>> Announcer: Live from San Francisco, it's theCUBE, covering Spark Summit 2017. Brought to you by Databricks. >> Welcome back to theCUBE, and we are pleased to have our first guest here today. He is a customer of Databricks, and also doing some exciting things with Spark. So welcome Nathan Murith, our senior software development manager from Autodesk. Welcome, Nathan. >> Thank you. >> Are you happy to be here? >> Absolutely, very exciting. >> Is this your first Spark Summit? >> It is, absolutely, yep. First time here, first time at a Spark Summit. Lot of fun, lot of people, lot of energy. So I'm very happy to be here. >> Well, before we dive into some of the exciting things you're doing with Spark, maybe tell me what you were hoping to learn at this summit. >> I think, I'm really interested in learning what's coming next. You know, Autodesk is a technology company. We build products, we build software, and we're always looking at the future, figuring out what we can build and what we can leverage this amazing technology for, in our own tools that we then offer to our customers. >> And did you just attend the Keynote? >> Nathan: I did. >> And what did you think? What stood out to you? >> A lot of interesting things that I want to go home and try, basically, or take back to the office and try, 'cause a lot of these things are very applicable to what we're doing on a day to day basis. >> All right, we've also got George Gilbert on the show. And George, we're going to dig in a little bit. Maybe you have a question for Nathan about what he's doing with ... >> Yeah, Nathan, for those of us who are antediluvian, in other words, having been born before the big flood that floated Noah's Ark, what was, tell us about the types of that Autodesk builds, and how Spark helps people who use those tools. >> Sure, so Autodesk, as a company, we do a lot of different things. Autodesk primarily builds software for the design and make space in three or four different verticals and disciplines. One is media and entertainment. One is manufacturing. One is architecture, engineering, and construction. The group that I'm a part of, and the software that our team is responsible for building, is mainly around the cloud and mobile products for the architecture, engineering, and construction industry. Specifically, we have a suite of products that we brand BIM 360, that basically are tailored to the construction industry and various personas, and various steps, depending on where you are in the life cycle. The building a vertical structure, a bridge, a hospital, a stadium, and we provide software for those individuals. >> Can you tell us a little bit about that life cycle, and then the life cycle of a project like that, and where Spark can help a customer who's thinking 360, and not a particular product. >> Absolutely. So the life cycle is pretty complex, but it starts, usually, on the computer, like this, where you'll design your building, or your structure, or whatever it is. Heavy 3D graphics, that's where Autodesk, the company, started. From that point on, you do a number of coordination exercises with the various disciplines in a construction project, so your architecture, your structural, your plumbing, your heat/ventilation/air conditioning people, that are all specific disciplines. Then you actually go on site, and you start building this structure, whatever it may be. And when you build, the building process that typically could take multiple months to multiple years, depending on how large the structure is, is when people are going to start leveraging some of our tools even more. Typically some of the things that we see a lot of is, when you're managing a construction site, you will see on a daily basis hundreds, probably, if not thousands of construction issues. This sheet of glass here is broken. The drywall, you know, fell off. This beam is going through a wall, you name it. Construction site problems happen every day. >> You want to know if the beam's going through the wall. >> Exactly, absolutely. So typically that generates a lot of data, to the point where our customers can possibly feel overwhelmed by their own data, because there just is so many things that get generated in our system. >> So this actually sounds like where IT operations is the the discipline of I've got all this infrastructure, and I'm getting all these alerts when little things go wrong, and I don't know where the, necessarily, the root cause is, or what I should attack first. Is that sort of what you're... >> It's where we're going. Yeah, absolutely. Right now, we're tackling, we're starting, we're still early stages with kind of the machine learning, data science applications to the products that we do and build. But right now, what we're tackling, we're just trying to help our customers gain insights on their own data, so when you're swimming in this vast ocean of data, and you don't know where to start, or typically a construction site the size of a stadium, or a campus, or a huge office building, you don't know where to start, typically. >> So what does this vast pool of data look like, and how, specifically, are you using Spark to help make sense out of it, or prioritize what you should look at? >> So a lot of what we're doing now is, we're using image data, and text data, so what happens is your superintendents, when they walk around a construction site to figure out what's going on, what's broken, what's working, what should I focus on today? They will walk around with our mobile devices, or their mobile devices using our mobile software, and take pictures, and write descriptions of things that they see walking around the construction site. So they've generated hundreds of thousands of these construction issues, and where we're leveraging Spark, is to help build classification models on top of that data, be it image and textual data, to help bring to the surface, and bring to the top the things that are most critical to our customers. Typically, one example is on a construction site any problem that's related to water is usually considered a big problem. So if we can help among hundreds of thousands of issues that happen on construction site, kind of identify what. Hey, Mister Superintendent, you have these 10 problems that are related to water, whatever they may be, you should probably focus on those first. And that's where we're leveraging things like, you know, Spark technologies, machine learning, data science to build our products. >> And are you are you learning from all the customers who use the product, or in other words, do you need their data to get smarter, or is it rules that you're building? >> So right now we're working with a subset of our customers, through which we've gone into a number of agreements where they were okay with us working with them very closely to possibly use some of their data that was generated in our tools and systems to help them to help build our model. So we're absolutely not looking at the entire data set, per se. >> Did you see anything in the keynote, with the Structured Streaming that's now down to a millisecond, which is truly impressive for Spark, or in the deep learning that might simplify traditional machine learning. Did you see anything there that looks like it might have an impact on the type of app you could build? >> Very much so. So I think all the streaming applications are very relevant, because more and more on the construction sites, or more and more construction sites are being censored with, whether it's webcams, cameras, temperature detection, dust detection, air quality detection. >> George: IoT. >> Exactly, IoT all over the place, so when we can start collecting the data from those devices, streaming into our systems, we can more proactively notify, warn our customers, people on site, either security risk, any dangerous situation, or simply this is happening right now on your construction site, you might have to wake up because it's the middle of the night, and go check out what's going on. >> This is actually of great interest to us, because one of our themes now, where customers are telling us that they're trying to figure out what type of analytics, especially the machine learning training, would happen in the cloud, and what type of analytics would happen on site, or on customer premise. Are you doing the training up in the Autodesk cloud, and then, are you doing would the models be evaluating and executing sort of on site, close to where the data is being captured? >> So right now, again, early stages. So a lot of those questions we're still trying to figure out and understand what's best, what's best for our customers, what's, obviously the most secure, and things like that. A lot of the training that we're doing today is in the Autodesk cloud, so we use a lot of our cloud infrastructure where the data resides for our products, of course, to build and train our models, essentially. >> Well, we only have a couple of minutes left, but I wanted to dig in to, maybe some of the lessons learned. You said it was early days. So what are some things you could share with the community here on theCUBE that would help them? So maybe just getting started with Spark and some of the valuable lessons you've learned you'd want to share. >> I would say, I would say probably, get started now, is probably my piece of advice. I think we're all going in this direction, a lot of technology, and it's interesting 'cause even the construction space that I'm in is maybe not considered the highest tech, you know, discipline, which your industry, which makes sense and is obvious, but even in the construction space, we're going in the direction of using machine learning, data science, Spark-like technology. So I would say, get started now. That would be my piece of advice, 'cause there's a lot to learn, and things move really fast. >> Okay, so if you could complete this sentence: Spark has finally enabled Autodesk to blank. And start with Spark. I'm trying to get a sound bite out of you. >> Yeah, I think so. Spark has finally allowed Autodesk to build valuable customer-facing machine learning and data science products for our customers. >> And then the business outcomes for that, are being closely watched by executive sponsors, or how does that work? >> They are, but again, early days, right? Any large corporation like Autodesk, early days is a lot of moving parts so we're still feeling the waters of it now. >> Right, George, the last question goes to you. >> I guess, from what you've seen today, and anything you've heard about also coming down the roadmap, how might you expand the application that you are building in terms of thinking new possibilities, pushing the boundary? >> I think Internet of things is something that we're looking at, and I can very well foresee being part of this solution and ecosystem, as well as just allowing, I think, allowing our customers to push and pull their data into our systems to leverage our technologies, or to pull it back out, to plug it into their BI tools, or things like that. And I think that's something that, at least for our enterprise customers, will be very valuable. >> All right. Well, Nathan Murith from Autodesk, thank you so much for spending some time >> Absolutely, thank you. >> here on theCUBE. We're going to let you get back to the show. It looks like the show floor is open now, so get out and network with some of those 3,000 attendees. >> Perfect, thank you very much. >> All right, thank you so much. And thank you for watching. We'll be back soon with more guests, here on theCUBE. (techno music)

(bright upbeat music) >> Hello, everyone. Welcome to SiliconANGLE News breaking story here. Amazon Web Services expanding their relationship with Hugging Face, breaking news here on SiliconANGLE. I'm John Furrier, SiliconANGLE reporter, founder, and also co-host of theCUBE. And I have with me, Swami, from Amazon Web Services, vice president of database, analytics, machine learning with AWS. Swami, great to have you on for this breaking news segment on AWS's big news. Thanks for coming on and taking the time. >> Hey, John, pleasure to be here. >> You know- >> Looking forward to it. >> We've had many conversations on theCUBE over the years, we've watched Amazon really move fast into the large data modeling, SageMaker became a very smashing success, obviously you've been on this for a while. Now with ChatGPT OpenAI, a lot of buzz going mainstream, takes it from behind the curtain inside the ropes, if you will, in the industry to a mainstream. And so this is a big moment, I think, in the industry, I want to get your perspective, because your news with Hugging Face, I think is another tell sign that we're about to tip over into a new accelerated growth around making AI now application aware, application centric, more programmable, more API access. What's the big news about, with AWS Hugging Face, you know, what's going on with this announcement? >> Yeah. First of all, they're very excited to announce our expanded collaboration with Hugging Face, because with this partnership, our goal, as you all know, I mean, Hugging Face, I consider them like the GitHub for machine learning. And with this partnership, Hugging Face and AWS, we'll be able to democratize AI for a broad range of developers, not just specific deep AI startups. And now with this, we can accelerate the training, fine tuning and deployment of these large language models, and vision models from Hugging Face in the cloud. And the broader context, when you step back and see what customer problem we are trying to solve with this announcement, essentially if you see these foundational models, are used to now create like a huge number of applications, suggest like tech summarization, question answering, or search image generation, creative, other things. And these are all stuff we are seeing in the likes of these ChatGPT style applications. But there is a broad range of enterprise use cases that we don't even talk about. And it's because these kind of transformative, generative AI capabilities and models are not available to, I mean, millions of developers. And because either training these elements from scratch can be very expensive or time consuming and need deep expertise, or more importantly, they don't need these generic models, they need them to be fine tuned for the specific use cases. And one of the biggest complaints we hear is that these models, when they try to use it for real production use cases, they are incredibly expensive to train and incredibly expensive to run inference on, to use it at a production scale. So, and unlike web search style applications, where the margins can be really huge, here in production use cases and enterprises, you want efficiency at scale. That's where Hugging Face and AWS share our mission. And by integrating with Trainium and Inferentia, we're able to handle the cost efficient training and inference at scale, I'll deep dive on it. And by teaming up on the SageMaker front, now the time it takes to build these models and fine tune them is also coming down. So that's what makes this partnership very unique as well. So I'm very excited. >> I want to get into the time savings and the cost savings as well on the training and inference, it's a huge issue, but before we get into that, just how long have you guys been working with Hugging Face? I know there's a previous relationship, this is an expansion of that relationship, can you comment on what's different about what's happened before and then now? >> Yeah. So, Hugging Face, we have had a great relationship in the past few years as well, where they have actually made their models available to run on AWS, you know, fashion. Even in fact, their Bloom Project was something many of our customers even used. Bloom Project, for context, is their open source project which builds a GPT-3 style model. And now with this expanded collaboration, now Hugging Face selected AWS for that next generation office generative AI model, building on their highly successful Bloom Project as well. And the nice thing is, now, by direct integration with Trainium and Inferentia, where you get cost savings in a really significant way, now, for instance, Trn1 can provide up to 50% cost to train savings, and Inferentia can deliver up to 60% better costs, and four x more higher throughput than (indistinct). Now, these models, especially as they train that next generation generative AI models, it is going to be, not only more accessible to all the developers, who use it in open, so it'll be a lot cheaper as well. And that's what makes this moment really exciting, because we can't democratize AI unless we make it broadly accessible and cost efficient and easy to program and use as well. >> Yeah. >> So very exciting. >> I'll get into the SageMaker and CodeWhisperer angle in a second, but you hit on some good points there. One, accessibility, which is, I call the democratization, which is getting this in the hands of developers, and/or AI to develop, we'll get into that in a second. So, access to coding and Git reasoning is a whole nother wave. But the three things I know you've been working on, I want to put in the buckets here and comment, one, I know you've, over the years, been working on saving time to train, that's a big point, you mentioned some of those stats, also cost, 'cause now cost is an equation on, you know, bundling whether you're uncoupling with hardware and software, that's a big issue. Where do I find the GPUs? Where's the horsepower cost? And then also sustainability. You've mentioned that in the past, is there a sustainability angle here? Can you talk about those three things, time, cost, and sustainability? >> Certainly. So if you look at it from the AWS perspective, we have been supporting customers doing machine learning for the past years. Just for broader context, Amazon has been doing ML the past two decades right from the early days of ML powered recommendation to actually also supporting all kinds of generative AI applications. If you look at even generative AI application within Amazon, Amazon search, when you go search for a product and so forth, we have a team called MFi within Amazon search that helps bring these large language models into creating highly accurate search results. And these are created with models, really large models with tens of billions of parameters, scales to thousands of training jobs every month and trained on large model of hardware. And this is an example of a really good large language foundation model application running at production scale, and also, of course, Alexa, which uses a large generator model as well. And they actually even had a research paper that showed that they are more, and do better in accuracy than other systems like GPT-3 and whatnot. So, and we also touched on things like CodeWhisperer, which uses generative AI to improve developer productivity, but in a responsible manner, because 40% of some of the studies show 40% of this generated code had serious security flaws in it. This is where we didn't just do generative AI, we combined with automated reasoning capabilities, which is a very, very useful technique to identify these issues and couple them so that it produces highly secure code as well. Now, all these learnings taught us few things, and which is what you put in these three buckets. And yeah, like more than 100,000 customers using ML and AI services, including leading startups in the generative AI space, like stability AI, AI21 Labs, or Hugging Face, or even Alexa, for that matter. They care about, I put them in three dimension, one is around cost, which we touched on with Trainium and Inferentia, where we actually, the Trainium, you provide to 50% better cost savings, but the other aspect is, Trainium is a lot more power efficient as well compared to traditional one. And Inferentia is also better in terms of throughput, when it comes to what it is capable of. Like it is able to deliver up to three x higher compute performance and four x higher throughput, compared to it's previous generation, and it is extremely cost efficient and power efficient as well. >> Well. >> Now, the second element that really is important is in a day, developers deeply value the time it takes to build these models, and they don't want to build models from scratch. And this is where SageMaker, which is, even going to Kaggle uses, this is what it is, number one, enterprise ML platform. What it did to traditional machine learning, where tens of thousands of customers use StageMaker today, including the ones I mentioned, is that what used to take like months to build these models have dropped down to now a matter of days, if not less. Now, a generative AI, the cost of building these models, if you look at the landscape, the model parameter size had jumped by more than thousand X in the past three years, thousand x. And that means the training is like a really big distributed systems problem. How do you actually scale these model training? How do you actually ensure that you utilize these efficiently? Because these machines are very expensive, let alone they consume a lot of power. So, this is where SageMaker capability to build, automatically train, tune, and deploy models really concern this, especially with this distributor training infrastructure, and those are some of the reasons why some of the leading generative AI startups are actually leveraging it, because they do not want a giant infrastructure team, which is constantly tuning and fine tuning, and keeping these clusters alive. >> It sounds like a lot like what startups are doing with the cloud early days, no data center, you move to the cloud. So, this is the trend we're seeing, right? You guys are making it easier for developers with Hugging Face, I get that. I love that GitHub for machine learning, large language models are complex and expensive to build, but not anymore, you got Trainium and Inferentia, developers can get faster time to value, but then you got the transformers data sets, token libraries, all that optimized for generator. This is a perfect storm for startups. Jon Turow, a former AWS person, who used to work, I think for you, is now a VC at Madrona Venture, he and I were talking about the generator AI landscape, it's exploding with startups. Every alpha entrepreneur out there is seeing this as the next frontier, that's the 20 mile stairs, next 10 years is going to be huge. What is the big thing that's happened? 'Cause some people were saying, the founder of Yquem said, "Oh, the start ups won't be real, because they don't all have AI experience." John Markoff, former New York Times writer told me that, AI, there's so much work done, this is going to explode, accelerate really fast, because it's almost like it's been waiting for this moment. What's your reaction? >> I actually think there is going to be an explosion of startups, not because they need to be AI startups, but now finally AI is really accessible or going to be accessible, so that they can create remarkable applications, either for enterprises or for disrupting actually how customer service is being done or how creative tools are being built. And I mean, this is going to change in many ways. When we think about generative AI, we always like to think of how it generates like school homework or arts or music or whatnot, but when you look at it on the practical side, generative AI is being actually used across various industries. I'll give an example of like Autodesk. Autodesk is a customer who runs an AWS and SageMaker. They already have an offering that enables generated design, where designers can generate many structural designs for products, whereby you give a specific set of constraints and they actually can generate a structure accordingly. And we see similar kind of trend across various industries, where it can be around creative media editing or various others. I have the strong sense that literally, in the next few years, just like now, conventional machine learning is embedded in every application, every mobile app that we see, it is pervasive, and we don't even think twice about it, same way, like almost all apps are built on cloud. Generative AI is going to be part of every startup, and they are going to create remarkable experiences without needing actually, these deep generative AI scientists. But you won't get that until you actually make these models accessible. And I also don't think one model is going to rule the world, then you want these developers to have access to broad range of models. Just like, go back to the early days of deep learning. Everybody thought it is going to be one framework that will rule the world, and it has been changing, from Caffe to TensorFlow to PyTorch to various other things. And I have a suspicion, we had to enable developers where they are, so. >> You know, Dave Vellante and I have been riffing on this concept called super cloud, and a lot of people have co-opted to be multicloud, but we really were getting at this whole next layer on top of say, AWS. You guys are the most comprehensive cloud, you guys are a super cloud, and even Adam and I are talking about ISVs evolving to ecosystem partners. I mean, your top customers have ecosystems building on top of it. This feels like a whole nother AWS. How are you guys leveraging the history of AWS, which by the way, had the same trajectory, startups came in, they didn't want to provision a data center, the heavy lifting, all the things that have made Amazon successful culturally. And day one thinking is, provide the heavy lifting, undifferentiated heavy lifting, and make it faster for developers to program code. AI's got the same thing. How are you guys taking this to the next level, because now, this is an opportunity for the competition to change the game and take it over? This is, I'm sure, a conversation, you guys have a lot of things going on in AWS that makes you unique. What's the internal and external positioning around how you take it to the next level? >> I mean, so I agree with you that generative AI has a very, very strong potential in terms of what it can enable in terms of next generation application. But this is where Amazon's experience and expertise in putting these foundation models to work internally really has helped us quite a bit. If you look at it, like amazon.com search is like a very, very important application in terms of what is the customer impact on number of customers who use that application openly, and the amount of dollar impact it does for an organization. And we have been doing it silently for a while now. And the same thing is true for like Alexa too, which actually not only uses it for natural language understanding other city, even national leverages is set for creating stories and various other examples. And now, our approach to it from AWS is we actually look at it as in terms of the same three tiers like we did in machine learning, because when you look at generative AI, we genuinely see three sets of customers. One is, like really deep technical expert practitioner startups. These are the startups that are creating the next generation models like the likes of stability AIs or Hugging Face with Bloom or AI21. And they generally want to build their own models, and they want the best price performance of their infrastructure for training and inference. That's where our investments in silicon and hardware and networking innovations, where Trainium and Inferentia really plays a big role. And we can nearly do that, and that is one. The second middle tier is where I do think developers don't want to spend time building their own models, let alone, they actually want the model to be useful to that data. They don't need their models to create like high school homeworks or various other things. What they generally want is, hey, I had this data from my enterprises that I want to fine tune and make it really work only for this, and make it work remarkable, can be for tech summarization, to generate a report, or it can be for better Q&A, and so forth. This is where we are. Our investments in the middle tier with SageMaker, and our partnership with Hugging Face and AI21 and co here are all going to very meaningful. And you'll see us investing, I mean, you already talked about CodeWhisperer, which is an open preview, but we are also partnering with a whole lot of top ISVs, and you'll see more on this front to enable the next wave of generated AI apps too, because this is an area where we do think lot of innovation is yet to be done. It's like day one for us in this space, and we want to enable that huge ecosystem to flourish. >> You know, one of the things Dave Vellante and I were talking about in our first podcast we just did on Friday, we're going to do weekly, is we highlighted the AI ChatGPT example as a horizontal use case, because everyone loves it, people are using it in all their different verticals, and horizontal scalable cloud plays perfectly into it. So I have to ask you, as you look at what AWS is going to bring to the table, a lot's changed over the past 13 years with AWS, a lot more services are available, how should someone rebuild or re-platform and refactor their application of business with AI, with AWS? What are some of the tools that you see and recommend? Is it Serverless, is it SageMaker, CodeWhisperer? What do you think's going to shine brightly within the AWS stack, if you will, or service list, that's going to be part of this? As you mentioned, CodeWhisperer and SageMaker, what else should people be looking at as they start tinkering and getting all these benefits, and scale up their ups? >> You know, if we were a startup, first, I would really work backwards from the customer problem I try to solve, and pick and choose, bar, I don't need to deal with the undifferentiated heavy lifting, so. And that's where the answer is going to change. If you look at it then, the answer is not going to be like a one size fits all, so you need a very strong, I mean, granted on the compute front, if you can actually completely accurate it, so unless, I will always recommend it, instead of running compute for running your ups, because it takes care of all the undifferentiated heavy lifting, but on the data, and that's where we provide a whole variety of databases, right from like relational data, or non-relational, or dynamo, and so forth. And of course, we also have a deep analytical stack, where data directly flows from our relational databases into data lakes and data virus. And you can get value along with partnership with various analytical providers. The area where I do think fundamentally things are changing on what people can do is like, with CodeWhisperer, I was literally trying to actually program a code on sending a message through Twilio, and I was going to pull up to read a documentation, and in my ID, I was actually saying like, let's try sending a message to Twilio, or let's actually update a Route 53 error code. All I had to do was type in just a comment, and it actually started generating the sub-routine. And it is going to be a huge time saver, if I were a developer. And the goal is for us not to actually do it just for AWS developers, and not to just generate the code, but make sure the code is actually highly secure and follows the best practices. So, it's not always about machine learning, it's augmenting with automated reasoning as well. And generative AI is going to be changing, and not just in how people write code, but also how it actually gets built and used as well. You'll see a lot more stuff coming on this front. >> Swami, thank you for your time. I know you're super busy. Thank you for sharing on the news and giving commentary. Again, I think this is a AWS moment and industry moment, heavy lifting, accelerated value, agility. AIOps is going to be probably redefined here. Thanks for sharing your commentary. And we'll see you next time, I'm looking forward to doing more follow up on this. It's going to be a big wave. Thanks. >> Okay. Thanks again, John, always a pleasure. >> Okay. This is SiliconANGLE's breaking news commentary. I'm John Furrier with SiliconANGLE News, as well as host of theCUBE. Swami, who's a leader in AWS, has been on theCUBE multiple times. We've been tracking the growth of how Amazon's journey has just been exploding past five years, in particular, past three. You heard the numbers, great performance, great reviews. This is a watershed moment, I think, for the industry, and it's going to be a lot of fun for the next 10 years. Thanks for watching. (bright music)

