to expand to Google or some on-premise to Google, and so on. >> Can you help unpack that multicloud a bit for us? So you know, maybe some of our audience might not know, you guys sit in some of those mega-datacenters, like the Equinixes of the world, and direct connect to the public cloud. >> Exactly, exactly. >> So, I've got, kind of, my storage being Zadara, and there actually is, you know, physical storage there, and then that, you know, plugs into the Cloud resources, of course. We know AWS's Direct Connect. Amazon has, you know, their equivalent, and Google has the same, so, can you have, you know, it's a single solution that, does it just get fibers to all three of them? Is there software that takes care of it? >> Yeah, actually- >> How does that work? >> Yeah, great question. So we sit in Equinix data centers with our cloud, and from there, in many cases, we use Equinix Cloud Exchange, that is, basically, like a networking inside Equinix, and can be connected to many different potential targets, so, currently we are cross-connected to Amazon, Google and Azure. So our customer, at first, can create a storage and mount with NFS or ZFS, or Block, and can mount the storage, especially if it is file storage, then you can share data. You can mount the same storage to virtual machines in Google, and to virtual machines in Amazon, and at the same time, they see the same files. >> Yeah, and what's the use case, why are they doing that? Is that for redundancy or certain features? You know, there was a certain cloud outage a week ago, were your customers riding through that, based on what they're doing? >> Yeah, so, the measured cloud outage that Amazon had last week, S3 ... it caused my people to rethink (laughs), I guess. Fortunately, for us, all our customers that sit in our storage, wasn't impacted, because they sit in our storage and they don't use, we don't use Amazon infrastructure, so they could continue- >> Stu: No S3 for your customers, right? >> Right, so we are doing the Block and the file storage for our customers. I think that, what is important here is not the outage, but what is important is people start recognizing that you need to have the data in two locations in order to be safe. >> Yeah, it's ... People that have done architecture and understand infrastructure is, you know, I need to be thoughtful as to how I architect things, so either I need to make sure I have the availability zones and the services and can take care of that, or perhaps even multicloud to be able to take care of that. >> So, multicloud and you're completely independent to each other. So, we have an array of many customers using us and Amazon and, again, because our storage can be cross-connected to multiple clouds, it's very easy to access from virtual machines in any cloud at the same time. So, people that are using that, it's either for a, kind of, these are tolerant solutions or more robust solutions. As well, in some cases for migration. Each cloud provider has the places or the attributes that it has. You can run applications better in that particular cloud, so, for example, in Microsoft Azure, anything that is related to Windows, they are the best, and, Oracle Cloud, if you run Oracle, probably is the best way to start. So, I will say that the multicloud is not only the disaster recovery type, but people want to use the best cloud for the particular application they have, and they have multiple applications, so use multiple clouds. >> I'm curious, do you get visibility, as to, you know, why are customers choosing Google? Are there, do you have customers that are using Google that aren't using the other public clouds, is it primarily your customers are using it as a secondary source, any data you've got or anecdotes would be helpful. >> So, we have two types of customers, the ones that are multicloud and the others that are going from on-premise to the cloud. As you know, we have an on-premise business, and we make it very easy, from on-premise, to move to the cloud. We just launched it, here in Google Cloud, a service called cloud iteratation, that basically, we allow a customer to move their entire infrastructure from on-premise to the cloud with zero, or minimal, downtime. So, we will ship all the storage to the on-premise facilities, the customer will pay per use, we will start doing replication to the cloud, or in some cases, if it is multiple petabytes, we will ship the equipment to the cloud, and, in the meantime, we can do replication and, at the end, we can switch and fail over, and the customer can continue from the cloud. >> Cloud hydration, >> Cloud hydration, yes. >> is that service. Does that support all the services, all the clouds? >> Yeah, so today, we are doing this for many customers, and the good use case is when a customer wants to move a lot of data to the cloud, but they don't want to have downtime. Because, Amazon Snowball and all these boxes, you need to copy the data, and then ship, and then restore. >> Stu: So, it's not a truck that takes three months >> Yeah, exactly. >> sitting on your location. >> This is what we do, we ship double amount of equipment to the customer, they start doing the copy, and then, half of it, we ship to the cloud. We connect to the cloud, and resume the connection, and, all the time, the customer continues to run, okay? And, at the last moment, they do the fail over. So, it's minimum downtime, even if you need to ship one petabyte of storage. >> Yeah. I'm curious, we've been going through such tremendous changes in the storage industry, do you guys sell, you know, is it the storage person, who do you sell to, and where is their mind at when they think about storage today? >> Yeah. Yeah, we sell storage, so the storage person is the one that's buying- >> Yeah, you know, a lot of people, if they're buying Google, or even if they're putting in AWS services, the storage person is, a lot of time, kind of shoved out of the mix, you're a little bit- >> Shoved out of the mix until they have a problem that they need to bring back the storage- >> Wait, are you saying that could be a problem? (laughs) >> So, what happen is that, and this is, I think, how the cloud started, is cloud storage is, "Ah, storage is just storage," until you start running real applications and you need the performance, and you need the reliability, and so on. So, this is why you need the storage guy to architect the solution, and this is where we, we come in and actually act as a really good outsourcing team of storage experts to the customer, and we help them with this transition from on-premise to the cloud and, in many cases, back and forth, if the customer wants to have a leg in the cloud, a leg on-premise, and move data easily back and forth. >> So, Google made a good push at the show, talking about building the ecosystem, how they want to work with partners. They had companies, like you know, PwC's all over the place, SAP, very strong partnership. How have you found it to work with Google? Any things you'd say to them as to how they can accelerate and move things faster to, you know, build up the ecosystem? >> Yeah, so far, our experience with Google was extremely good. The people are very dynamic, they have the Google dynamism that is very good, and, for us, it was really good to have a close relationship with the Google product managers and sales people, and so on, so we enjoy, have a really good relationship with Google Cloud. >> All right, well, Nelson, I want to give you the final word. You know, things you've learned this week, any cool customer conversation you've had, give us the final takeaway. >> Yeah, so, the ... I guess my summary is that, here in Google Cloud, we have a big advantage because we have ... NAS, NFS, ZFS, we've architected their integration and all the snapshot capabilities that enterprises need. And, you know that Google doesn't have a EFS type of functionality, and our functionality's actually higher than EFS. So, this is what we are talking to customers, here in Google Cloud, anybody that needs NFS and ZFS, and NAS and multicloud, and on-premise to the cloud, they talk to us and we are ready to go. >> All right, Nelson Nahum, really appreciate you coming to the studio here to share what's happening at the Google event. Be sure to check out wikibon.com for our cloud research and, of course, siliconangle.tv to see all the shows we're going to be at, as well as the replays from this and lots of other cloud infrastructure, IoT and big data shows. We'll be back with lots more coverage here, day two of two, covering Google Cloud. From the SiliconANGLE media studio in Palo Alto, you're watching The Cube. (techno music)