>> From every ISV to solve the problems. You want there to be tools in place that you can use, either open source tools or whatever it is that help you build it. And slowly over time, that building will become easier and easier. So my question to you was, where do you see you playing? Do you see yourself playing to ISVs as a set of tools, which will make their life a lot easier and provide that work? >> Absolutely. >> If they don't have, so they don't have to do it. Or you're providing this for the end users? Or both? >> So it's a progression. If you go to the ISVs first, you're doomed to starved before you have time for that other option. >> Yeah. >> Right? So it's a question of phase, the phasing of it. And also if you go directly to end users, you can demonstrate the power of it and get the attention of the ISVs. I believe that the ISVs, especially those with the biggest footprints and the most, you know, coveted estates, they have already made massive investments at trying to solve decentralization of their software stack. And I believe that they have used it as a hook to try to move to a software as a service model and rope people into leasing their infrastructure. So if you look at the clouds that have been propped up by Autodesk or by Adobe, or you name the company, they are building proprietary makeshift solutions for decentralizing or hybrid clouding. Or maybe they're not even doing that at all and all they're is saying hey, if you want to get location agnosticness, then what you should just, is just move into our cloud. >> Right. >> And then they try to solve on the background how to decentralize it between different regions so they can have decent offerings in each region. But those who are more advanced have already made larger investments and will be more averse to, you know, throwing that stuff away, all of their makeshift machinery away, and using a platform that gives them high performance parallel, low level file system access, while at the same time having metadata-driven, you know, policy-based, intent-based orchestration to manage the diffusion of data across a decentralized infrastructure. They are not going to be as open because they've made such an investment and they're going to look at how do they monetize it. So what we have found with like the movie studios who are using us already, many of the app they're using, many of those software offerings, the ISVs have their own cloud that offers that software for the cloud. But what we got when I asked about this, 'cause I was dealt specifically into this question because I'm very interested to know how we're going to make that leap from end user upstream into the ISVs where I believe we need to, and they said, look, we cannot use these software ISV-specific SAS clouds for two reasons. Number one is we lose control of the data. We're giving it to them. That's security and other issues. And here you're talking about we're doing work for Disney, we're doing work for Netflix, and they're not going to let us put our data on those software clouds, on those SAS clouds. Secondly, in any reasonable pipeline, the data is shared by many different applications. We need to be agnostic as to the application. 'Cause the inputs to one application, you know, the output for one application provides the input to the next, and it's not necessarily from the same vendor. So they need to have a data platform that lets them, you know, go from one software stack, and you know, to run it on another. Because they might do the rendering with this and yet, they do the editing with that, and you know, et cetera, et cetera. So I think the further you go up the stack in the structured data and dedicated applications for specific functions in specific verticals, the further up the stack you go, the harder it is to justify a SAS offering where you're basically telling the end users you need to park all your data with us and then you can run your application in our cloud and get this. That ultimately is a dead end path versus having the data be open and available to many applications across this supercloud layer. >> Okay, so-- >> Is that making any sense? >> Yes, so if I could just ask a clarifying question. So, if I had to take Snowflake as an example, I think they're doing exactly what you're saying is a dead end, put everything into our proprietary system and then we'll figure out how to distribute it. >> Yeah. >> And and I think if you're familiar with Zhamak Dehghaniis' data mesh concept. Are you? >> A little bit, yeah. >> But in her model, Snowflake, a Snowflake warehouse is just a node on the mesh and that mesh is-- >> That's right. >> Ultimately the supercloud and you're an enabler of that is what I'm hearing. >> That's right. What they're doing up at the structured level and what they're talking about at the structured level we're doing at the underlying, unstructured level, which by the way has implications for how you implement those distributed database things. In other words, implementing a Snowflake on top of Hammerspace would have made building stuff like in the first place easier. It would allow you to easily shift and run the database engine anywhere. You still have to solve how to shard and distribute at the transaction layer above, so I'm not saying we're a substitute for what you need to do at the app layer. By the way, there is another example of that and that's Microsoft Office, right? It's one thing to share that, to have a file share where you can share all the docs. It's something else to have Word and PowerPoint, Excel know how to allow people to be simultaneously editing the same doc. That's always going to happen in the app layer. But not all applications need that level of, you know, in-app decentralization. You know, many of them, many workflows are pipelined, especially the ones that are very data intensive where you're doing drug discovery or you're doing rendering, or you're doing machine learning training. These things are human in the loop with large stages of processing across tens of thousands of cores. And I think that kind of data processing pipeline is what we're focusing on first. Not so much the Microsoft Office or the Snowflake, you know, parking a relational database because that takes a lot of application layer stuff and that's what they're good at. >> Right. >> But I think... >> Go ahead, sorry. >> Later entrance in these markets will find Hammerspace as a way to accelerate their work so they can focus more narrowly on just the stuff that's app-specific, higher level sharing in the app. >> Yes, Snowflake founders-- >> I think it might be worth mentioning also, just keep this confidential guys, but one of our customers is Blue Origin. And one of the things that we have found is kind of the point of what you're talking about with our customers. They're needing to build this and since it's not commercially available or they don't know where to look for it to be commercially available, they're all building themselves. So this layer is needed. And Blue is just one of the examples of quite a few we're now talking to. And like manufacturing, HPC, research where they're out trying to solve this problem with their own scripting tools and things like that. And I just, I don't know if there's anything you want to add, David, but you know, but there's definitely a demand here and customers are trying to figure out how to solve it beyond what Hammerspace is doing. Like the need is so great that they're just putting developers on trying to do it themselves. >> Well, and you know, Snowflake founders, they didn't have a Hammerspace to lean on. But, one of the things that's interesting about supercloud is we feel as though industry clouds will emerge, that as part of company's digital transformations, they will, you know, every company's a software company, they'll begin to build their own clouds and they will be able to use a Hammerspace to do that. >> A super pass layer. >> Yes. It's really, I don't know if David's speaking, I don't want to speak over him, but we can't hear you. May be going through a bad... >> Well, a regional, regional talks that make that possible. And so they're doing these render farms and editing farms, and it's a cloud-specific to the types of workflows in the median entertainment world. Or clouds specifically to workflows in the chip design world or in the drug and bio and life sciences exploration world. There are large organizations that are kind of a blend of end users, like the Broad, which has their own kind of cloud where they're asking collaborators to come in and work with them. So it starts to even blur who's an end user versus an ISV. >> Yes. >> Right? When you start talking about the massive data is the main gravity is to having lots of people participate. >> Yep, and that's where the value is. And that's where the value is. And this is a megatrend that we see. And so it's really important for us to get to the point of what is and what is not a supercloud and, you know, that's where we're trying to evolve. >> Let's talk about this for a second 'cause I want to, I want to challenge you on something and it's something that I got challenged on and it has led me to thinking differently than I did at first, which Molly can attest to. Okay? So, we have been looking for a way to talk about the concept of cloud of utility computing, run anything anywhere that isn't addressed in today's realization of cloud. 'Cause today's cloud is not run anything anywhere, it's quite the opposite. You park your data in AWS and that's where you run stuff. And you pretty much have to. Same with with Azure. They're using data gravity to keep you captive there, just like the old infrastructure guys did. But now it's even worse because it's coupled back with the software to some degree, as well. And you have to use their storage, networking, and compute. It's not, I mean it fell back to the mainframe era. Anyhow, so I love the concept of supercloud. By the way, I was going to suggest that a better term might be hyper cloud since hyper speaks to the multidimensionality of it and the ability to be in a, you know, be in a different dimension, a different plane of existence kind of thing like hyperspace. But super and hyper are somewhat synonyms. I mean, you have hyper cars and you have super cars and blah, blah, blah. I happen to like hyper maybe also because it ties into the whole Hammerspace notion of a hyper-dimensional, you know, reality, having your data centers connected by a wormhole that is Hammerspace. But regardless, what I got challenged on is calling it something different at all versus simply saying, this is what cloud has always meant to be. This is the true cloud, this is real cloud, this is cloud. And I think back to what happened, you'll remember, at Fusion IO we talked about IO memory and we did that because people had a conceptualization of what an SSD was. And an SSD back then was low capacity, low endurance, made to go military, aerospace where things needed to be rugged but was completely useless in the data center. And we needed people to imagine this thing as being able to displace entire SAND, with the kind of capacity density, performance density, endurance. And so we talked IO memory, we could have said enterprise SSD, and that's what the industry now refers to for that concept. What will people be saying five and 10 years from now? Will they simply say, well this is cloud as it was always meant to be where you are truly able to run anything anywhere and have not only the same APIs, but you're same data available with high performance access, all forms of access, block file and object everywhere. So yeah. And I wonder, and this is just me throwing it out there, I wonder if, well, there's trade offs, right? Giving it a new moniker, supercloud, versus simply talking about how cloud is always intended to be and what it was meant to be, you know, the real cloud or true cloud, there are trade-offs. By putting a name on it and branding it, that lets people talk about it and understand they're talking about something different. But it also is that an affront to people who thought that that's what they already had. >> What's different, what's new? Yes, and so we've given a lot of thought to this. >> Right, it's like you. >> And it's because we've been asked that why does the industry need a new term, and we've tried to address some of that. But some of the inside baseball that we haven't shared is, you remember the Web 2.0, back then? >> Yep. >> Web 2.0 was the same thing. And I remember Tim Burners Lee saying, "Why do we need Web 2.0? "This is what the Web was always supposed to be." But the truth is-- >> I know, that was another perfect-- >> But the truth is it wasn't, number one. Number two, everybody hated the Web 2.0 term. John Furrier was actually in the middle of it all. And then it created this groundswell. So one of the things we wrote about is that supercloud is an evocative term that catalyzes debate and conversation, which is what we like, of course. And maybe that's self-serving. But yeah, HyperCloud, Metacloud, super, meaning, it's funny because super came from Latin supra, above, it was never the superlative. But the superlative was a convenient byproduct that caused a lot of friction and flack, which again, in the media business is like a perfect storm brewing. >> The bad thing to have to, and I think you do need to shake people out of their, the complacency of the limitations that they're used to. And I'll tell you what, the fact that you even have the terms hybrid cloud, multi-cloud, private cloud, edge computing, those are all just referring to the different boundaries that isolate the silo that is the current limited cloud. >> Right. >> So if I heard correctly, what just, in terms of us defining what is and what isn't in supercloud, you would say traditional applications which have to run in a certain place, in a certain cloud can't run anywhere else, would be the stuff that you would not put in as being addressed by supercloud. And over time, you would want to be able to run the data where you want to and in any of those concepts. >> Or even modern apps, right? Or even modern apps that are siloed in SAS within an individual cloud, right? >> So yeah, I guess it's twofold. Number one, if you're going at the high application layers, there's lots of ways that you can give the appearance of anything running anywhere. The ISV, the SAS vendor can engineer stuff to have the ability to serve with low enough latency to different geographies, right? So if you go too high up the stack, it kind of loses its meaning because there's lots of different ways to make due and give the appearance of omni-presence of the service. Okay? As you come down more towards the platform layer, it gets harder and harder to mask the fact that supercloud is something entirely different than just a good regionally-distributed SAS service. So I don't think you, I don't think you can distinguish supercloud if you go too high up the stack because it's just SAS, it's just a good SAS service where the SAS vendor has done the hard work to give you low latency access from different geographic regions. >> Yeah, so this is one of the hardest things, David. >> Common among them. >> Yeah, this is really an important point. This is one of the things I've had the most trouble with is why is this not just SAS? >> So you dilute your message when you go up to the SAS layer. If you were to focus most of this around the super pass layer, the how can you host applications and run them anywhere and not host this, not run a service, not have a service available everywhere. So how can you take any application, even applications that are written, you know, in a traditional legacy data center fashion and be able to run them anywhere and have them have their binaries and their datasets and the runtime environment and the infrastructure to start them and stop them? You know, the jobs, the, what the Kubernetes, the job scheduler? What we're really talking about here, what I think we're really talking about here is building the operating system for a decentralized cloud. What is the operating system, the operating environment for a decentralized cloud? Where you can, and that the main two functions of an operating system or an operating environment are the process scheduler, the thing that's scheduling what is running where and when and so forth, and the file system, right? The thing that's supplying a common view and access to data. So when we talk about this, I think that the strongest argument for supercloud is made when you go down to the platform layer and talk of it, talk about it as an operating environment on which you can run all forms of applications. >> Would you exclude--? >> Not a specific application that's been engineered as a SAS. (audio distortion) >> He'll come back. >> Are you there? >> Yeah, yeah, you just cut out for a minute. >> I lost your last statement when you broke up. >> We heard you, you said that not the specific application. So would you exclude Snowflake from supercloud? >> Frankly, I would. I would. Because, well, and this is kind of hard to do because Snowflake doesn't like to, Frank doesn't like to talk about Snowflake as a SAS service. It has a negative connotation. >> But it is. >> I know, we all know it is. We all know it is and because it is, yes, I would exclude them. >> I think I actually have him on camera. >> There's nothing in common. >> I think I have him on camera or maybe Benoit as saying, "Well, we are a SAS." I think it's Slootman. I think I said to Slootman, "I know you don't like to say you're a SAS." And I think he said, "Well, we are a SAS." >> Because again, if you go to the top of the application stack, there's any number of ways you can give it location agnostic function or you know, regional, local stuff. It's like let's solve the location problem by having me be your one location. How can it be decentralized if you're centralizing on (audio distortion)? >> Well, it's more decentralized than if it's all in one cloud. So let me actually, so the spectrum. So again, in the spirit of what is and what isn't, I think it's safe to say Hammerspace is supercloud. I think there's no debate there, right? Certainly among this crowd. And I think we can all agree that Dell, Dell Storage is not supercloud. Where it gets fuzzy is this Snowflake example or even, how about a, how about a Cohesity that instantiates its stack in different cloud regions in different clouds, and synchronizes, however magic sauce it does that. Is that a supercloud? I mean, so I'm cautious about having too strict of a definition 'cause then only-- >> Fair enough, fair enough. >> But I could use your help and thoughts on that. >> So I think we're talking about two different spectrums here. One is the spectrum of platform to application-specific. As you go up the application stack and it becomes this specific thing. Or you go up to the more and more structured where it's serving a specific application function where it's more of a SAS thing. I think it's harder to call a SAS service a supercloud. And I would argue that the reason there, and what you're lacking in the definition is to talk about it as general purpose. Okay? Now, that said, a data warehouse is general purpose at the structured data level. So you could make the argument for why Snowflake is a supercloud by saying that it is a general purpose platform for doing lots of different things. It's just one at a higher level up at the structured data level. So one spectrum is the high level going from platform to, you know, unstructured data to structured data to very application-specific, right? Like a specific, you know, CAD/CAM mechanical design cloud, like an Autodesk would want to give you their cloud for running, you know, and sharing CAD/CAM designs, doing your CAD/CAM anywhere stuff. Well, the other spectrum is how well does the purported supercloud technology actually live up to allowing you to run anything anywhere with not just the same APIs but with the local presence of data with the exact same runtime environment everywhere, and to be able to correctly manage how to get that runtime environment anywhere. So a Cohesity has some means of running things in different places and some means of coordinating what's where and of serving diff, you know, things in different places. I would argue that it is a very poor approximation of what Hammerspace does in providing the exact same file system with local high performance access everywhere with metadata ability to control where the data is actually instantiated so that you don't have to wait for it to get orchestrated. But even then when you do have to wait for it, it happens automatically and so it's still only a matter of, well, how quick is it? And on the other end of the spectrum is you could look at NetApp with Flexcache and say, "Is that supercloud?" And I would argue, well kind of because it allows you to run things in different places because it's a cache. But you know, it really isn't because it presumes some central silo from which you're cacheing stuff. So, you know, is it or isn't it? Well, it's on a spectrum of exactly how fully is it decoupling a runtime environment from specific locality? And I think a cache doesn't, it stretches a specific silo and makes it have some semblance of similar access in other places. But there's still a very big difference to the central silo, right? You can't turn off that central silo, for example. >> So it comes down to how specific you make the definition. And this is where it gets kind of really interesting. It's like cloud. Does IBM have a cloud? >> Exactly. >> I would say yes. Does it have the kind of quality that you would expect from a hyper-scale cloud? No. Or see if you could say the same thing about-- >> But that's a problem with choosing a name. That's the problem with choosing a name supercloud versus talking about the concept of cloud and how true up you are to that concept. >> For sure. >> Right? Because without getting a name, you don't have to draw, yeah. >> I'd like to explore one particular or bring them together. You made a very interesting observation that from a enterprise point of view, they want to safeguard their store, their data, and they want to make sure that they can have that data running in their own workflows, as well as, as other service providers providing services to them for that data. So, and in in particular, if you go back to, you go back to Snowflake. If Snowflake could provide the ability for you to have your data where you wanted, you were in charge of that, would that make Snowflake a supercloud? >> I'll tell you, in my mind, they would be closer to my conceptualization of supercloud if you can instantiate Snowflake as software on your own infrastructure, and pump your own data to Snowflake that's instantiated on your own infrastructure. The fact that it has to be on their infrastructure or that it's on their, that it's on their account in the cloud, that you're giving them the data and they're, that fundamentally goes against it to me. If they, you know, they would be a pure, a pure plate if they were a software defined thing where you could instantiate Snowflake machinery on the infrastructure of your choice and then put your data into that machinery and get all the benefits of Snowflake. >> So did you see--? >> In other words, if they were not a SAS service, but offered all of the similar benefits of being, you know, if it were a service that you could run on your own infrastructure. >> So did you see what they announced, that--? >> I hope that's making sense. >> It does, did you see what they announced at Dell? They basically announced the ability to take non-native Snowflake data, read it in from an object store on-prem, like a Dell object store. They do the same thing with Pure, read it in, running it in the cloud, and then push it back out. And I was saying to Dell, look, that's fine. Okay, that's interesting. You're taking a materialized view or an extended table, whatever you're doing, wouldn't it be more interesting if you could actually run the query locally with your compute? That would be an extension that would actually get my attention and extend that. >> That is what I'm talking about. That's what I'm talking about. And that's why I'm saying I think Hammerspace is more progressive on that front because with our technology, anybody who can instantiate a service, can make a service. And so I, so MSPs can use Hammerspace as a way to build a super pass layer and host their clients on their infrastructure in a cloud-like fashion. And their clients can have their own private data centers and the MSP or the public clouds, and Hammerspace can be instantiated, get this, by different parties in these different pieces of infrastructure and yet linked together to make a common file system across all of it. >> But this is data mesh. If I were HPE and Dell it's exactly what I'd be doing. I'd be working with Hammerspace to create my own data. I'd work with Databricks, Snowflake, and any other-- >> Data mesh is a good way to put it. Data mesh is a good way to put it. And this is at the lowest level of, you know, the underlying file system that's mountable by the operating system, consumed as a real file system. You can't get lower level than that. That's why this is the foundation for all of the other apps and structured data systems because you need to have a data mesh that can at least mesh the binary blob. >> Okay. >> That hold the binaries and that hold the datasets that those applications are running. >> So David, in the third week of January, we're doing supercloud 2 and I'm trying to convince John Furrier to make it a data slash data mesh edition. I'm slowly getting him to the knothole. I would very much, I mean you're in the Bay Area, I'd very much like you to be one of the headlines. As Zhamak Dehghaniis going to speak, she's the creator of Data Mesh, >> Sure. >> I'd love to have you come into our studio as well, for the live session. If you can't make it, we can pre-record. But you're right there, so I'll get you the dates. >> We'd love to, yeah. No, you can count on it. No, definitely. And you know, we don't typically talk about what we do as Data Mesh. We've been, you know, using global data environment. But, you know, under the covers, that's what the thing is. And so yeah, I think we can frame the discussion like that to line up with other, you know, with the other discussions. >> Yeah, and Data Mesh, of course, is one of those evocative names, but she has come up with some very well defined principles around decentralized data, data as products, self-serve infrastructure, automated governance, and and so forth, which I think your vision plugs right into. And she's brilliant. You'll love meeting her. >> Well, you know, and I think.. Oh, go ahead. Go ahead, Peter. >> Just like to work one other interface which I think is important. How do you see yourself and the open source? You talked about having an operating system. Obviously, Linux is the operating system at one level. How are you imagining that you would interface with cost community as part of this development? >> Well, it's funny you ask 'cause my CTO is the kernel maintainer of the storage networking stack. So how the Linux operating system perceives and consumes networked data at the file system level, the network file system stack is his purview. He owns that, he wrote most of it over the last decade that he's been the maintainer, but he's the gatekeeper of what goes in. And we have leveraged his abilities to enhance Linux to be able to use this decentralized data, in particular with decoupling the control plane driven by metadata from the data access path and the many storage systems on which the data gets accessed. So this factoring, this splitting of control plane from data path, metadata from data, was absolutely necessary to create a data mesh like we're talking about. And to be able to build this supercloud concept. And the highways on which the data runs and the client which knows how to talk to it is all open source. And we have, we've driven the NFS 4.2 spec. The newest NFS spec came from my team. And it was specifically the enhancements needed to be able to build a spanning file system, a data mesh at a file system level. Now that said, our file system itself and our server, our file server, our data orchestration, our data management stuff, that's all closed source, proprietary Hammerspace tech. But the highways on which the mesh connects are actually all open source and the client that knows how to consume it. So we would, honestly, I would welcome competitors using those same highways. They would be at a major disadvantage because we kind of built them, but it would still be very validating and I think only increase the potential adoption rate by more than whatever they might take of the market. So it'd actually be good to split the market with somebody else to come in and share those now super highways for how to mesh data at the file system level, you know, in here. So yeah, hopefully that answered your question. Does that answer the question about how we embrace the open source? >> Right, and there was one other, just that my last one is how do you enable something to run in every environment? And if we take the edge, for example, as being, as an environment which is much very, very compute heavy, but having a lot less capability, how do you do a hold? >> Perfect question. Perfect question. What we do today is a software appliance. We are using a Linux RHEL 8, RHEL 8 equivalent or a CentOS 8, or it's, you know, they're all roughly equivalent. But we have bundled and a software appliance which can be instantiated on bare metal hardware on any type of VM system from VMware to all of the different hypervisors in the Linux world, to even Nutanix and such. So it can run in any virtualized environment and it can run on any cloud instance, server instance in the cloud. And we have it packaged and deployable from the marketplaces within the different clouds. So you can literally spin it up at the click of an API in the cloud on instances in the cloud. So with all of these together, you can basically instantiate a Hammerspace set of machinery that can offer up this file system mesh. like we've been using the terminology we've been using now, anywhere. So it's like being able to take and spin up Snowflake and then just be able to install and run some VMs anywhere you want and boom, now you have a Snowflake service. And by the way, it is so complete that some of our customers, I would argue many aren't even using public clouds at all, they're using this just to run their own data centers in a cloud-like fashion, you know, where they have a data service that can span it all. >> Yeah and to Molly's first point, we would consider that, you know, cloud. Let me put you on the spot. If you had to describe conceptually without a chalkboard what an architectural diagram would look like for supercloud, what would you say? >> I would say it's to have the same runtime environment within every data center and defining that runtime environment as what it takes to schedule the execution of applications, so job scheduling, runtime stuff, and here we're talking Kubernetes, Slurm, other things that do job scheduling. We're talking about having a common way to, you know, instantiate compute resources. So a global compute environment, having a common compute environment where you can instantiate things that need computing. Okay? So that's the first part. And then the second is the data platform where you can have file block and object volumes, and have them available with the same APIs in each of these distributed data centers and have the exact same data omnipresent with the ability to control where the data is from one moment to the next, local, where all the data is instantiate. So my definition would be a common runtime environment that's bifurcate-- >> Oh. (attendees chuckling) We just lost them at the money slide. >> That's part of the magic makes people listen. We keep someone on pin and needles waiting. (attendees chuckling) >> That's good. >> Are you back, David? >> I'm on the edge of my seat. Common runtime environment. It was like... >> And just wait, there's more. >> But see, I'm maybe hyper-focused on the lower level of what it takes to host and run applications. And that's the stuff to schedule what resources they need to run and to get them going and to get them connected through to their persistence, you know, and their data. And to have that data available in all forms and have it be the same data everywhere. On top of that, you could then instantiate applications of different types, including relational databases, and data warehouses and such. And then you could say, now I've got, you know, now I've got these more application-level or structured data-level things. I tend to focus less on that structured data level and the application level and am more focused on what it takes to host any of them generically on that super pass layer. And I'll admit, I'm maybe hyper-focused on the pass layer and I think it's valid to include, you know, higher levels up the stack like the structured data level. But as soon as you go all the way up to like, you know, a very specific SAS service, I don't know that you would call that supercloud. >> Well, and that's the question, is there value? And Marianna Tessel from Intuit said, you know, we looked at it, we did it, and it just, it was actually negative value for us because connecting to all these separate clouds was a real pain in the neck. Didn't bring us any additional-- >> Well that's 'cause they don't have this pass layer underneath it so they can't even shop around, which actually makes it hard to stand up your own SAS service. And ultimately they end up having to build their own infrastructure. Like, you know, I think there's been examples like Netflix moving away from the cloud to their own infrastructure. Basically, if you're going to rent it for more than a few months, it makes sense to build it yourself, if it's at any kind of scale. >> Yeah, for certain components of that cloud. But if the Goldman Sachs came to you, David, and said, "Hey, we want to collaborate and we want to build "out a cloud and essentially build our SAS system "and we want to do that with Hammerspace, "and we want to tap the physical infrastructure "of not only our data centers but all the clouds," then that essentially would be a SAS, would it not? And wouldn't that be a Super SAS or a supercloud? >> Well, you know, what they may be using to build their service is a supercloud, but their service at the end of the day is just a SAS service with global reach. Right? >> Yeah. >> You know, look at, oh shoot. What's the name of the company that does? It has a cloud for doing bookkeeping and accounting. I forget their name, net something. NetSuite. >> NetSuite. NetSuite, yeah, Oracle. >> Yeah. >> Yep. >> Oracle acquired them, right? Is NetSuite a supercloud or is it just a SAS service? You know? I think under the covers you might ask are they using supercloud under the covers so that they can run their SAS service anywhere and be able to shop the venue, get elasticity, get all the benefits of cloud in the, to the benefit of their service that they're offering? But you know, folks who consume the service, they don't care because to them they're just connecting to some endpoint somewhere and they don't have to care. So the further up the stack you go, the more location-agnostic it is inherently anyway. >> And I think it's, paths is really the critical layer. We thought about IAS Plus and we thought about SAS Minus, you know, Heroku and hence, that's why we kind of got caught up and included it. But SAS, I admit, is the hardest one to crack. And so maybe we exclude that as a deployment model. >> That's right, and maybe coming down a level to saying but you can have a structured data supercloud, so you could still include, say, Snowflake. Because what Snowflake is doing is more general purpose. So it's about how general purpose it is. Is it hosting lots of other applications or is it the end application? Right? >> Yeah. >> So I would argue general purpose nature forces you to go further towards platform down-stack. And you really need that general purpose or else there is no real distinguishing. So if you want defensible turf to say supercloud is something different, I think it's important to not try to wrap your arms around SAS in the general sense. >> Yeah, and we've kind of not really gone, leaned hard into SAS, we've just included it as a deployment model, which, given the constraints that you just described for structured data would apply if it's general purpose. So David, super helpful. >> Had it sign. Define the SAS as including the hybrid model hold SAS. >> Yep. >> Okay, so with your permission, I'm going to add you to the list of contributors to the definition. I'm going to add-- >> Absolutely. >> I'm going to add this in. I'll share with Molly. >> Absolutely. >> We'll get on the calendar for the date. >> If Molly can share some specific language that we've been putting in that kind of goes to stuff we've been talking about, so. >> Oh, great. >> I think we can, we can share some written kind of concrete recommendations around this stuff, around the general purpose, nature, the common data thing and yeah. >> Okay. >> Really look forward to it and would be glad to be part of this thing. You said it's in February? >> It's in January, I'll let Molly know. >> Oh, January. >> What the date is. >> Excellent. >> Yeah, third week of January. Third week of January on a Tuesday, whatever that is. So yeah, we would welcome you in. But like I said, if it doesn't work for your schedule, we can prerecord something. But it would be awesome to have you in studio. >> I'm sure with this much notice we'll be able to get something. Let's make sure we have the dates communicated to Molly and she'll get my admin to set it up outside so that we have it. >> I'll get those today to you, Molly. Thank you. >> By the way, I am so, so pleased with being able to work with you guys on this. I think the industry needs it very bad. They need something to break them out of the box of their own mental constraints of what the cloud is versus what it's supposed to be. And obviously, the more we get people to question their reality and what is real, what are we really capable of today that then the more business that we're going to get. So we're excited to lend the hand behind this notion of supercloud and a super pass layer in whatever way we can. >> Awesome. >> Can I ask you whether your platforms include ARM as well as X86? >> So we have not done an ARM port yet. It has been entertained and won't be much of a stretch. >> Yeah, it's just a matter of time. >> Actually, entertained doing it on behalf of NVIDIA, but it will absolutely happen because ARM in the data center I think is a foregone conclusion. Well, it's already there in some cases, but not quite at volume. So definitely will be the case. And I'll tell you where this gets really interesting, discussion for another time, is back to my old friend, the SSD, and having SSDs that have enough brains on them to be part of that fabric. Directly. >> Interesting. Interesting. >> Very interesting. >> Directly attached to ethernet and able to create a data mesh global file system, that's going to be really fascinating. Got to run now. >> All right, hey, thanks you guys. Thanks David, thanks Molly. Great to catch up. Bye-bye. >> Bye >> Talk to you soon.

>> From The Cube Studios in Palo Alto and Boston, bringing you data driven insights from The Cube at ETR. This is "Breaking Analysis" with Dave Vellante. >> The reverse momentum in tech stocks caused by rising interest rates, less attractive discounted cash flow models, and more tepid forward guidance, can be easily measured by public market valuations. And while there's lots of discussion about the impact on private companies and cash runway and 409A valuations, measuring the performance of non-public companies isn't as easy. IPOs have dried up and public statements by private companies, of course, they accentuate the good and they kind of hide the bad. Real data, unless you're an insider, is hard to find. Hello and welcome to this week's "Wikibon Cube Insights" powered by ETR. In this "Breaking Analysis", we unlock some of the secrets that non-public, emerging tech companies may or may not be sharing. And we do this by introducing you to a capability from ETR that we've not exposed you to over the past couple of years, it's called the Emerging Technologies Survey, and it is packed with sentiment data and performance data based on surveys of more than a thousand CIOs and IT buyers covering more than 400 companies. And we've invited back our colleague, Erik Bradley of ETR to help explain the survey and the data that we're going to cover today. Erik, this survey is something that I've not personally spent much time on, but I'm blown away at the data. It's really unique and detailed. First of all, welcome. Good to see you again. >> Great to see you too, Dave, and I'm really happy to be talking about the ETS or the Emerging Technology Survey. Even our own clients of constituents probably don't spend as much time in here as they should. >> Yeah, because there's so much in the mainstream, but let's pull up a slide to bring out the survey composition. Tell us about the study. How often do you run it? What's the background and the methodology? >> Yeah, you were just spot on the way you were talking about the private tech companies out there. So what we did is we decided to take all the vendors that we track that are not yet public and move 'em over to the ETS. And there isn't a lot of information out there. If you're not in Silicon (indistinct), you're not going to get this stuff. So PitchBook and Tech Crunch are two out there that gives some data on these guys. But what we really wanted to do was go out to our community. We have 6,000, ITDMs in our community. We wanted to ask them, "Are you aware of these companies? And if so, are you allocating any resources to them? Are you planning to evaluate them," and really just kind of figure out what we can do. So this particular survey, as you can see, 1000 plus responses, over 450 vendors that we track. And essentially what we're trying to do here is talk about your evaluation and awareness of these companies and also your utilization. And also if you're not utilizing 'em, then we can also figure out your sales conversion or churn. So this is interesting, not only for the ITDMs themselves to figure out what their peers are evaluating and what they should put in POCs against the big guys when contracts come up. But it's also really interesting for the tech vendors themselves to see how they're performing. >> And you can see 2/3 of the respondents are director level of above. You got 28% is C-suite. There is of course a North America bias, 70, 75% is North America. But these smaller companies, you know, that's when they start doing business. So, okay. We're going to do a couple of things here today. First, we're going to give you the big picture across the sectors that ETR covers within the ETS survey. And then we're going to look at the high and low sentiment for the larger private companies. And then we're going to do the same for the smaller private companies, the ones that don't have as much mindshare. And then I'm going to put those two groups together and we're going to look at two dimensions, actually three dimensions, which companies are being evaluated the most. Second, companies are getting the most usage and adoption of their offerings. And then third, which companies are seeing the highest churn rates, which of course is a silent killer of companies. And then finally, we're going to look at the sentiment and mindshare for two key areas that we like to cover often here on "Breaking Analysis", security and data. And data comprises database, including data warehousing, and then big data analytics is the second part of data. And then machine learning and AI is the third section within data that we're going to look at. Now, one other thing before we get into it, ETR very often will include open source offerings in the mix, even though they're not companies like TensorFlow or Kubernetes, for example. And we'll call that out during this discussion. The reason this is done is for context, because everyone is using open source. It is the heart of innovation and many business models are super glued to an open source offering, like take MariaDB, for example. There's the foundation and then there's with the open source code and then there, of course, the company that sells services around the offering. Okay, so let's first look at the highest and lowest sentiment among these private firms, the ones that have the highest mindshare. So they're naturally going to be somewhat larger. And we do this on two dimensions, sentiment on the vertical axis and mindshare on the horizontal axis and note the open source tool, see Kubernetes, Postgres, Kafka, TensorFlow, Jenkins, Grafana, et cetera. So Erik, please explain what we're looking at here, how it's derived and what the data tells us. >> Certainly, so there is a lot here, so we're going to break it down first of all by explaining just what mindshare and net sentiment is. You explain the axis. We have so many evaluation metrics, but we need to aggregate them into one so that way we can rank against each other. Net sentiment is really the aggregation of all the positive and subtracting out the negative. So the net sentiment is a very quick way of looking at where these companies stand versus their peers in their sectors and sub sectors. Mindshare is basically the awareness of them, which is good for very early stage companies. And you'll see some names on here that are obviously been around for a very long time. And they're clearly be the bigger on the axis on the outside. Kubernetes, for instance, as you mentioned, is open source. This de facto standard for all container orchestration, and it should be that far up into the right, because that's what everyone's using. In fact, the open source leaders are so prevalent in the emerging technology survey that we break them out later in our analysis, 'cause it's really not fair to include them and compare them to the actual companies that are providing the support and the security around that open source technology. But no survey, no analysis, no research would be complete without including these open source tech. So what we're looking at here, if I can just get away from the open source names, we see other things like Databricks and OneTrust . They're repeating as top net sentiment performers here. And then also the design vendors. People don't spend a lot of time on 'em, but Miro and Figma. This is their third survey in a row where they're just dominating that sentiment overall. And Adobe should probably take note of that because they're really coming after them. But Databricks, we all know probably would've been a public company by now if the market hadn't turned, but you can see just how dominant they are in a survey of nothing but private companies. And we'll see that again when we talk about the database later. >> And I'll just add, so you see automation anywhere on there, the big UiPath competitor company that was not able to get to the public markets. They've been trying. Snyk, Peter McKay's company, they've raised a bunch of money, big security player. They're doing some really interesting things in developer security, helping developers secure the data flow, H2O.ai, Dataiku AI company. We saw them at the Snowflake Summit. Redis Labs, Netskope and security. So a lot of names that we know that ultimately we think are probably going to be hitting the public market. Okay, here's the same view for private companies with less mindshare, Erik. Take us through this one. >> On the previous slide too real quickly, I wanted to pull that security scorecard and we'll get back into it. But this is a newcomer, that I couldn't believe how strong their data was, but we'll bring that up in a second. Now, when we go to the ones of lower mindshare, it's interesting to talk about open source, right? Kubernetes was all the way on the top right. Everyone uses containers. Here we see Istio up there. Not everyone is using service mesh as much. And that's why Istio is in the smaller breakout. But still when you talk about net sentiment, it's about the leader, it's the highest one there is. So really interesting to point out. Then we see other names like Collibra in the data side really performing well. And again, as always security, very well represented here. We have Aqua, Wiz, Armis, which is a standout in this survey this time around. They do IoT security. I hadn't even heard of them until I started digging into the data here. And I couldn't believe how well they were doing. And then of course you have AnyScale, which is doing a second best in this and the best name in the survey Hugging Face, which is a machine learning AI tool. Also doing really well on a net sentiment, but they're not as far along on that access of mindshare just yet. So these are again, emerging companies that might not be as well represented in the enterprise as they will be in a couple of years. >> Hugging Face sounds like something you do with your two year old. Like you said, you see high performers, AnyScale do machine learning and you mentioned them. They came out of Berkeley. Collibra Governance, InfluxData is on there. InfluxDB's a time series database. And yeah, of course, Alex, if you bring that back up, you get a big group of red dots, right? That's the bad zone, I guess, which Sisense does vis, Yellowbrick Data is a NPP database. How should we interpret the red dots, Erik? I mean, is it necessarily a bad thing? Could it be misinterpreted? What's your take on that? >> Sure, well, let me just explain the definition of it first from a data science perspective, right? We're a data company first. So the gray dots that you're seeing that aren't named, that's the mean that's the average. So in order for you to be on this chart, you have to be at least one standard deviation above or below that average. So that gray is where we're saying, "Hey, this is where the lump of average comes in. This is where everyone normally stands." So you either have to be an outperformer or an underperformer to even show up in this analysis. So by definition, yes, the red dots are bad. You're at least one standard deviation below the average of your peers. It's not where you want to be. And if you're on the lower left, not only are you not performing well from a utilization or an actual usage rate, but people don't even know who you are. So that's a problem, obviously. And the VCs and the PEs out there that are backing these companies, they're the ones who mostly are interested in this data. >> Yeah. Oh, that's great explanation. Thank you for that. No, nice benchmarking there and yeah, you don't want to be in the red. All right, let's get into the next segment here. Here going to look at evaluation rates, adoption and the all important churn. First new evaluations. Let's bring up that slide. And Erik, take us through this. >> So essentially I just want to explain what evaluation means is that people will cite that they either plan to evaluate the company or they're currently evaluating. So that means we're aware of 'em and we are choosing to do a POC of them. And then we'll see later how that turns into utilization, which is what a company wants to see, awareness, evaluation, and then actually utilizing them. That's sort of the life cycle for these emerging companies. So what we're seeing here, again, with very high evaluation rates. H2O, we mentioned. SecurityScorecard jumped up again. Chargebee, Snyk, Salt Security, Armis. A lot of security names are up here, Aqua, Netskope, which God has been around forever. I still can't believe it's in an Emerging Technology Survey But so many of these names fall in data and security again, which is why we decided to pick those out Dave. And on the lower side, Vena, Acton, those unfortunately took the dubious award of the lowest evaluations in our survey, but I prefer to focus on the positive. So SecurityScorecard, again, real standout in this one, they're in a security assessment space, basically. They'll come in and assess for you how your security hygiene is. And it's an area of a real interest right now amongst our ITDM community. >> Yeah, I mean, I think those, and then Arctic Wolf is up there too. They're doing managed services. You had mentioned Netskope. Yeah, okay. All right, let's look at now adoption. These are the companies whose offerings are being used the most and are above that standard deviation in the green. Take us through this, Erik. >> Sure, yet again, what we're looking at is, okay, we went from awareness, we went to evaluation. Now it's about utilization, which means a survey respondent's going to state "Yes, we evaluated and we plan to utilize it" or "It's already in our enterprise and we're actually allocating further resources to it." Not surprising, again, a lot of open source, the reason why, it's free. So it's really easy to grow your utilization on something that's free. But as you and I both know, as Red Hat proved, there's a lot of money to be made once the open source is adopted, right? You need the governance, you need the security, you need the support wrapped around it. So here we're seeing Kubernetes, Postgres, Apache Kafka, Jenkins, Grafana. These are all open source based names. But if we're looking at names that are non open source, we're going to see Databricks, Automation Anywhere, Rubrik all have the highest mindshare. So these are the names, not surprisingly, all names that probably should have been public by now. Everyone's expecting an IPO imminently. These are the names that have the highest mindshare. If we talk about the highest utilization rates, again, Miro and Figma pop up, and I know they're not household names, but they are just dominant in this survey. These are applications that are meant for design software and, again, they're going after an Autodesk or a CAD or Adobe type of thing. It is just dominant how high the utilization rates are here, which again is something Adobe should be paying attention to. And then you'll see a little bit lower, but also interesting, we see Collibra again, we see Hugging Face again. And these are names that are obviously in the data governance, ML, AI side. So we're seeing a ton of data, a ton of security and Rubrik was interesting in this one, too, high utilization and high mindshare. We know how pervasive they are in the enterprise already. >> Erik, Alex, keep that up for a second, if you would. So yeah, you mentioned Rubrik. Cohesity's not on there. They're sort of the big one. We're going to talk about them in a moment. Puppet is interesting to me because you remember the early days of that sort of space, you had Puppet and Chef and then you had Ansible. Red Hat bought Ansible and then Ansible really took off. So it's interesting to see Puppet on there as well. Okay. So now let's look at the churn because this one is where you don't want to be. It's, of course, all red 'cause churn is bad. Take us through this, Erik. >> Yeah, definitely don't want to be here and I don't love to dwell on the negative. So we won't spend as much time. But to your point, there's one thing I want to point out that think it's important. So you see Rubrik in the same spot, but Rubrik has so many citations in our survey that it actually would make sense that they're both being high utilization and churn just because they're so well represented. They have such a high overall representation in our survey. And the reason I call that out is Cohesity. Cohesity has an extremely high churn rate here about 17% and unlike Rubrik, they were not on the utilization side. So Rubrik is seeing both, Cohesity is not. It's not being utilized, but it's seeing a high churn. So that's the way you can look at this data and say, "Hm." Same thing with Puppet. You noticed that it was on the other slide. It's also on this one. So basically what it means is a lot of people are giving Puppet a shot, but it's starting to churn, which means it's not as sticky as we would like. One that was surprising on here for me was Tanium. It's kind of jumbled in there. It's hard to see in the middle, but Tanium, I was very surprised to see as high of a churn because what I do hear from our end user community is that people that use it, like it. It really kind of spreads into not only vulnerability management, but also that endpoint detection and response side. So I was surprised by that one, mostly to see Tanium in here. Mural, again, was another one of those application design softwares that's seeing a very high churn as well. >> So you're saying if you're in both... Alex, bring that back up if you would. So if you're in both like MariaDB is for example, I think, yeah, they're in both. They're both green in the previous one and red here, that's not as bad. You mentioned Rubrik is going to be in both. Cohesity is a bit of a concern. Cohesity just brought on Sanjay Poonen. So this could be a go to market issue, right? I mean, 'cause Cohesity has got a great product and they got really happy customers. So they're just maybe having to figure out, okay, what's the right ideal customer profile and Sanjay Poonen, I guarantee, is going to have that company cranking. I mean they had been doing very well on the surveys and had fallen off of a bit. The other interesting things wondering the previous survey I saw Cvent, which is an event platform. My only reason I pay attention to that is 'cause we actually have an event platform. We don't sell it separately. We bundle it as part of our offerings. And you see Hopin on here. Hopin raised a billion dollars during the pandemic. And we were like, "Wow, that's going to blow up." And so you see Hopin on the churn and you didn't see 'em in the previous chart, but that's sort of interesting. Like you said, let's not kind of dwell on the negative, but you really don't. You know, churn is a real big concern. Okay, now we're going to drill down into two sectors, security and data. Where data comprises three areas, database and data warehousing, machine learning and AI and big data analytics. So first let's take a look at the security sector. Now this is interesting because not only is it a sector drill down, but also gives an indicator of how much money the firm has raised, which is the size of that bubble. And to tell us if a company is punching above its weight and efficiently using its venture capital. Erik, take us through this slide. Explain the dots, the size of the dots. Set this up please. >> Yeah. So again, the axis is still the same, net sentiment and mindshare, but what we've done this time is we've taken publicly available information on how much capital company is raised and that'll be the size of the circle you see around the name. And then whether it's green or red is basically saying relative to the amount of money they've raised, how are they doing in our data? So when you see a Netskope, which has been around forever, raised a lot of money, that's why you're going to see them more leading towards red, 'cause it's just been around forever and kind of would expect it. Versus a name like SecurityScorecard, which is only raised a little bit of money and it's actually performing just as well, if not better than a name, like a Netskope. OneTrust doing absolutely incredible right now. BeyondTrust. We've seen the issues with Okta, right. So those are two names that play in that space that obviously are probably getting some looks about what's going on right now. Wiz, we've all heard about right? So raised a ton of money. It's doing well on net sentiment, but the mindshare isn't as well as you'd want, which is why you're going to see a little bit of that red versus a name like Aqua, which is doing container and application security. And hasn't raised as much money, but is really neck and neck with a name like Wiz. So that is why on a relative basis, you'll see that more green. As we all know, information security is never going away. But as we'll get to later in the program, Dave, I'm not sure in this current market environment, if people are as willing to do POCs and switch away from their security provider, right. There's a little bit of tepidness out there, a little trepidation. So right now we're seeing overall a slight pause, a slight cooling in overall evaluations on the security side versus historical levels a year ago. >> Now let's stay on here for a second. So a couple things I want to point out. So it's interesting. Now Snyk has raised over, I think $800 million but you can see them, they're high on the vertical and the horizontal, but now compare that to Lacework. It's hard to see, but they're kind of buried in the middle there. That's the biggest dot in this whole thing. I think I'm interpreting this correctly. They've raised over a billion dollars. It's a Mike Speiser company. He was the founding investor in Snowflake. So people watch that very closely, but that's an example of where they're not punching above their weight. They recently had a layoff and they got to fine tune things, but I'm still confident they they're going to do well. 'Cause they're approaching security as a data problem, which is probably people having trouble getting their arms around that. And then again, I see Arctic Wolf. They're not red, they're not green, but they've raised fair amount of money, but it's showing up to the right and decent level there. And a couple of the other ones that you mentioned, Netskope. Yeah, they've raised a lot of money, but they're actually performing where you want. What you don't want is where Lacework is, right. They've got some work to do to really take advantage of the money that they raised last November and prior to that. >> Yeah, if you're seeing that more neutral color, like you're calling out with an Arctic Wolf, like that means relative to their peers, this is where they should be. It's when you're seeing that red on a Lacework where we all know, wow, you raised a ton of money and your mindshare isn't where it should be. Your net sentiment is not where it should be comparatively. And then you see these great standouts, like Salt Security and SecurityScorecard and Abnormal. You know they haven't raised that much money yet, but their net sentiment's higher and their mindshare's doing well. So those basically in a nutshell, if you're a PE or a VC and you see a small green circle, then you're doing well, then it means you made a good investment. >> Some of these guys, I don't know, but you see these small green circles. Those are the ones you want to start digging into and maybe help them catch a wave. Okay, let's get into the data discussion. And again, three areas, database slash data warehousing, big data analytics and ML AI. First, we're going to look at the database sector. So Alex, thank you for bringing that up. Alright, take us through this, Erik. Actually, let me just say Postgres SQL. I got to ask you about this. It shows some funding, but that actually could be a mix of EDB, the company that commercializes Postgres and Postgres the open source database, which is a transaction system and kind of an open source Oracle. You see MariaDB is a database, but open source database. But the companies they've raised over $200 million and they filed an S-4. So Erik looks like this might be a little bit of mashup of companies and open source products. Help us understand this. >> Yeah, it's tough when you start dealing with the open source side and I'll be honest with you, there is a little bit of a mashup here. There are certain names here that are a hundred percent for profit companies. And then there are others that are obviously open source based like Redis is open source, but Redis Labs is the one trying to monetize the support around it. So you're a hundred percent accurate on this slide. I think one of the things here that's important to note though, is just how important open source is to data. If you're going to be going to any of these areas, it's going to be open source based to begin with. And Neo4j is one I want to call out here. It's not one everyone's familiar with, but it's basically geographical charting database, which is a name that we're seeing on a net sentiment side actually really, really high. When you think about it's the third overall net sentiment for a niche database play. It's not as big on the mindshare 'cause it's use cases aren't as often, but third biggest play on net sentiment. I found really interesting on this slide. >> And again, so MariaDB, as I said, they filed an S-4 I think $50 million in revenue, that might even be ARR. So they're not huge, but they're getting there. And by the way, MariaDB, if you don't know, was the company that was formed the day that Oracle bought Sun in which they got MySQL and MariaDB has done a really good job of replacing a lot of MySQL instances. Oracle has responded with MySQL HeatWave, which was kind of the Oracle version of MySQL. So there's some interesting battles going on there. If you think about the LAMP stack, the M in the LAMP stack was MySQL. And so now it's all MariaDB replacing that MySQL for a large part. And then you see again, the red, you know, you got to have some concerns about there. Aerospike's been around for a long time. SingleStore changed their name a couple years ago, last year. Yellowbrick Data, Fire Bolt was kind of going after Snowflake for a while, but yeah, you want to get out of that red zone. So they got some work to do. >> And Dave, real quick for the people that aren't aware, I just want to let them know that we can cut this data with the public company data as well. So we can cross over this with that because some of these names are competing with the larger public company names as well. So we can go ahead and cross reference like a MariaDB with a Mongo, for instance, or of something of that nature. So it's not in this slide, but at another point we can certainly explain on a relative basis how these private names are doing compared to the other ones as well. >> All right, let's take a quick look at analytics. Alex, bring that up if you would. Go ahead, Erik. >> Yeah, I mean, essentially here, I can't see it on my screen, my apologies. I just kind of went to blank on that. So gimme one second to catch up. >> So I could set it up while you're doing that. You got Grafana up and to the right. I mean, this is huge right. >> Got it thank you. I lost my screen there for a second. Yep. Again, open source name Grafana, absolutely up and to the right. But as we know, Grafana Labs is actually picking up a lot of speed based on Grafana, of course. And I think we might actually hear some noise from them coming this year. The names that are actually a little bit more disappointing than I want to call out are names like ThoughtSpot. It's been around forever. Their mindshare of course is second best here but based on the amount of time they've been around and the amount of money they've raised, it's not actually outperforming the way it should be. We're seeing Moogsoft obviously make some waves. That's very high net sentiment for that company. It's, you know, what, third, fourth position overall in this entire area, Another name like Fivetran, Matillion is doing well. Fivetran, even though it's got a high net sentiment, again, it's raised so much money that we would've expected a little bit more at this point. I know you know this space extremely well, but basically what we're looking at here and to the bottom left, you're going to see some names with a lot of red, large circles that really just aren't performing that well. InfluxData, however, second highest net sentiment. And it's really pretty early on in this stage and the feedback we're getting on this name is the use cases are great, the efficacy's great. And I think it's one to watch out for. >> InfluxData, time series database. The other interesting things I just noticed here, you got Tamer on here, which is that little small green. Those are the ones we were saying before, look for those guys. They might be some of the interesting companies out there and then observe Jeremy Burton's company. They do observability on top of Snowflake, not green, but kind of in that gray. So that's kind of cool. Monte Carlo is another one, they're sort of slightly green. They are doing some really interesting things in data and data mesh. So yeah, okay. So I can spend all day on this stuff, Erik, phenomenal data. I got to get back and really dig in. Let's end with machine learning and AI. Now this chart it's similar in its dimensions, of course, except for the money raised. We're not showing that size of the bubble, but AI is so hot. We wanted to cover that here, Erik, explain this please. Why TensorFlow is highlighted and walk us through this chart. >> Yeah, it's funny yet again, right? Another open source name, TensorFlow being up there. And I just want to explain, we do break out machine learning, AI is its own sector. A lot of this of course really is intertwined with the data side, but it is on its own area. And one of the things I think that's most important here to break out is Databricks. We started to cover Databricks in machine learning, AI. That company has grown into much, much more than that. So I do want to state to you Dave, and also the audience out there that moving forward, we're going to be moving Databricks out of only the MA/AI into other sectors. So we can kind of value them against their peers a little bit better. But in this instance, you could just see how dominant they are in this area. And one thing that's not here, but I do want to point out is that we have the ability to break this down by industry vertical, organization size. And when I break this down into Fortune 500 and Fortune 1000, both Databricks and Tensorflow are even better than you see here. So it's quite interesting to see that the names that are succeeding are also succeeding with the largest organizations in the world. And as we know, large organizations means large budgets. So this is one area that I just thought was really interesting to point out that as we break it down, the data by vertical, these two names still are the outstanding players. >> I just also want to call it H2O.ai. They're getting a lot of buzz in the marketplace and I'm seeing them a lot more. Anaconda, another one. Dataiku consistently popping up. DataRobot is also interesting because all the kerfuffle that's going on there. The Cube guy, Cube alum, Chris Lynch stepped down as executive chairman. All this stuff came out about how the executives were taking money off the table and didn't allow the employees to participate in that money raising deal. So that's pissed a lot of people off. And so they're now going through some kind of uncomfortable things, which is unfortunate because DataRobot, I noticed, we haven't covered them that much in "Breaking Analysis", but I've noticed them oftentimes, Erik, in the surveys doing really well. So you would think that company has a lot of potential. But yeah, it's an important space that we're going to continue to watch. Let me ask you Erik, can you contextualize this from a time series standpoint? I mean, how is this changed over time? >> Yeah, again, not show here, but in the data. I'm sorry, go ahead. >> No, I'm sorry. What I meant, I should have interjected. In other words, you would think in a downturn that these emerging companies would be less interesting to buyers 'cause they're more risky. What have you seen? >> Yeah, and it was interesting before we went live, you and I were having this conversation about "Is the downturn stopping people from evaluating these private companies or not," right. In a larger sense, that's really what we're doing here. How are these private companies doing when it comes down to the actual practitioners? The people with the budget, the people with the decision making. And so what I did is, we have historical data as you know, I went back to the Emerging Technology Survey we did in November of 21, right at the crest right before the market started to really fall and everything kind of started to fall apart there. And what I noticed is on the security side, very much so, we're seeing less evaluations than we were in November 21. So I broke it down. On cloud security, net sentiment went from 21% to 16% from November '21. That's a pretty big drop. And again, that sentiment is our one aggregate metric for overall positivity, meaning utilization and actual evaluation of the name. Again in database, we saw it drop a little bit from 19% to 13%. However, in analytics we actually saw it stay steady. So it's pretty interesting that yes, cloud security and security in general is always going to be important. But right now we're seeing less overall net sentiment in that space. But within analytics, we're seeing steady with growing mindshare. And also to your point earlier in machine learning, AI, we're seeing steady net sentiment and mindshare has grown a whopping 25% to 30%. So despite the downturn, we're seeing more awareness of these companies in analytics and machine learning and a steady, actual utilization of them. I can't say the same in security and database. They're actually shrinking a little bit since the end of last year. >> You know it's interesting, we were on a round table, Erik does these round tables with CISOs and CIOs, and I remember one time you had asked the question, "How do you think about some of these emerging tech companies?" And one of the executives said, "I always include somebody in the bottom left of the Gartner Magic Quadrant in my RFPs. I think he said, "That's how I found," I don't know, it was Zscaler or something like that years before anybody ever knew of them "Because they're going to help me get to the next level." So it's interesting to see Erik in these sectors, how they're holding up in many cases. >> Yeah. It's a very important part for the actual IT practitioners themselves. There's always contracts coming up and you always have to worry about your next round of negotiations. And that's one of the roles these guys play. You have to do a POC when contracts come up, but it's also their job to stay on top of the new technology. You can't fall behind. Like everyone's a software company. Now everyone's a tech company, no matter what you're doing. So these guys have to stay in on top of it. And that's what this ETS can do. You can go in here and look and say, "All right, I'm going to evaluate their technology," and it could be twofold. It might be that you're ready to upgrade your technology and they're actually pushing the envelope or it simply might be I'm using them as a negotiation ploy. So when I go back to the big guy who I have full intentions of writing that contract to, at least I have some negotiation leverage. >> Erik, we got to leave it there. I could spend all day. I'm going to definitely dig into this on my own time. Thank you for introducing this, really appreciate your time today. >> I always enjoy it, Dave and I hope everyone out there has a great holiday weekend. Enjoy the rest of the summer. And, you know, I love to talk data. So anytime you want, just point the camera on me and I'll start talking data. >> You got it. I also want to thank the team at ETR, not only Erik, but Darren Bramen who's a data scientist, really helped prepare this data, the entire team over at ETR. I cannot tell you how much additional data there is. We are just scratching the surface in this "Breaking Analysis". So great job guys. I want to thank Alex Myerson. Who's on production and he manages the podcast. Ken Shifman as well, who's just coming back from VMware Explore. Kristen Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hof is our editor in chief over at SiliconANGLE. Does some great editing for us. Thank you. All of you guys. Remember these episodes, they're all available as podcast, wherever you listen. All you got to do is just search "Breaking Analysis" podcast. I publish each week on wikibon.com and siliconangle.com. Or you can email me to get in touch david.vellante@siliconangle.com. You can DM me at dvellante or comment on my LinkedIn posts and please do check out etr.ai for the best survey data in the enterprise tech business. This is Dave Vellante for Erik Bradley and The Cube Insights powered by ETR. Thanks for watching. Be well. And we'll see you next time on "Breaking Analysis". (upbeat music)

(upbeat music) >> Welcome everyone to theCUBE presentation of the AWS Startup Showcase, the theme here is Data as Code. This is season two, episode two of our ongoing series covering the exciting startups from the AWS ecosystem. And talk about the future of data, future of analytics, the future of development and all kind of cool stuff in Multicloud. I'm your host, John Furrier. Today we're joined by Ed Bailey, Senior Technology, Technical Evangelist at Cribl. Thanks for coming on the queue here. >> I thank you for the invitation, thrilled to be here. >> The theme of this session is the observability lake, which I love by the way I'm getting into that in a second. A breach investigation's best friend, which is a great topic. Couple of things, one, I like the breach investigation angle, but I also like this observability lake positioning, because I think this is a teaser of what's coming, more and more data usage where it's actually being applied specifically for things here, it's observability lake. So first, what is an observability lake? Why is it important? >> Why it's important is technology professionals, especially security professionals need data to make decisions. They need data to drive better decisions. They need data to understand, just to achieve understanding. And that means they need everything. They don't need what they can afford to store. They don't need not what vendor is going to let them store. They need everything. And I think as a point of the observability lake, because you couple an observability pipeline with the lake to bring your enterprise of data, to make it accessible for analytics, to be able to use it, to be able to get value from it. And I think that's one of the things that's missing right now in the enterprises. Admins are being forced to make decisions about, okay, we can't afford to keep this, we can afford to keep this, they're missing things. They're missing parts of the picture. And by bringing, able to bring it together, to be able to have your cake and eat it too, where I can get what I need and I can do it affordably is just, I think that's the future, and it just drives value for everyone. >> And it just makes a lot of sense data lake or the earlier concert, throw everything into the lake, and you can figure it out, you can query it, you can take action on it real time, you can stream it. You can do all kinds of things with it. Verb observability is important because it's the most critical thing people are doing right now for all kinds of things from QA, administration, security. So this is where the breach piece comes in. I like that's part of the talk because the breached investigation's best friend, it implies that you got the secret sourced to behind it, right? So, what is the state of the breach investigation today? What's going on with that? Because we know breaches, we see 'em out there, but like, why is this the best friend of a breach investigator? >> Well, and this is unfortunate, but typically there's an enormous delay between breach and detection. And right now, there's an IBM study, I think it's 287 days, but from the actual breach to detection and containment. It's an enormous amount of time. And the key is so when you do detect a breach, you're bringing in your instant, your response team, and typically without an observability lake, without Cribl solutions around observability pipeline, you're going to have an incomplete picture. The incident response team has to first to understand what's the scope of the breach. Is it one server? Is it three servers? Is it all the servers? You got to understand what's been compromised, what's been the end, what's the impact? How did the breach occur in the first place? And they need all the data to stitch that together, and they need it quickly. The more time it takes to get that data, the more time it takes for them to finish their analysis and contain the breach. I mean, hence the, I think about an 87, 90 days to contain a breach. And so by being able to remove the friction, by able to make it easier to achieve these goals, what shouldn't be hard, but making, by removing that friction, you speed up the containment and resolution time. Not to mention for many system administrators, they don't simply have the data because they can afford to store the data in their SIEM. Or they have to go to their backup team to get a restore which can take days. And so that's-- It's just so many obstacles to getting resolution right now. >> I mean, it's just, you're crawling through glass there, right? Because you think about it like just the timing aspect. Where is the data? Where is it stored and relevant and-- >> And do you have it at all? >> And you have it at all, and then, you know, that person doesn't work anywhere, they change jobs. I mean, who is keeping track of all this? You guys have now, this capability where you can come in and do the instrumentation with the observability lake without a lot of change to the environment, which is not the way it used to be. Used to be, buy a tool, build a platform. Cribl has a solution that eases the struggles with the enterprise. What specifically is that pain point? And what do you guys do specifically? >> Well, I'll start out with kind of example, what drew me to Cribl, so back in 2018. I'm running the Splunk team for a very large multinational. The complexity of that, we were dealing with the complexity of the data, the demands we were getting from security and operations were just an enormous issue to overcome. I had vendors come to me all the time that will solve your problems, but that means you got to move to our platform where you have to get rid of Splunk or you have to do this, and I'm losing something. And what Cribl stream brought into, was I could put it between my sources and my destinations and manage my data. And I would have flow control over the data. I don't have to lose anything. I could keep continuing use our existing analytics tools, and that sense of power and control, and I don't have to lose anything. I was like, there's something wrong here. This is too good to be true. And so what we're talking about now in terms of breach investigation, is that with Cribl stream, I can create a clone of my data to an object store. So this is in, this is almost any object store. So it can be AWS, it could be the other vendor object stores. It could be on-prem object stores. And then I can house my data, I can house all my data at the cheapest possible price. So instead of eating up my most expensive storage, I put all my data in my object store. And I only put the data I need for the detections in my SIEM. So if, and hopefully never, but if you do have a breach, lock stream has a wonderful UI that makes a trivial to then pick my data out of my object store and restore it back into my SIEM so that my IR team has to develop a complete picture of how the breach happen. What's the scope? What is their lateral movement and answer those questions. And it just, it takes the friction away. Just like you said, just no more crawling over glass. You're running to your solution. >> You mentioned object store, and you're streaming that in. You talk about the Cribble stream tool. I'm assuming there when you're streaming the pipeline stuff, but is there a schema involved? Is there database challenges? What, how do you guys look at that? I know you're vendor agnostic. I like that piece, you plug in and you leverage all the tools that are out there, Splunk, Datadog, whatever. But how about on the database side, what's the impact there? >> Well, so I'm assuming you're talking about the object store itself, so we don't have to apply the schema. We can fit the data to whichever the object store is. We structure the data so it makes it easier to understand. For example, if I want to see communications from one IP to another IP, we structure it to make it easier to see that and query that, but it is just, we're-- Yeah, it's completely vendor neutral and this makes it so simple, so simple to enable, I think-- >> So no pre-defined schema needed. >> No, not at all. And this, it made it so much easier. I think we enabled this for the enterprise. I think it took us three hours to do, and we were able to then start, I mean, start cutting our retention costs dramatically. >> Yeah, it's great when you get that kind of value, time to value critical and all the skeptics fall to the sides pretty quickly. (chuckles) I got to ask you, well, go ahead. >> So I say, I mean, previously, I would have to go to our backup team. We'd have to open up a ticket, we'd have to have a bridge, then we'd have to go through the process of pulling tape and being, it could take, you know, hours, hours if not days to restore the amount of data we needed. And just it, you know, we were able to run to our goals, and solve business problems instead of focusing on the process steps of getting things done. >> Right, so take me through the architecture here and some customer examples, 'cause you have the Cribble streaming there, observability pipeline. That's key, you mentioned that. >> Yes. >> And then they build out these observability lakes from that. So what is the impact of that? Can you share the customers that are using that solution? What are they seeing for benefits? What are some of the impact? Can you give us some specifics? >> I mean, I can't share with all the exact customer names. I can definitely give you some examples. Like referenceable conference would be TransUnion, so that I came from TransUnion. I was one of the first customers and it solved enormous number of problems for us. Autodesk is another great example. The idea that we're able to automate and data practices. I mean, just for example, what we were talking about with backups. We'd have to, you have to put a lot of time into managing your backups in your inner analytics platforms, you have to. And then you're locked into custom database schemas, you're locked into vendors. And it's also, it's still, it's expensive. So being able to spend a few hours, dramatically cut your costs, but still have the data available, and that's the key. I didn't have to make compromises, 'cause before I was having to say, okay, we're going to keep this, we're going to just drop this and hope for the best. And we just don't, we just didn't have to do that anymore. I think for the same thing for TransUnion and Autodesk, the idea that we're going to lower our cost, we're going to make it easier for our administrators to do their job and so they can spend more time on business value fundamentals, like responding to a breach. You're going to spend time working with your teams, getting value observability solutions and stop spending time on writing custom solutions using to open source tools. 'Cause your engineering time is the most precious asset for any enterprise and you got to focus your engineering time on where it's needed the most. >> Yeah, and they can't underestimate the hassle and cost of ownership, of swapping out pre-existing stuff, just for the sake of having a functionality. I mean that's a big-- >> It's pain and that's a big thing about lock stream is that being vendor neutral is so important. If you want to use the Splunk universal forwarder, that's great. If you want to use Beats, that's awesome. If you want to use Fluentd, even better. If you want to use all three, you can do that too. It's the customer choice and we're saying to people, use what suits your needs. And if you want to write some of your data to elastic, that's great. Some of your data to Splunk, that's even better. Some of it to, pick your pick, fine as well or Exabeam. You have the choices to put together, put your own solutions together and put your data where you need it to be. We're not asking you only in our ecosystem to work with only our partners. We're letting you pick and choose what suits your business. >> Yeah, you know, that's the direction I was just talking about the Amazon folks around their serverless. You know, you can use any tool, you know, you can, they have that core architecture for everything, the S3 and then pick whatever you want to use. SageMaker, just that other thing. This is the new way. That's the way it has to be to be effective. How do you guys handle that? What's been the reaction from customers? Do they like, roll their eyes and doubt you guys, or can you do it? Are they skeptical? How fast can you convert 'em over? (chuckles) >> Right, and that's always the challenge. And that's, I mean, the best part of my day is talking to customers. I love hearing and feedback, what they like, what they don't and what they need. And of course I was skeptical. I didn't believe it when I first saw it because I was like this, you know, because I'm, I was used to being locked in. I was used to having to put a lot of effort, a lot of custom code, like, what do you mean? It's this easy? I believe I did the first, this is 2018, and I did our first demos, like 30 minutes in, and I cut about 1/2 million dollars out of our license in the first 30 minutes in our first demo. And I was stunned because I mean, it's like, this is easy. >> Yeah, I mean-- >> Yeah, exactly. I mean, this is, and then this is the future. And then for example, we needed to bring in so like the security team wanted to bring in a UBA solution that wasn't part of the vendor ecosystem that we were in. And I was like, not a problem. We're going to use log stream. We're going to clone a copy of our data to the UBA solution. We were able to get value from this UBA solution in weeks. What typically is a six month cycle to start getting value. And it just, it was just too easy and the best part of it. And the thing is, it just struck me was my engineers can now spend their time on delivering value instead of integrations and moving data around. >> Yeah, and also we can spend more time preventing breaches. But what's interesting is counterintuitive here is that, if you, as you add more flexibility and choice, you'd think it'd be harder to handle a breach, right? So, now let's go back to the scenario. Now you guys, say an organization has a breach, and they have the observability pipeline, They got the lake in place, your observability lake, take me through the investigation. How easy is it, what happens? How they start it, what goes on? >> So, once your SOC detects a breach, then they bring in the idea. Typically you're going to bring in your incident response team. So what we did, and this is one more way that we removed that friction, we cleaned up the glass, is we delegate to the instant response team, the ability to restore, we call it-- So if Cribl calls it replay, we play data at our object store back into your SIEM. There's a very nice UI that gives you the ability to say, "I want data from this time period, at this time period, I want it to be all the data." Or the ability to filter and say, "I want this, just this IP." For example, if I detected, okay, this IP has been breached then I'm going to pull all the data that mentions this IP and this timeframe, hit a button and it just starts. And then it's going to restore how as fast your IOPS are for your solution. And then it's back in your tool, it's back in your tool. One of the things I also want to mention is we have an amazing enrichment capability. So one of the things that we would do is we would've pipelines so as the data comes out of the object store, it hits the pipeline, and then we enrich it. We hit use GoIP information, perverse and NAS. It gets processed through threat Intel feed. So the data's already enriched and ready for the incident response people to do their job. And so it just, it bamboozle the friction of getting to the point where I can start doing my job. >> You know, at this theme, this episode for this showcase is about Data as Code. And which is, you know, we've been, I've been saying this on theCUBES for since it was being around 13 years ago, that developers are going to be dealing with data like they deal with software code, and you're starting to see, you mentioned enrichment. Where do you see Data as Code going? How relevant in it now, because we really talking about when you add machine learning in here, that has to be enriched, and iterated on too. We're talking about taking things off a branch and putting it back into the core. This is a data discussion, this isn't software, but it sounds the same. >> Right, and this is something that the irony is that, I remember first time saying it to an auditor. I was constantly going with auditors, and that's what I described is I'm going to show you the code that manages the data. This is the data's code that's going to show you how we transform it, how we secure it, where the data goes, how it's enriched. So you can see the whole story, the data life cycle in one place. And that's how we handled our orders. And I think that is enormously, you know, positive because it's so easy to be confused. It's so easy to have complexity to get in the way of progress. And by being able to represent your Data as Code, it's a step forward 'cause the amount of data and the complexity of data, it's not getting simpler, it's getting more complex. So we need to come up with better ways to handle it. >> Now you've been on both sides of the fence. You've been in the trenches as customer, now you're a supplier with Great Solution. What are people doing with this data engineering roles? Because it's not enough data engineering. I mean, 'cause if you say Data as Code, if you believe that to be true and many people do, we do. And you looked at the history of infrastructure risk code that enabled DevOps, AIOps, MLOps, DataOps, it's happening, right? So data stack ops is coming. Obviously security is huge in this. How does that data engineering role evolve? Because it just seems more and more that there's going to be a big push towards an SRE version of data, right? >> I completely agree. I was working with a customer yesterday, and I spent a large part of our conversation talking about implementing development practices for administrators. It's a new role. It's a new way to think of things 'cause traditionally your Splunk or elastic administrators is talking about operating systems and memory and talking about how to use proprietary tools in the vendor, that's just not quite the same. And so we started talking about, you need to have, you need to start getting used to code reviews. Yeah, the idea of getting used to making sure everything has a comment, was one thing I told him was like, you know, if you have a function has to have a comment, just by default, just it has to. Yeah, the standards of how you write things, how you name things all really start to matter. And also you got to start adding, considering your skillset. And this is some mean probably one of the best hire I ever made was I hired a guy with a math degree, because I needed his help to understand how do machine learning works, how to pick the best type of algorithm. And I think this is going to evolve, that you're going to be just away from the gray bearded administrator to some other gray bearded administrator with a math degree. >> It's interesting, it's a step function. You have a data engineer who's got that kind of capabilities, like what the SRA did with infrastructure. The step function of enablement, the value creation from really good data engineering, puts the democratization playback on the table, and changes, >> Thank you very much John. >> And changes that entire landscape. How do you, what's your reaction to that? >> I completely agree 'cause so operational data. So operational security data is the most volatile data in the enterprise. It changes on a whim, you have developers who change things. They don't tell you what happens, vendor doesn't tell you what happened, and so that idea, that life cycle of managing data. So the same types of standards of disciplines that database administrators have done for years is going to have, it has to filter down into the operational areas, and you need tooling that's going to give you the ability to manage that data, manage it in flight in real time, in order to drive detections, in order to drive response. All those business value things we've been talking about. >> So I got to ask you the larger role that you see with observability lakes we were talking before we came on camera live here about how exciting this kind of concept is, and you were attracted to the company because of it. I love the observability lake concept because it puts all that data in one spot, you can manage it. But you got machine learning in AI around the corner that also can help. How has all this changed in the landscape of data security and things because it makes a lot of sense, and I can only see it getting better with machine learning. >> Yeah, definitely does. >> Totally, and so the core issue, and I don't want to say, so when you talk about observability, most people have assumptions around observability is only an operational or an application support process. It's also security process. The idea that you're looking for your unknown, unknowns. This is what keeps security administrators up at night is I'm being attacked by something I don't know about. How do you find those unknown? And that's where your machine learning comes in. And that's where that you have to understand there's so many different types of machine learning algorithms, where the guy that I hired, I mean, had started educating me about the umpteen number of algorithms and how it applies to different data and how you get different value, how you have to test your data constantly. There's no such thing as the magical black box of machine learning that gives you value. You have to implement, but just like the developer practices to keep testing and over and over again, data scientists, for example. >> The best friend of a machine learning algorithm is data, right? You got to keep feeding that data, and when the data sets are baked and secure and vetted, even better, all cool. Had great stuff, great insight. Congratulations Cribl, Great Solution. Love the architecture, love the pipelining of the observability data and streaming that in to a lake. Great stuff. Give a plug for the company where you guys are at, where people can get information. I know you guys got a bunch of live feeds on YouTube, Twitch, here in theCUBE. Where else can people find you? Give the plug. >> Oh, please, please join our slack community, go to cribl.io/community. We have an amazing community. This was another thing that drew me to the company is have a large group of people who are genuinely excited about data, about managing data. If you want to try Cribl out, we have some great tool. Try Cribl tools out. We have a cloud platform, one terabyte up free data. So go to cribl.io/cloud or cribl.cloud, sign up for, you know, just never times out. You're not 30 day, it's forever up to one terabyte. Try out our new products as well, Cribl Edge. And then finally come watch Nick Decker and I, every Thursday, 2:00 PM Eastern. We have live streams on Twitter, LinkedIn and YouTube live. And so just my Twitter handle is EBA 1367. Love to have, love to chat, love to have these conversations. And also, we are hiring. >> All right, good stuff. Great team, great concepts, right? Of course, we're theCUBE here. We got our video lake coming on soon. I think I love this idea of having these video. Hey, videos data too, right? I mean, we've got to keep coming to you. >> I love it, I love videos, it's awesome. It's a great way to communicate, it's a great way to have a conversation. That's the best thing about us, having conversations. I appreciate your time. >> Thank you so much, Ed, for representing Cribl here on the Data as Code. This is season two episode two of the ongoing series covering the hottest, most exciting startups from the AWS ecosystem. Talking about the future data, I'm John Furrier, your host. Thanks for watching. >> Ed: All right, thank you. (slow upbeat music)

>> Narrator: From theCube studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is theCube Conversation. >> Hey, welcome back, everybody. Jeff Frick here with theCube. We're still on our Palo Alto studios, we're still getting through COVID and we're still doing all of our remotes, all of our interviews via remote and I'm really excited to have a guest we had around a long time ago. I looked it up is 2016, April 2016. She's Coco Brown, the founder and CEO of the Athena Alliance. Coco, it's great to see you. >> It's great to see you as well. We actually formally started in April of 2016. >> I know, I saw, I noticed that on LinkedIn. So we were at the Girls in Tech Catalyst Conference in Phoenix, I remembers was a really cool conference, met a ton of people, a lot of them have turned out that are on your board. So yeah, and you formally on LinkedIn, it says you started in May. So that was right at the very, very beginning. >> Yeah, that's right. >> So for people that aren't familiar with the at the Athena Alliance give them the quick overview. >> Okay. Well, it's a little different that it was four years ago. So Athena first and foremost is a digital platform. So you literally log in to Athena. And we're a combination of community access to opportunity and learning. And so you can kind of envision it a little bit like a walled garden around the LinkedIn, meets Khan Academy for senior executives, meets Hollywood agency for women trying to get into the boardroom and senior level roles in the c-suite as advisors, et cetera. And then the way that we operate is you can have a self-service experience of Athena, you can have a concierge experience with Athena with real humans in the loop making key connections for you and you can add accelerators where we build brand packages and BIOS and give you executive coaching. So... >> Wow. >> Kind of a... >> You've built out your services portfolio over the last several years. But still the focus >> yes, we have. is boards, right? Still the focus is getting women on public boards, or is that no longer still the focus? >> No, that's a big piece of it for sure. I mean, one of the things that we discovered, that was the very first mission of Athena, was to bring more women into the boardroom. And as we were doing that we discovered that once you get into a senior realm of leadership in general, there's more things that you want to do than just get into the boardroom. Some of it may be wanting to be an investor or an LP in a fund or become a CEO, or certainly join outside boards but also be relevant to your own inside board. And so we started to look at Athena as a more holistic experience for senior leaders who are attempting to make sure that they are the best they can be in this very senior realm of overarching stewardship of business. >> Awesome. and have you seen, so obviously your your focus shifted 'cause you needed to add more services based on the demand from the customers. But have you seen the receptiveness to women board members change over the last four years? How have you seen kind of the marketplace change? >> Yeah, it's changed a lot, I would say. First of all I think laws like the California law and Goldman Sachs coming out saying they won't take companies public unless they have diverse board data. The statements by big entities that people are paying attention to made the boardroom dynamics a conversation around the dinner table in general. So it became more of a common conversation and common interest as opposed to just the interest of a few people who are trying to get in there. And so that's created a lot of momentum as well as sort of thoughtfulness from leaders and from employees and from larger stakeholders to say the diversity at the top business has to mimic the demographics of society as a whole. And that's become a little bit more accepted as opposed to grudgingly sort of taken in. >> Right. So one of the big problems always it's like the VC problem, right? Is the whole matchmaking problem. How do you, how do qualified people find qualified opportunities? And I wonder if you can speak a little bit as to how that process has evolved, how are you really helping because there's always people that are looking for quality candidates, and there's great quality candidates out there that just don't know where to go. How are you helping bridge kind of that kind of basic matchmaking function? >> Yeah. I mean, there's a couple of different ways to go about it. One is certainly to understand and have real connections into the parts of the leadership ecosystem that influences or makes the decision as to who sits around that table. So that would be communities of CEOs, it's communities of existing board directors, it's venture capital firms, its private equity firms, and as you get really entrenched in those organizations and those ecosystems, you become part of that ecosystem and you become what they turn to to say, "Hey, do you know somebody?" Because it still is a "who do yo know" approach at the senior most levels. So that's one way. The other mechanism is really for individuals who are looking for board seats who want to be on boards to actually be thinking about how they proactively navigate their way to the kinds of boards that they would fit to. I like in a very much to the way our children go after the schools that they might want to when it's time for university. You'll figure out who your safeties, your matches, your reaches are, and figure out how you're going to take six degrees of separation and turn them into one through connections. So those are that's another way to go about it. >> You know, it's interesting, I talked to Beth Stewart from True Star, they also help place women on boards. And one of the issues is just the turnover. And I asked that just straight up, are there formal mechanisms to make sure that people who've been doing business from way before there were things like email and the internet eventually get swapped out. And she said, that's actually a big part of the problem is there isn't really a formal way to keep things fresh and to kind of rotate the incumbents out to enable somebody who's new and maybe has a different point of view to come in. So I'm curious when someone is targeting their A-list and B-list and C-lists, how do they factor in kind of the age of the board composition of the existing board, to really look for where there's these opportunities where a spot opens up, 'cause if there's not a spot open up clearly, there's really not much opportunity there. >> Yeah, I mean, you have to look at the whole ecosystem, right? I mean, there's anything from let's say series A, venture backed private companies all the way up to the mega cap companies, right? And there's this continuum. And it's not, there's not one universal answer to what you're talking about. So for example, if you're talking about smaller private companies, you're competing against, not somebody giving up their seat, but whether or not the company feels real motivation to fill that particular independent director seat. So the biggest competition is often that that seat goes unfilled. When you're talking about public companies, the biggest competition is really the fact that as my friend Adam Epstein of the small cap Institute will tell you, that 80% of public companies are actually small cap companies. And they don't have the same kinds of pressures that large caps do to have turnover. But yeah, it takes a big piece of the challenge is really boards having the disposition collectively to see the board as a competitive advantage for the business as a very necessary and productive piece of the business and when they see that then they take more proactive measures to make sure they have a evolving and strong board that does turnover as it needs to. >> Right. So I'm curious when you're talking to the high power women, right, who are in operational roles probably most of the time, how do you help coach them, how should they be thinking, what do they have to do different when they want to kind of add board seats to their portfolio? Very different kind of a role than an operational role, very different kind of concerns and day to day tasks. So, and clearly, you've added a whole bunch of extra things to your portfolio. So how do you help people, what do you tell women who say, "Okay, I've been successful, "I'm like successful executive, "but now I want to do this other thing, "I want to take this next step in my career"? What usually the gaps and what are the things that they need to do to prepare for that? >> Well, I'm going to circle in then land a little bit. Autodesk was actually a really great partner to us back when you and I first met. They had a couple of women at the top of the organization that were part of Athena, specifically because they wanted to join boards. They are on boards now, Lisa Campbell, Amy Bunszel, Debbie Clifford. And what they told us is they were experiencing everything that we were offering in terms of developing them, helping them to position themselves, understand themselves, navigate their way, was that they simply became better leaders as a result of focusing on themselves as that next level up, irrespective of the fact that it took them two to three years to land that seat. They became stronger in their executive role in general and better able to communicate and engage with their own boards. So I think, now I'm landing, the thing that I would say about that is don't wait until you're thinking oh, I want to join a board, to do the work to get yourself into that ecosystem, into that atmosphere and into that mindset, because the sooner you do that as an executive, the better you will be in that atmosphere, the more prepared you will be. And you also have to recognize that it will take time. >> Right. And the how has COVID impacted it, I mean, on one hand, meeting somebody for coffee and having a face to face is a really important part of getting to know someone and a big part of I'm sure, what was the recruitment process, and do you know someone, yeah, let's go meet for a cup of coffee or dinner or whatever. Can't do that anymore, but we can all meet this way, we can all get on virtually and so in some ways, it's probably an enabler, which before you could grab an hour or you didn't have to fly cross-country or somebody didn't have to fly cross-country. So I'm kind of curious in this new reality, which is going to continue for some time. How has that impacted kind of people's ability to discover and get to know and build trust for these very very senior positions. >> HBR just came out with a really great article about the virtual board meeting. I don't know if you saw it but I can send you a link. I think that what I'm learning from board directors in general and leaders in general is that yes, there's things that make it difficult to engage remotely, but there's also a lot of benefit to being able to get comfortable with the virtual world. So it's certainly, particularly with COVID, with racial equity issues, with the uncertain economy, boards are having to meet more often and they're having, some are having weekly stand ups and those are facilitated by getting more and more comfortable with being virtual. And I think they're realizing that you don't have to press flesh, as they say, to actually build intimacy and real connection. And that's been a hold up, but I think as the top leadership gets to understand that and feel that for themselves, it becomes easier for them to adopt it throughout the organization that the virtual world is one we can really embrace, not just for a period of time. >> It's funny we had John Chambers on early on in this whole process, really talking about leadership and leading through transition. And he used the example, I think had been that day or maybe a couple days off from our interview where they had a board meeting, I think they were talking about some hamburger restaurant, and so they just delivered hamburgers to everybody's office and they had the board meeting. But that's really progressive for a board to actually be doing weekly stand ups. That really shows a pretty transformative way to manage the business and kind of what we think is the stodgy old traditional get together now and then, fly and then get some minutes and fly out, that's super progressive. >> Yeah. I mean, I was on three different board meetings this week with a company I'm on the board of in Minnesota. And we haven't seen each other in person in, I guess since January. (woman laughs) >> So final tips for women that want to make this this move, who, they've got some breathing space, they're not homeschooling the kids all day while they're trying to get their job done and trying to save their own business, but have some cycles and the capabilities. What do you tell them, where should they begin, how should they start thinking about, kind of taking on this additional responsibility and really professional growth in their life? >> Well, I mean, I think something very important for all of us to think about with regard to board service and in general as we get into a very senior level point in our careers at a managing and impact portfolio. People get into a senior point and they don't just want to be an executive for one company, they want to have a variety of ways that they're delivering impact, whether it's as an investor or as a board member or as other things as well as being an operator. And I think the misnomer is that people believe that you have to add them up and they, one plus one plus one equals three, and it's just not true. The truth is that when you add a board seat, when you add that other thing that you're doing it makes you better as a leader in general. Every board meeting I have with [Indistinct] gives me more than I bring back to Athena as an example. And so I think we tend to think of not being able to take on one more thing and I say that we all have a little more space than we think we have to take on the things we want to do. >> Right? That's a good message to me. It is often said if you want to get something done, give it to the busiest person in the room. It's more likely to get it done 'cause you got to be efficient and you just have that kind of get it done attitude. >> That's right. >> All right, Coco. Well, thank you for sharing your thoughts. >> Congratulations, so I guess it's your four year anniversary, five year anniversary [Indistinct] about right? >> Yes, four. >> That's terrific. And we look forward to continuing to watch the growth and hopefully checking in face to face at some point in the not too distant future. >> I would like that. >> All right. Thanks a lot Coco. >> Great talking to you. >> Already. >> She's Coco, I'm Jeff. You're watching theCube. Thanks for watching, we'll see you next time. (upbeat music)

>> Announcer: Live from Las Vegas, it's theCUBE! Covering AWS re:Invent 2019. Brought to you by Amazon Web Services and Intel, along with its ecosystem partners. >> Hey welcome back, everyone, it's theCUBE's coverage here live in Las Vegas for re:Invent 2019, this is theCUBE's seventh year covering re:Invent, the event's only been going for eight years, it feels like a decade, so much growth, so much action, I'm John Furrier with my co-host Dave Vellante, here extracting the signal from the noise in the Intel AWS studio of theCUBE, thank you for that sponsorship. Mike Miller is our next guest, he's director of AI devices at AWS, super excited for this segment, because DeepRacer's here, and we got some music, AI is the front and center, great to see you again, thanks for coming on. >> Absolutely, thank you for having me on again, I appreciate it. >> All right, let's just jump right in, the toys. Developers are geeking out over DeepRacer and the toys you guys are putting out there as a fun way to play and learn. >> Absolutely, getting hands-on with these new broadly applicable machine learning technologies. >> Let's jump into DeepRacer, so first of all, give us a quick update on what's happened between last year and this year in the DeepRacer community, there's been a lot of froth, competitiveness, street battles, and then we'll get an update, give us a quick update on the community. >> So we launched DeepRacer last year as a 1/18 scale race car designed to teach reinforcement learning, so this thing drives by itself around the tracks. We've got an online experience where customers can train models, so we launched a DeepRacer league where we plan to visit 22 sites around the world at AWS summits, where developers can come visit us and race a car physically around a track, and we had online contests, so every month we had a new track for developers to be challenged by and race their cars around the track. We've seen tremendous engagement and excitement, a little bit of competition really gets developers' juices going. >> It's been a lot of fun, congratulations, by the way. >> Absolutely, thank you. >> All right, let's get into the new toy, so DeepRacer 2.0, whatever you're calling it, just DeepRacer-- >> DeepRacer Evo. >> Evo, okay. >> New generation, so we've basically provided more opportunities to race for developers, more challenges for them to learn, and more ways for them to win. So we integrated some new sensors on this car, so on top there's a LIDAR, which is a laser range finding device that can detect other cars or obstacles in the rear of the car and to the sides, and in the front of the car we have stereo cameras that we added so that the car can sense depth in front of it, so with those new sensors, developers can now be challenged by integrating depth sensing and object avoidance and head to head racing into their machine learning models. >> So currently it's not an obstacle course, correct, it's a race track, right? >> So we call it a time trial, so it's a single car on the track at a time, how fast can you make a lap, our world record actually is 7.44 seconds, set by a young lady from Tokyo this past year, really exciting. >> And she was holding up the trophy and said this is basically a dream come true. And so, what are they trying to optimize, is it just the speed at the turn, what are they sort of focused on? >> Yeah, it's a little bit of art and a little bit of science, so there's the reinforcement learning model that learns through what's called a reward function, so you give the car rewards for achieving specific objectives, or certain behaviors, and so it's really up to the developer to decide what kind of behaviors do they want to reward the car with, whether it's stay close to the center line, reduce the amount of turns, they can also determine its position on the track and so they can reward it for cutting corners close, speeding up or slowing down, so it's really a little bit of art and science through some experimentation and deciding. >> So we had Intel on yesterday, talking about some of their AI, Naveen Rao, great guy, but they were introducing this concept called GANs, Generative Adversarial Networks, which is kind of like neural network technology, lot of computer science in some of the tech here, this is not kiddie scripting kind of thing, this is like real deal. >> Yeah, so GANs actually formed the basis of the product that we just announced this year called DeepComposer, so DeepComposer is a keyboard and a cloud service designed to work together to teach developers about generative AI, and GANs are the technique that we teach developers. So what's interesting about generative AI is that machine learning moves from a predictions-based technology to something that can actually create new content, so create new music, new stories, new art, but also companies are using generative AI to do more practical things like take a sketch and turn it into a 3D model, or autocorrect colorize black and white photos, Autodesk even has a generative design product, where you can give, an industrial designer can give a product some constraints and it'll generate hundreds of ideas for the design. >> Now this is interesting to me, because I think this takes it to, I call basic machine learning, to really some more advanced practical examples, which is super exciting for people learning AI and machine learning. Can you talk about the composer and how it works, because pretend I'm just a musician, I'm 16 years old, I'm composing music, I got a keyboard, how can I get involved, what would be a path, do I buy a composer device, do I link it to Ableton Live, and these tools that are out there, there's a variety of different techniques, can you take us through the use case? >> Yeah, so really our target customer for this is an aspiring machine learning developer, maybe not necessarily a musician. So any developer, whether they have musical experience or machine learning background, can use the DeepComposer system to learn about the generative AI techniques. So GANs are comprised of these two networks that have to be trained in coordination, and what we do with DeepComposer is we walk users through or walk developers through exactly how to set up that structure, how these two things train, and how is it different from traditional machine learning where you've got a large data set, and you're training a single model to make a prediction. How do these multiple networks actually work against each other, and how do you make sure that they're generating new content that's actually of the right type of quality that you want, and so that's really the essence of the Generative Adversarial Networks and these two networks that work against each other. >> So a young musician who happens to like machine learning. >> So if I give this to my kid, he'll get hooked on machine learning? That's good for the college apps. >> Plug in his Looper and set two systems working together or against each other. >> When we start getting to visualization, that's going to be very interesting when you start getting the data at the fundamental level, now this is early days. Some would say day zero, because this is really early. How do you explain that to developers, and people you're trying to get attention to, because this is certainly exciting stuff, it's fun, playful, but it's got some nerd action in it, it's got some tech, what are some of the conversations you're having with folks when they say "Hey, how do I get involved, why should I get involved," and what's really going to be the impact, what's the result of all this? >> Yeah, well it's fascinating because through Amazon's 20 years of artificial intelligence investments, we've learned a lot, and we've got thousands of engineers working on artificial intelligence and machine learning, and what we want to do is try to take a lot of that knowledge and the experiences that those folks have learned through these years, and figure out how we can bring them to developers of all skill levels, so developers who don't know machine learning, through developers who might be data scientists and have some experience, we want to build tools that are engaging and tactile and actually tangible for them to learn and see the results of what machine learning can do, so in the DeepComposer case it's how do these generative networks actually create net new content, in this case music. For DeepRacer, how does reinforcement learning actually translate from a simulated environment to the real world, and how might that be applicable for, let's say, robotics applications? So it's really about reducing the learning curve and making it easy for developers to get started. >> But there is a bridge to real world applications in all this, it's a machine learning linchpin. >> Absolutely, and you can just look at all of the innovations that are being done from Amazon and from our customers, whether they're based on improving product recommendations, forecasting, streamlining supply chains, generating training data, all of these things are really practical applications. >> So what's happening at the device, and what's happening in the cloud, can you help us understand that? >> Sure, so in DeepComposer, the device is really just a way to input a signal, and in this case it's a MIDI signal, so MIDI is a digital audio format that allows machines to kind of understand music. So the keyboard allows you to input MIDI into the generative network, and then in the cloud, we've got the generative network takes that input, processes it, and then generates four-part accompaniments for the input that you provide, so say you play a little melody on the keyboard, we're going to generate a drum track, a guitar track, a keyboard track, maybe a synthesizer track, and let you play those back to hear how your input inspired the generation of this music. >> So GANs is a big deal with this. >> Absolutely, it forms the basis of the first technique that we're teaching using DeepComposer. >> All right, so I got to ask you the question that's on everyone's mind, including mine, what are some of the wackiest and/or coolest things you've seen this year with DeepComposer and DeepRacer because I can imagine developers' creativity straying off the reservation a little bit, any cool and wacky things you've seen? >> Well we've got some great stories of competitors in the DeepRacer league, so we've got father-son teams that come in and race at the New York summit, a 10 year old learning how to code with his dad. We had one competitor in the US was at our Santa Clara summit, tried again at our Atlanta summit, and then at the Chicago summit finally won a position to come back to re:Invent and race. Last year, we did the race here at re:Invent, and the winning time, the lap time, a single lap was 51 seconds, the current world record is 7.44 seconds and it's been just insane how these developers have been able to really optimize and generate models that drive this thing at incredible speeds around the track. >> I'm sure you've seen the movie Ford v Ferrari yet. You got to see that movie, because this DeepRacer, you're going to have to need a stadium soon, with eSports booming, this has got its own legs for its own business. >> Well we've got six tracks set up down at the MGM Grand Arena, so we've already got the arena set up, and that's where we're doing all the knock-out rounds and competitors. >> And you mentioned father-son, you remember when we were kids, Cub Scouts, I think it was, or Boy Scouts, whatever it was, you had the pinewood derby, right, you'd make a car and file down the nails that you use for the axles and, taking it to a whole new level here. >> It's a modern-day version. >> All right, Mike, thanks for coming on, appreciate it, let's keep in touch. If you can get us some of that B-roll for any video, I'd love to get some B-roll of some DeepRacer photos, send 'em our way, super excited, love what you're doing, I think this is a great way to make it fun, instructive, and certainly very relevant. >> Absolutely, that's what we're after. Thank you for having me. >> All right, theCUBE's coverage here, here in Las Vegas for our seventh, Amazon's eighth re:Invent, we're documenting history as the ecosystem evolves, as the industry wave is coming, IoT edge, lot of cool things happening, we're bringing it to you, we're back with more coverage after this short break. (techno music)

(upbeat music) >> Hi, everybody, we're back. This is Dave Vellante with theCUBE. We're here talking storage at Amazon in Boston. Asa Kalavade's here, she's the general manager for Hybrid and Data Transfer services. >> Let me give you a perspective of how these services come together. We have DataSync, Storage Gateway, and Transfer. As a set of Hybrid and Data Transfer services. The problem that we're trying to address for customers is how to connect their on premises infrastructure to the cloud. And we have customers at different stages of their journey to the cloud. Some are just starting out to use the cloud, some are migrating, and others have migrated, but they still need access to the cloud from on-prem. So the broad charter for these services is to enable customers to use AWS Storage from on-premises. So for example, DataStorage Gateway today is used by customers to get unlimited access to cloud storage from on-premises. And they can do that with low latency, so they can run their on-prem workloads, but still leverage storage in the cloud. In addition to that, we have DataSync, which we launched at re:Invent last year, in 2018. And DataSync essentially is designed to help customers move a lot of their on-premises storage to the cloud, and back and forth for workloads that involve replication, migration, or ongoing data transfers. So together, Gateway and DataSync help solve the access and transfer problem for customers. >> Let's double down on the benefits. You started the segment just sort of describing the problem that you're solving, connecting on-prem to cloud, sort of helping create these hybrid environments. So that's really the other benefit for customers, really simplifying that sort of hybrid approach, giving them high performance confidence that it actually worked. >> Maybe talk a little bit more about that. >> So with DataSync, we see two broad use cases. There is a class of customers that have adopted DataSync for migration. So we have customers like Autodesk who've migrated hundreds of terabytes from their on-premises storage to AWS. And that has allowed them to shut down their data center, or retire their existing storage, because they're on their journey to the cloud. The other class of use cases is customers that have ongoing data that they need to move to the cloud for a workload. So it could be data from video cameras, or gene sequencers that they need to move to a data pipeline in the cloud, and they can do further processing there. And in some cases, bring the results back. So that's the second continuous data transfer use case, that DataSync allows customers to address. >> You're also talking today, about Storage Gateway high availability version of Storage Gateway. What's behind that? >> Storage Gateway today is used by customers to get access to data in the cloud, from on-premises. So if we continue this migration story that I mentioned with DataSync, now you have a customer that has moved a large amount of data to the cloud. They can now access that same data from on-premises for latency reasons, or if they need to distribute data across organizations and so on. So that's where the Gateway comes into play. Today we have 10's of thousands of customers that are using Gateway to do their back-ups, do archiving, or in some cases, use it as a target to replace their on-premises storage, with cloud backed storage. So a lot of these customers are running business critical applications today. But then some of our customers have told us they want to do additional workloads that are uninterruptible. So they can not tolerate downtime. So with that requirement in mind, we are launching this new capability around high availability. And we're quite excited, because that's solving, yet allowing us to do even more workloads on the Gateway. This announcement will allow customers to have a highly available Gateway, in a VMware environment. With that, their workloads can continue running, even if one of the Gateways goes down, if they have a hardware failure, a networking event, or software error such as the file shares becoming unavailable. The Gateway automatically restarts, so the workloads remain uninterrupted. >> So talk a little bit more about how it works, just in terms of anything customers have to do, any prerequisites they have. How does it all fit? >> Customers can essentially use this in their VMware H.A. environment today. So they would deploy their Gateway much like they do today. They can download the Gateway from the AWS console. If they have an existing Gateway, the software gets updated so they can take advantage of the high availability feature as well. The Gateway integrates into the VMware H.A. environment. It builds up a number of health checks, so we keep monitoring for the application up-time, network up-time, and so on. And if there is an event, the health check gets communicated back to VMware, and the Gateway gets restarted within, in most typical cases, under 60 seconds. >> So customers that are VMware customers, can take advantage of this, and to them, it's very non disruptive it sounds like. That's one of the benefits. But maybe talk about some of the other benefits. >> We saw a large number of our on-premises customers, especially in the enterprise environments, use VMware today. And they're using VMware HA for a number of their other applications. So we wanted to plug into that environment so the Gateway is as well highly available. So all their applications just work in that same framework. And then along with high availability, we're also introducing two additional capabilities. One is real time reports and visibility into the Gateway's resource consumption. So customers can now see embedded cloud watch graphs on how is their storage being consumed, what's their cache utilization, what's the network utilization. And then the administrators can use that to, in fairly real time, adapt the resources that they've allocated to the Gateway. So with that, as their workloads change, they can continue to adapt their Gateway resources, so they're getting the maximum performance out of the Gateway. >> So if they see a performance problem, and it's a high priority, they can put more resources on it-- >> They can attach more storage to it, or move it to a higher resourced VM, and they can continue to get the performance they need. Previously they could still do that, but they had to have manual checks. Now this is all automated, we can get this in a single pane of control. And they can use the AWS console today, like they do for their in cloud workloads. They can use that to look at performance of their on-premises Gateway's as well. So it's one pane of control. They can get CloudWatch health reports on their infrastructure on-prem. >> And if course it's cloud, so I can assume this is a service, I pay for it when I used it, I don't have to install any infrastructure, right? >> So the Gateways, again, consumption based, much like all AWS services. You download the Gateway, it doesn't cost you anything. And we charge one cent per gigabyte of data transfer through the Gateway, and it's capped at $125 a month. And you just pay for whatever storage is consumed by the Gateway. >> When you talk to senior exec's like Andy Jassy, always says "We focus on the customers." And sometimes people roll their eyes, but it's true. This is a hybrid world. Years ago, you didn't really hear much talk about hybrid. You talked to your customers and say, "Hey, we want to connect our on-prem to the public cloud." You're bringing services to do that. Asa, thanks so much for coming to theCUBE. Appreciate it. >> Thank you, thanks for your time. >> You're welcome. And thank you for watching everybody. This is Dave Vellante with theCUBE. We'll be back right after this short break. (upbeat music)

(lively music) >> Narrator: Live, from San Francisco, celebrating 10 years of high tech coverage, it's theCUBE, covering VMworld 2019! Brought to you by VMware and its ecosystem partners. >> Okay, welcome back to theCUBE's live coverage in VMworld 2019. We're in San Francisco. We're in Moscone North Lobby. I'm John Frer, my co Stu Miniman, here covering all the action of VMworld, two sets for theCUBE, our tenth year, Stu. Keeping it going. Two great guests, John Fanelli, CUBE Alumni, Vice President of Product, Virtual GPUs at NVIDIA Kevin Gray, Director of Product Marketing, Dell EMC. Thanks for coming back on. Good to see you. >> Awesome. >> Good to see you guys, too. >> NVIDIA, big news, we saw your CEO up on the keynote videoing in. Two big announcements. You got some stats on some Windows stats to talk about. Let's talk about the news first, get the news out of the way. >> Sure, at this show, NVIDIA announced our new product called NVIDIA Virtual Compute Server. So for the very first time anywhere, we're able to virtualize artificial intelligence, deep learning, machine learning, and data analytics. Of course, we did that in conjunction with our partner, VMware. This runs on top of vSphere and also in conjunction with our partner at Dell. All of this Virtual Compute Server runs on Dell VxRail, as well. >> What's the impact going to be for that? What does that mean for the customers? >> For customers, it's really going to be the on-ramp for Enterprise AI. A lot of customers, let's say they have a team of maybe eight data scientists are doing data analytics, if they want to move through GPU today, they have to buy eight GPUs. However, with our new solution, maybe they start with two GPUs and put four users on a GPU. Then as their models get bigger and their data gets bigger, they move to one user per GPU. Then ultimately, because we support multiple GPUs now as part of this, they move to a VM that has maybe four GPUs in it. We allow the enterprise to start to move on to AI and deep learning, in particular, machine learning for data analytics very easily. >> GPUs are in high demand. My son always wants the next NVIDIA, in part told me to get some GPUs from you when you came on. Ask the NVIDIA guy to get some for his gaming rig. Kidding aside, now in the enterprise, really important around some of the data crunching, this has really been a great use case. Talk about how that's changed, how people think about it, and how it's impacted traditional enterprise. >> From a data analytics perspective, the data scientists will ingest data, they'll run some machine learning on it, they'll create an inference model that they run to drive predictive business decisions. What we've done is we've GPU-accelerated the key libraries, the technologies, like PyTorch, XGBoost to use a GPU. The first announcement is about how they can now use Virtual Compute Server to do that. The second announcement is that workflow is, as I mentioned, they'll start small, and then they'll do bigger models, and eventually they want to train that scale. So what they want to do is they want to move to the cloud so they can have hundreds or thousands of GPUs. The second announcement is that NVIDIA and VMware are bringing Virtual Compute Server to VMware Cloud running on AWS with our T4 GPUs. So now I can scale virtually starting with fractional GPU to single GPU to multi GPU, and push a button with HCX and move it directly into AWS T4 accelerated cloud. >> That's the roadmap so you can get in, get the work done, scale up, that's the benefit of that. Availability, timing, when all of this is going to hit in-- >> So Virtual Compute Server is available on Friday, the 29th. We're looking at mid next year for the full suite of VMware Cloud on top of Aws T4. >> Kevin, you guys are supplier here at Dell EMC. What's the positioning there with you guys? >> We're working very closely with NVIDIA in general on all of their efforts around both AI as well as VDI too. We'll work quite a bit, most recently on the VDI front as well. We look to drive things like qualifying the devices. There's both VDI or analytics applications. >> Kevin, bring us up-to-date 'cause it's funny we were talking about this is our 10th year here at the show. I remember sitting across Howard Street here in 2010 and Dell, and HP, and IBM all claiming who had the lowest dollar per desktop as to what they were doing in VDI. It's a way different discussion here in 2019. >> Absolutely. Go ahead. >> One of the things that we've learned with NVIDIA is that it's really about the user experience. It's funny we're at a transition point now from Windows 7 to Windows 10. The last transition was Windows XP to Windows 7. What we did then is we took Windows 7, we tore everything out of it we possibly could, we made it look like XP, and we shoved it out. 10 years later, that doesn't work. Everyone's got their iPhones, their iOS devices, their Android devices. Microsoft's done a great job on Windows 10 being immersive. Now we're focused on user experience. When the VDI environment, as you move to Windows 10, you may not be aware of this, but from Windows 7 to Windows 10, it uses 50% more CPU, and you don't even get that great of a user experience. You pop a GPU in there, and you're good. Most of our customers together are working on a five-year life cycle. That means over the next five years, they're going to get 10 updates of Windows 10, and they're going to get like 60 updates of their Office applications. That means that they want to be future-proof now by putting the GPUs in to guarantee a great user experience. >> On the performance side too, obviously. In auto updates, this is the push notification world we live in. This has to built in from day one. >> Absolutely, and if you look at what Dell's doing, we really built this into both our VxRails and our VxBlocks. GPUs are just now part of it. We do these fully qualified. It stacks specifically for VDI environments as well. We're working a lot with the n-vector tools from VM which makes sure we're-- >> VDI finally made it! >> qualifying user experience. >> All these years. >> Yes, yes. In fact, we have this user experience tool called n-vector, which actually, without getting super technical for the audience, it allows you to look at the user experience based on frame-rate, latency, and image quality. We put this tool together, but Dell has really been taking a lead on testing it and promoting it to the users to really drive the cost-effectiveness. It still is about the dollar per desktop, but it's the dollar per dazzling desktop. (laughing) >> Kevin, I hear the frame-rate in there, and I've got all the remote workers, and you're saying how do I make sure that's not the gaming platform they're using because I know how important that is. >> Absolutely. There's a ton of customers that are out there that we're using. We look at folks like Guillevin as like the example of a company that's worked with us and NVIDIA to truly drive types of applications that are essential to VDI. These types of power workers doing applications like Autodesk, that user experience and that ability to support multiple users. If you look at Pat, he talked a little bit about any cloud, any application, any device. In VDI, that's really what it's about, allowing those workers to come together. >> I think the thing that the two of you mentioned, and Stu you pointed out brilliantly was that VDI is not just an IT thing anymore. It really is the expectation now that my rig, if I'm a gamer, or a young person, the younger kids, if you're under 25, if you don't have a kick-ass rig, (laughs) that's what they call it. Multiple monitors, that's the expectation, again, mobility. Work experience, workspace. >> Exactly, along those same lines, by the way. >> This is the whole category. It's not just like a VDI, this thing over here that used to be talked about as an IT thing. >> It's about the workflow. So it's how do I get my job done. We used to use words like "business worker" and "knowledge worker." It's just I'm a worker. Everybody today uses their phone that's mobile. They use their computer at home, they use their computer at work. They're all running with dual monitors. Dual monitors, sometimes dual 4K monitors. That really benefits as well from having a GPU. I know we're on TV so hopefully some of you guys are watching VDI on your GPU-accelerated. It's things like Skype, WebEX, Zoom, all the collaboration to 'em, Microsoft Teams, they all benefit from our joint solution, like the GPU. >> These new subsystems like GPUs become so critical. They're not just subsystem, they are the main part because the offload is now part of the new operating environment. >> We optimized together jointly using the n-vector tool. We optimized the server and operating environment, so that if you run into GPU, you can right-size your CPU in terms of cores, speed, etc., so that you get the best user experience at a most cost effective way. >> Also, the gaming world helps bring in the new kind of cool visualization. That's going to move into just the workflow of workers. You start to see this immersive experience, VR, ARs obviously around the corner. It's only going to get more complex, more needs for GPUs. >> Yes, in fact, we're seeing more, I think, requirements for AR and VR from business than we are actually for gaming. Don't you want to go into your auto showroom at your house and feel the fine Corinthian leather? >> We got to upgrade our CUBE game, get more GPU focused and get some tracing in there. >> Kevin, I know I've seen things from the Dell family on levering VR in the enterprise space. >> Oh, absolutely. If you look at a lot of the things that we're doing with some of the telcos around 5G. They're very interested in VR and AR. Those are areas that'll continue to use things like GPUs to help accelerate those types of applications. It really does come down to having that scalable infrastructure that's easy to manage and easy to operate. That's where I think the partnership with NVIDIA really comes together. >> Deep learning and all this stuff around data. Michael Dell always comes on theCUBE, talks about it. He sees data as the biggest opportunity and challenge. In whatever applications coming in, you got to be able to pound into that data. That's where AI's really shown... Machine learning has kind of shown that that's helping heavy lifting a lot of things that were either manual. >> Exactly. The one thing that's really great about data analytics that are GPU-accelerated is we can take a job that used to take days and bring it down to hours. Obviously, doing something faster is great, but if I take a job that used to take a week and I can do it in one day, that means I have four more days to do other things. It's almost like I'm hiring people for free because I get four more extra work days. The other thing that's really interesting as our joint solution is you can leverage that same virtual GPU technology. You can do VDI by day and at night, you run Compute. So when your users aren't at work, you migrate them off, you spin up your VMs that are doing your data analytics using our RAPIDS technology, and then you're able to get that platform running 24 by seven. >> Productivity gains just from an infrastructure. Even the user too, up and down, the productivity gains are significant. So I'll get three monitors now. I'm going to get one of those Alienware curved monitors. >> Just the difference we had, we have a suite here at the show, and just the difference, you can see such a difference when you insert the GPUs into the platform. It's just makes all the difference. >> John, I got to ask you a personal question. How many times have people asked you for a GPU? You must get that all the time? >> We do. I have a NVIDIA backpack. When I walk around, there's a lot of people that only know NVIDIA for games. So random people will always ask for that. >> I've got two sons and two daughters and they just nerd out on the GPUs. >> I think he's trying to get me to commit on camera on giving him a GPU. (laughing) I think I'm in trouble here. >> Yeah, they get the latest and greatest. Any new stuff, they're going to be happy to be the first on the block to get the GPU. It's certainly impacted on the infrastructure side, the components, the operating environment, Windows 10. Any other data you guys have to share that you think is notable around how all this is coming together working from user experience around Windows and VDI? >> I think one piece of data, again, going back to your first comment about cost per desktop. We're seeing a lot of migration to Windows 10. Customers are buying our joint solution from Dell which includes our hardware and software. They're buying that five-year life cycle, so we actually put a program in place to really drive down the cost. It's literally like $3 per month to have a GPU-accelerated virtual desktop. It's really great Value for the customers besides the great productivity. >> If you look at doing some of these workloads on premises, some of the costs can come down. We had a recent study around the VxBlock as an example. We showed that running GPUs and VDI can be up as much as 45% less on a VxBlock at scale. When you talk about the whole hybrid cloud, multi-cloud strategy, there's pluses and minuses to both. Certainly, if we look at some of the ability to start small and scale out, whether you're going HCI or you're going CI, I think there's a VDI solution there that can really drive the economics. >> The intense workloads. Is there any industries that are key for you guys in terms of verticals? >> Absolutely. So we're definitely looking at a lot of the CAD/CAM industries. We just did a certification on our platforms with Dassault's CATIA system. That's an area that we'll continue to explore as we move forward. >> I think in the workstation side of things, it's all the standard, it's automotive, it's manufacturing. Architecture is interesting. Architecture is one of those companies that has kind of an S and B profile. They have lots of offices, but they have enterprise requirements for all the hard work that they do. Then with VDI, we're very strong in financial services as well as healthcare. In fact, if you haven't seen, you should come by. We have a Bloomberg demo for financial services about the impact for traders. I have a virtualized GPU desktop. >> The speed is critical for them. Final question. Take-aways from the show this year, 2019 VMworld, Stu, we got 10 years to look back, but guys, take-aways from the show that you're going to take back from this week. >> I think there's still a lot of interest and enthusiasm. Surprisingly, there's still a lot of customers that haven't finished there migration to Windows 10 and they're coming to us saying, Oh my gosh, I only have until January, what can you do to help me? (laughing) >> Get some GPUs. Thoughts from the show. >> The multi-cloud world continues to evolve, the continued partnerships that emerge as part of this is just pretty amazing in how that's changing in things like virtual GPUs and accelerators. That experience that people have come to expect from the cloud is something, for me is a take-away. >> John Fanelli, NVIDIA, thanks for coming on. Congratulations on all the success. Kevin, Dell EMC, thanks for coming on. >> Thank you. >> Thanks for the insights. Here on theCUBE, Vmworld 2019. John Furrier, Stu Miniman, stay with us for more live coverage after this short break. (lively music)

>> Announcer: From Santa Clara, California, It's theCUBE! Covering Technovation World Pitch Summit 2019. Brought to you by SiliconANGLE Media. Now, here's Sonia Tagare. >> Hi and welcome to theCUBE. I'm your host, Sonia Tagare, and we're here at the Oracle Agnews Campus in Santa Clara, California, covering Technovations World Pitch Summit 2019, a pitch competition in which girls from around the world develop mobile apps in order to create positive change in the world. With us today, we have a Technovation executive, Devin Dillon, who is the Senior Director of Partnerships at Technovation, welcome to The Cube. >> Thank you. >> So, before we start, for people who don't know, can you tell us more about Technovation World Pitch? >> Sure, so Technovation World Pitch is sort of the culminating event of a program that we run for young girls around the world. So we invite girls to solve problems in their community. This year, we had over 7,000 girls from 57 countries participating. So lots of girls with lots of ideas. And then this World Pitch is the culmination of that. So it's a competition, and our winners from around the world are invited to come here and share their ideas. And a really exciting part is they get to meet all of their peers that are also working on solving problems and exploring technology, so it's a really great week. >> That's awesome, and can you tell us more about how you got involved in Technovation, and what your role is at the company? >> Sure, so I got involved in Technovation about seven years ago, the program was small. It had just gone international. I think our first year, we had less than 10 countries that were participating, but I really liked the idea of putting education online, accessible to anybody. Anyone can lead it, and solve a problem in their community, and learn a little bit as they were doing that. So that's how I got involved. And then, the program has grown, and we now have this big celebration event. So it looks different, but yeah, that's how I got involved. >> And can you tell me more about your role? >> Sure yes. So, I lead the program. So we have two programs at Technovation. We have Technovation Girls, which this World Summit is the celebrating event for, then we have Technovation Families, which is an educational program for our younger audience. It invites families to solve problems with AI. So my role is really to make sure that our programs are awesome, and helping people to learn. Our resources are good, and we're supporting our leaders around the world. So, our Technovation team never actually leads programs, we invite everyone from around the world to lead the programs, so we do a lot of work to make sure that the quality is there, and that the programs are having a great impact on the kids. >> Wow, and I recently heard that Iridescent became Technovation, so can you tell us more about that change, and why that decision was made? >> Yeah, I'm happy to. So, like I mentioned, we have two flagship programs. They previously had names that were pretty different and our organization was called Iridescent. And Technovation, it was this program, it was like a program that had gotten a lot of global scale and participants. So much so, that when we would say Iridescent, people would recognize us. So we changed our overall organization name to Technovation, and this program is now called Technovation Girls. We challenge girls to solve a problem in their community, using coding, and create a mobile app and a business plan, and then our other program, Technovation Families, challenges families to solve a problem using AI. >> And so I heard the girls had an amazing week. What was the schedule like, who did they get to meet? >> Sure, so it's a busy week. We have flown in girls from all over to be able to see a little bit of the Bay Area, to be able to meet each other, so we have lots of activities. We've had field trips to a lot of tech companies, so we were able to visit Uber, we were able to visit Autodesk, Google Ventures, where the girls are able to see and hear from different mentors in the industry, meet people that are working on technology, ask the questions, and then the other component is we invite the girls to connect with each other. It's a powerful moment where we have a lot of girls representing different cultures and different ideas, so we have fun things like dance parties and opportunities for them to get to know each other also. >> That sounds like a really bonding sleep over. >> Yeah, we try to create that atmosphere. Of course the girls can be shy, and they're coming maybe the first time to the United States. Many of them, English is their third or their fourth language, so it can be a little scary at first, but I think by today, they have been able to hopefully create some lasting friendships. >> That's amazing, and along with the friendships, for the people who do win, what kind of prizes do they get? >> Yeah, so we are giving away this year, over $50,000 worth of prizes. $30,000 of that is scholarships so the students can continue their education since they're young girls, they're able to sort of put that to their education how they would like, and then another option is that they can continue developing their idea. So the girls have crated a mobile app and a business plan, and so they're able to continue developing that if they would like to. >> And do they have mentors guiding them through that? >> Yes, and the exciting thing is, a lot of the mentors are here. So the way that the competition works, is that the girls are working on their idea for many months. They are creating an idea, they're coding, they're learning a lot of different things, they can be creating business plans, and the mentors are really there to support them, to help them build a relationship with someone who's maybe in the tech industry, but also just someone to give encouragement and to help them work together on their problem. >> And have you seen an increase in participant in Technovation over the years? >> Yeah, so this year, like I mentioned, we had 7,000 participants, which is a large year for us. The past two years, we've had great growth, because the program is online, and it's freely accessible. We've really been able to see a lot of take up from different people around the world. >> What countries do you hope to reach to eventually? >> Yeah, good question. Well we had submissions from 57 countries this year, so you know, each year, the submissions kind of change. So we're growing in a lot of really exciting places, I always love to see ideas from all different areas of the world, so tonight, we have some great ideas represented from Nigeria, and Cambodia, and Bolivia, and Canada, like really right there, like lots of corners of the world, so it's always exciting to see. >> And like what criteria do finalists have to pass to make it to this stage? >> Yeah, good question. So they need to submit a lot of different things to be invited to the competition. So the girls really work on pitching their idea, because we know that if you have an idea, not just in technology, you need to be able to understand how to present it and develop you know a business plan, and how you want others to understand what you're doing. They have created a mobile app, so they've coded something. They've probably learned technology or some technology skills, and then, what are our other components. They like develop their idea. So a large part of it is really thinking of an idea, making it batter, developing an actual product, so. >> Wow, and how do you think Technovation is helping the overall girls in tech, women in tech community? >> Yeah, so we're hoping it could get girls interested. So our girls are young, but we really hope to spark an interest and get them involved in the community, hopefully, this is a step on their path. Maybe they will keep taking classes that are technology related, or maybe they'll make some friends that are into technology and form a community. Maybe they'll go to college for this. Maybe some of them will become computer scientists, or engineers, or someone in technology, so it's pretty open, we want to create problem solvers and problem solvers so a lot of different things in our world, including impact technology. >> And going off of that, are there any success stories that really stand out to you? >> Yeah, I'm trying to think of some girls from this year. I think what always stands out to me, from the girls, is that they aren't just building like a mobile app. A lot of them are collaborating with people in their community, with their governments, with different non-profits. So, one of the girls this year, she's working on opioid addiction, and she's been collaborating with a lot of researchers in different universities, she's been thinking about how to create a prototype. Another girl this year is working on supporting farmers and invasive species. So she's been working with different invasive species groups to understand how this program is affecting people, so I think it's always really fun to see how the girls are not just thinking about themselves, or collaborating just on their team, they're really thinking about their community and making an impact with different people and different groups. >> And how do you hope Technovations going to continue to improve and impact more girls? >> Well, I hope we continue to create girls that feel empowered to make the world better. Which you know, is idealistic, but I think that's power of education, is that you help people to think about how to make the world better at the end of the day, and I hope we're giving them those tools. Hope we continue giving them the tools to make their lives and their communities better. >> That's awesome, and thank you so much for being here. >> Devon: Sure, thank you so much. >> This is Devon Dillon, and I'm Sonia Tagare. Thanks for watching The Cube. Stay tuned for more. (upbeat funky music)

>> Announcer: Live, from Anaheim, California, it's theCUBE, covering Nutanix .NEXT 2019, brought to you by Nutanix. >> Welcome back, everyone to theCUBE's live coverage of Nutanix .NEXT here in Anaheim, California. I'm your host, Rebecca Knight, along with my co-host, John Furrier. We are so excited to welcome back to the program, Dheeraj Pandey, the co-founder/CEO and Chairman of Nutanix. Thank you so much for coming back on theCUBE. >> Thank you for pronouncing my name diligently. >> You are welcome. >> John: Gotta work on that. >> So, Dheeraj, it was a poignant moment in the keynote when you got up there with many of the people who were sort of employee number one, two, and three, four at Nutanix. They are the builders, the dreamers, the visionaries, the innovators, the disruptors of this company, a company that you started. So I'd love you to just start out by reflecting a little bit on your journey and sort of how Nutanix has evolved. >> Yeah, I mean it's a poignant 10 years, you know. The moment itself is poignant and it brought a lot of nostalgia, you know, for just looking at the early folks and how we had to huddle together in the smallest of technical blips that you'd find in our thesis, because our thesis was very bold. It was, like, hey, we can put a lot of hardware into your software. It's, like, the way Apple would say, we'll get rid of the camera and make it into an app. Like, what? There's no need for a camera anymore. So that's what we had to do with data center infrastructure. So, those moments are memorable, they're etched in history and my memory, and every time you get a tough moment now, we actually invoke a lot of those tough moments from the past and say, look, the more things change, the more they remain the same. >> The beautiful thing about theCUBE, is our 10th year as well, we've been following your journey as well. We actually have soundbites of the early interviews, and one of the things I was always impressed with you guys was you stayed the course, you didn't waver on what was fashionable at the time. HCI was an early category. You were misunderstood at the beginning and then the numbers started to show and you guys built a great business. But now, you're 10 years old, you're public. All the numbers are out there. You gotta go the next level. This is your challenge with the team. What's the focus? What's the strategy? What's the marching orders for the team now, as you go past 10 years old? You got competitive pressure. There's marketplace. The numbers are there. It's a big piece of the pie there. >> Yeah. You know, I go back to everything I just said in my last answer as well. The more things change, the more they remain the same. The friction hasn't changed. Five years ago we were a much smaller brand. We didn't have a customer base. We didn't have money in the bank and we still had to keep raising money to fund ourselves. Today, we are running this business, spending, you know, a billion dollars every year now. But it's a free cash flow neutral business, and we have told the Street that we gonna keep running it like that, but just go back to the basics. The basics of this company are what made it come to here. The same basics will need to take it from here to the next 10 years. 10 years is the new zero. I mean, I said, look, we've reset the clock and it's a very metaphorical thing to say, but it's the new zero for us, you know. So going back to the basics are the three Ds I talked about. Data, we are greater data. And we continue to be amazing at data. Reliable, highly available, high performance data management. A greater design. You know, just making things simple, and we're really really really good at delivery and when we suck at it, we go and improve and are very resilient in delivering things, you know, so whenever some things falter within our customer success, customer service, the way we delivering things with your software and subscription, I think nobody can touch us in these three Ds. >> As you guys have proven a great loyalty, customer basis, very loyal on the product. As you have to go multi-cloud, as the Enterprise gets modernized, this is a big part of your current business. What are some of the things that you're looking at, in terms of these new products? Because you don't want to open the door up for either a competitor or a misfire on you guys. You gotta continue to provide product leadership. >> Well, the most important thing is honesty and vulnerability. The fact that these things are not awesome big products yet, but they are awesome nonetheless. So how do you really have the small wins? You know, I go back in time to, Look, it took 10 years for Amazon Prime to become Primetime. It took six years for YouTube to even start to figure out who YouTube is really gonna be, and you know, Google bought Writely, which was the company that became Google Docs. Five years, they didn't know what they were doing with those things, so what's really important for the new products is this long-term greed. You know, the fact that you really have this 10 year view of a multi-product portfolio, but the most important thing is how they gell well together, how they really integrate well together, because if we don't integrate these products, and we just throw it out as things, as opposed to an experience. Customers are, like, I can buy things from Best of Breed. So how do you really make these multi-product look like an experience is where the real Nutanix design value is actually shown. >> One of the things that you guys have a good customer reaction to is the simplicity and how you can integrate well and reduce all these manual tasks, which is, people talk about automation and everything, but you guys have customers saying, "I went from 24 racks to six. "I now run everything with the push of a button. "Not there yet with the one-click but pretty close." That sounds like the multi-cloud game right now, where it is kinda hodge-podge. No one's actually figured out how to bring it all together and orchestrate it. >> That's the money statement, John. That's where the money is. Complexities where we go in and really figure out how to really save money for our customers, make money for our partners and make money for ourselves. >> And the partner-side, HPE, a big announcement that you guys have been part of. They're gonna be coming on today. How's that going? Give us the update on the HPE. >> You know, the energy levels are high, but there's a bell curve of people, you know. You can't have everybody really be an innovator, an early adopter. We're looking for innovators and early adopters. Some great discussions happening with HP account managers. They're our account managers of very large accounts, and the word-of-mouth has to basically play its powerful game actually. >> I wanna ask you about innovation. Earlier, on a CUBE conversation, you talked with our own John Furrier, and you said, we disrupt ourselves, but you also just talked about these products being these sort of long-term play and really thinking about what the, more of a holistic view of what the customers need. I wanna hear about the Nutanix innovation process and sort of how you have kept that culture of a tech start-up now that you are a company with a market cap in the multiple billions. >> You know, as I said before, we are like a billion dollar start-up, you know. And it's not easy, because everybody wants you to grow up, like, behave and grow up, and I saw one of my slides in there taking real potshots of the sand and we haven't changed much, you know. So in many ways, we're reminding everybody that it's still Day Zero and Day One. Is the great cultural gravitas that we need to keep, retained in the business, actually, in the company? You know, having the kind of humor that we had, and you know, keeping it personal and personable with everybody, as opposed to, you know, stiff upper lip, and suits and mahogany tables and corner offices. Those are things that are the antithesis of what Nutanix is. And just keeping it humble, you know. Like, the fact that even though we have layers of management in the middle, how do you go six levels deep and really have a conversation as technical as you wanted it to be and as business incisively as we want it to be? And you know, there's a lot of things you can do by going six levels deep that otherwise were not possible if you just said, look, I just talked to my next level action team, and to us, that's the engine of innovation. >> And how is your leadership changed? >> I have a new customer called Wall Street. >> That's true. >> 'Cause you know, they buy my product. It just happens to be a retail product that you folks can buy, too. It's called NTNX, the ticker. So I have Main Street customers and then I have Wall Street as a customer, and I need to figure out where to really keep them balanced, because I sell products to both of them, and it's a journey. You know, it's never easy, because there's a customer that actually wants instant success. There's another customer that says we are with you for the long haul, and what I need to find in this Wall Street customer is the ones who are actually for the long haul. My leadership, actually, is about balancing the two together. >> So let's talk about the Wall Street thing for a second, because I think that's interesting. You've always said to me, you're gonna play the long game and you do. We've kinda proved that, but Wall Street, they're very short sighted right? So the earnings come out, you gotta deal with the shot clock, as a public company. As you go to Wall Street, how are they looking at the long game? Because there's major examples. Microsoft stock's at an all-time high. They were in the 20s a few years ago. Cloud obviously is validated, so you got a cloud vision, this cloud marketplace. You're in the core enterprise, which has been revitalized with private cloud. Again, proves your thesis originally. So you're in good position and you got the cloud game right there. What are they missing? What's Wall Street missing? >> I think the biggest thing is that in any transformation is actually messy. Look at all the transformations in the last 20 years. The good thing is that those that took the tough call of transforming themselves, they really have done well, you know. And this is not just Microsoft alone, but Adobe, where I sit on the board. There is Autodesk and there is Parametric PTC and Cadence and many many other companies that have gone through this transition of getting out of the box to being software and subscription actually, and that's the journey that we said we couldn't punt and postpone 'cause we wanna be a hybrid cloud company. How can we not have subscription on prem? If subscription is gonna be the off prem, it has to have on prem subscription as well. And I think it requires communication, constant communication, watch, don't be stupid, with Wall Street as well. >> Well, Wall Street likes those valuations. If you look at the SaaS companies, or subscription-based companies, their valuations are really on a multiple, much higher than, >> I mean, look, valuation, to me, is not an end in itself. If you do it right by Main Street, I think this Wall Street thing will take care of itself. >> Awesome. On the long game with your innovation, I gotta ask you about how you're gonna look at the partnerships and integrating in, because the competitor out there in the middle of the room there is VMware and Dell Technologies. They want to go end-to-end and they want to own everything end-to-end. You guys are taking a different approach. Could you share your competitive strategy in terms of how you guys are different than that, because you're partnering? You're competing in a different way. >> Yeah, as we go into becoming a bigger company and yet, having a real child-like brain, I think it's important, really, that we are in this cooperative world and every competitor is also a company we cooperate with. Look, I mean, we run on top of VMware and more than half our customers still use VMware underneath us. We are an app on their platform. So we are a platform company. We are also an app company and our platform should run all apps and our apps should run on all platforms and that's the way we look at it. That's the reason why Microsoft is relevant again, 'cause they're still looking at, rather than a single stack strategy, how do you really look at yourselves as living two lives actually, you know? And to compete, you just have to go back to the three Ds I talked about. If you just keep doing a really good job of data, disrupting the biggest hardware players out there in data, and be really really good with design and elegance and friction-less delivery, I think we'll be in good shape. >> One of the compliments that the analysts on theCUBE always pay to you, Dheeraj, is that you have a really good sense of the wave. You really know which way the technological and economic winds are blowing. I wanna know, what do you read? Who do you talk to? What signals are you paying attention to? Or is it just this innate sense you have that the rest of us can't hope to ever achieve? >> Well, thank for that compliment, first of all. I'm honored. But I just have this simple mantra which is, the more things change, the more they remain the same. So I bring a lot of things from my consumer life because I read a lot about consumer life and I have a little bit of an artist in me and even though I am supposed to be a geek, I was telling somebody I was trying to recruit the other day that, look, I'm really, at heart, an artist, more so than an engineer, and I think a lot of what you see in this conference and this company and the product portfolio, it's really the empathy for the other side. You know, that really brings out a lot of the innovation, and obviously, I don't innovate alone, but the people that are with us in this company, I just try to tell them about the empathy that I invoke for everybody else and I read a lot of history, I'm a big history buff, and not just the last 30 years of IT, which I invoke a lot, but I'm deep into, like, the history of humans, you know. Like, last two weeks, I spent a lot of time reading about Neanderthals and the hybrid Neanderthals with humans, modern humans, and there's another ones that they found in these caves of Denisova. They call Denisovans, you know. So I read a lot of history and that gives me a lot of perspective and a lot of courage and I bring a lot of those things into this new life, that's again, as I said, it's the same as the old one, with some new color. >> You're an entrepreneur. That's what entrepreneurship is all about. What entrepreneurial thing are you working on right now? 'Cause I've known, You've gotta have your hands in some new things. What's the new entrepreneurial thinking or project that you're taking on? >> Well, the one that is very interesting one for operating a business is Capital Allocation, and it's a difficult one because you have to, basically, be somebody who really balances content and delivery, you know, and content is products and delivery is go to market, and when you go to market, it's marketing and sales. So as a company, we were tested in the last nine months to really understand Capital Allocation. I'm a big fan of the book, The Outsiders. I just read this probably a year ago, and you could see that there was some themes in The Outsiders about running the business on free cash flow, which is nothing new. It's not like Amazon invented it. They've been doing it for those 40, 50 years. Second one is Decentralized Decision Making. The third one is a really good capital allocation. So as an entrepreneur, I'm learning to actually understand what it means to decentralize decision making, and do a really good job of capital allocation, and finally, go and tell the Street about why free cash is the way to run a business as opposed to profitability and a gap way, because a lot of our dollars are sitting in the balance sheet, and they aren't in the P&L. So I think really running the business where growth matters, which is about free cash flow, about making sure that we can really create more CEOs in the company, independent decision making, and finally, this idea that you want to run this business as if it was a bunch of businesses, actually. >> Great. >> Awesome. >> One of the things you keep talking about in this interview is balance. You're balancing the needs of Main Street and Wall Street, the needs of your cloud customers, the needs of your employees, while also growing this business. How do you balance at all? As the CEO of this fast-growing company? You said you're an artist. And you read a lot of history. >> Honestly, I'm not a very balanced person. If you ask me, like, work and life, family and work, is because of my wife that I find a balance there. >> So you owe it all to her? >> Yeah, I think you can say that again, and the same thing is true for, like, one of my team members, our COO, David Sangster. He says, "Look, our health, family, and work, "in that order," and honestly, mine is in the reverse right now. So I need to really go and, These kind of conversations remind myself that it's important to actually have some balance. >> Great, well, Dheeraj, always a pleasure having you on theCUBE. >> Pleasure. >> I'm Rebecca Knight, for John Furrier. We'll have so much more from Nutanix next coming up on theCUBE just after this. (techno music)

[Music] hi I'm Peter Burroughs welcome to another cube conversation from our beautiful Studios here in palo alto california as we do with every cube conversation we come up with a great topic and we find someone who really understands it so they can talk about it we capture them for you so you can learn something about some of the new trends and changes in the industry and we've doing that today too the topic that we're talking about is how do you do a better job of mapping the costs that are being generated by the cloud or that informations coming out of cloud suppliers related to what you're using with the actual business activities that generate the differential capabilities that customers are looking for that's a tough tough challenge and to understand that better we're talking with junior Stormin who's a co-founder of cloud ability Jaron welcome to the cube thanks Peter good to be here so so let's talk about first who are you yeah so I am co-founder of cloud ability and credibility is focused around improving the unit economics of cloud spend so our customers tend to be those who are spending large amounts in AWS or Azure or GC P and we take their billing data their utilization data various metadata about their business and do machine learning and data science on top of it to help them get better visibility into sort of where that spend is going how they're using it but more importantly to give them some controls around how they want to optimize an optimize doesn't necessarily mean save money in a cloud world because most companies who are moving into cloud very heavily are doing that for the innovation for the speed so they can deliver you know better data faster but it's really about fine-tuning the conversation say okay here we want to save money here we want to move faster here we want to focus on quality and really providing a way for the the various groups that aren't normally talking the finance teams with the engineering teams with the procurement teams all these groups to come together and be able to take executive input to say okay how do we want to operate and how do we one improve those your economics as we go well I want to start with just quick comment on this notion of Union I can when people here historically hear the notion of unit unit economics they think of you know increasing scale so the average cost per unit goes down yeah I think you're talking about more than that right are you really also talking about a mapping of what spend is generating to the business activities that actually generate value and ensuring that you get the differential or the optimized Union economics unit cost yeah oh so the mapping is actually really interesting ly challenging in cloud it's hard enough in traditional IT if you look at somebody like AWS they have two hundred thousand SKUs different products you can buy and they now bill at a second level resolution so what this means is you've got all these engineers out there using cloud in a very good way to move quickly and of 8/2 little more features and they kind of have an unlimited credit card that they can go spend on as quickly as they need and they never see the statements they never see the bills and the other side you've got finance teams procurement teams who've sort of lost control of traditionally the power of the PIO that they have to actually rein that in and they're they're struggling just to understand what is the spend and then to the mapping question how do i allocate these hundreds and millions of charges that i have this month into cost centers and business units and getting that sorted in a world where engineers are focused on moving fast or not they're not tagging things based on cost and are typically so once you get that sort of mapping aspect sorted to the next point you brought as is in bringing the business value so how do we start to relate that back there's a concept a lot of you know IT has been a cost center and now it's sexual driver of value in a world where businesses are increasingly delivering their value through software so we need to start tying the spending mapping into the business and then tying that to the value delivered a great example of this I was sitting last week with one of the largest cloud spenders in the world there have been you know nine figures with their primary vendor and in the conversation with the executives we realized that nobody was looking at both sides of that equation you had the the finance people who were saying hey we're tracking the cost and we think I was happening there and then you had the the revenue generators looking at the money coming anyhow the cloud people with that but there wasn't this centralized view to say alright we want to have a conversation about what value I were getting to spend and the question that always comes up what that is I was doing the right amount well let me build on that because it's seat because IT is historically and this is one of the things that we've been doing over the last few years IT has historically done things on a project level yes all right so we had waterfall development we tried to change that with agile we had you know buy the hardware upfront and then deploy the application on a cloud changes that so this project orientation has led to a set of decisions about finance at the moment that the business asides to do it we've changed the practices that we use at a development level we've changed the practices that we use at an asset level is it now time to change the practices that we use at a finance level is that really kind of what's going on here it is that the project analogy is good because what we're seeing is they're shifting from a project basis to a productive basis and products that deliver value increasingly if you think about the change that's happened with DevOps it in the scene and cloud companies are delivering more of their value through software and they're not just using IT for internal projects right it's actually the driver of business how we interact with Airlines and banks and all these things so that's the shift to say okay now we've gotten good at DevOps moving fast and we've gotten good at deploying and building better data stores now we need to bring in this new discipline and the discipline is what the market is calling fin ops which essentially is combining financial financial operations but you simply combine technology applied specifically do a cloud roll and it only can really happen in cloud it can't happen in data centers because data centers have fixed spending right you have to wait to get resources once you make the investment it's a sunk cost there's months of lead time cloud introduced the removal of constraints which means you can get whatever you want as quickly as you want and DevOps meant it's all automated so instead of your collection of 60 servers you've got thousands that are coming up and down all the time so what you don't have to do is bring in all these groups engineers have to think about cost as a new efficiency metric they have to think about the impact of their business at this code this confirmation template they just wrote is going to have and the finance teams have to shift from this mode of I'm under report retro actively and at a quarterly granularity sixty days after it happened and block investment to be I'm going to partner with these teams report in a real-time fashion give them the visibility help forecast and actually bring them together to make better business decisions about the cloud spend so cloud has allowed development to alter practically agile has been around for a long time before the cloud predates the cloud but it became practical and almost demanded as a consequence of what you could do with cloud so cloud change development through agile it changed infrastructure management through DevOps where now you're you're deploying software infrastructure of code and know as code and what you're saying is the third leg of that stool cloud is now changing how you do financial management of technology financial management of IT and we're calling that fin ops yeah and you you you can't really have fin ops without cloud or without DevOps and if you have the two together you alter we need this new set of it's a new operating model the reason this has come to a head of late is you know if you look at going to the Amazon riemeck conferences a few years back it was like well how much is cloud gonna be a thing and okay clouds now gonna be a thing when's it gonna happen now it's about the how and how do we do this better cloud is hitting for the material spend levels now at bigger organizations I mean the you know see the the cloud projections where it's going I think it's now 360 billion the next few years and we're seeing CFO's at public companies look to say okay it's not my biggest line-item yet but it's the most variable and fastest growing cogs expense so it's actually start to affect our margins we needed a new set of process used to actually manage this so one of the things that's coming to market is this new group called the phenoms Foundation which is a non-profit trade association that initially has a few dozen of some of the largest cloudspinners of the world there's the Spotify as the alaskans the nation why it's Autodesk's and they've all come together as a set of best practice practitioners to start to codify this into something that can be you know scaled out in organizations so that group is gonna be putting out a user conference around this area there's a new o'reilly book that's coming out the end of the year that's going to be sort of the treatise and all this stuff pulled together because what we found in you know me is in code ability in the last eight years we bring in technology and platform to show the recommendations of visibility how to do this but the real challenge companies run into is they don't have the internal expertise their finance teams understand what they need to the engineers don't and so you know they came to us last year saying can you help figure out the processes can you educate us and that's really where you know the spin offs foundation is growing bringing together those people to define those processes so the the impact of cloud on each of these different groups on the development group on the infrastructure team and now on the finance team the interest the developer groups I think some of them resisted it but generally speaking it's gone okay and and eventually tooling from a variety different players came along that made it easy to enact best practices and software development through an agile mechanism in the last few years after significant battles within infrastructure teams about whether or not they were going to use software as code we've seen new products new tooling that has facilitated the adoption of those practices what kind of tooling are we going to see introduced that facilitates thin ops so that finance teams procurement teams move from a project orientation to a strategic management of the resource orientation I mean I think the first is on the engineering side is seeing costs become a first-class citizen of an efficiency metric that they need to look at so you know in their build processes baked in the CI CD looking to see am I properly sizing my compute request for the workload that it needs there's some research research just came out showing that I think it's like 80 percent of the market is not using the best discounting options the cloud providers offer you hear these horror stories it's too expensive we said overspend that's not actually a problem with the cloud providers that's a problem with the enterprises not using the tools offered the discounts the reserved ences the infrequent access door exactly so I think at the end of the day it's the first step in this is getting those checks in place to say are we using the things that help drive the right cost for our needs and the other side of that the finance team is really changing the way that they are interacting with their technology teams becoming partners becoming advocates in this versus a passive you know retroactive reporter down the line and this enables these sort of micro optimization discussions that can happen where data center world we bought it some cots is sitting there odd world we can make decisions today that impact you know the business tomorrow so let me make sure I got this so I have a client who who I was having a conversation with them they told me that their their Amazon there AWS bill is 87 gigabytes mm-hmm not that monthly that's 87 pages that's 87 gigabyte yeah so we get we bring this 87 gigabytes in and it's a story about what I consume out of Amazon it's not a story what my business utilizes to achieve its objectives so we're now entering into a world where we're trying to introduce those financial visit that financial visibility into how that spend can be mapped to what the business does so the finance group can look at a common notion of truth and the IT group can look at a combination of troop application owners can look at a common notion of truth and that's what is fin ops is providing if I got that right yeah absolutely and the eighty-seven gigabyte example is the exactly reason why it is fin option not just cloud financial management you can't have a person with a spreadsheet looking at that and trying to make decisions about it right it has to be automated its IT finances code it's got to be baked into the processes you know we we've seen organizations that have hundreds of millions of individual charges hitting them in a consumption based manner the other thing that's come in with the fin ops as a core tenant is we're now seeing a decentralization of accountability for that spend so if you look at the big cloud spenders out there maybe spending tens or hundreds mils a year some of them have thousands of cloud environments gone is the day of we have a centralized Group begins to say we're gonna turn this off turn this off we want to give each of those teams the ability to see there's just their portion of that bill in the right mapped way as you said and to be able to take actions on the back of that so that's changed and they you know you run it you maintain it you understand which shutdown what has sort of come back to the old centralized model is this notion and this is where procurements job is shifted to largely of we deal still want to centralize the rate reduction so engineers you go use less right essentially finance team procurement work together with the cloud vendors to get the best possible rates through reserved instances can be reduced discounts you know volume discounts negotiated rates whatever it is and they become sort of strategic sourcing just say you're gonna use whatever you're going to use and you're gonna watch that to make sure you're using the right amount will targets threshold we're gonna make sure we get the best rate and that's sort of the two sides of the coin well very importantly procurement has always been organized on episodic purchases where the whole point is to bring the price point down and now we're talking about a continuous services where you were literally you're literally basing your business on capabilities provided by a third party and that is a very very very different relation just-in-time purchasing right and it's and it's a new supply chain management process where you have so many SKU options and you are making these purchase decisions sometimes thousands a day and that impacts everything down the road excellent gr storm and co-founder of cloud ability talking about Finn ops and cloud abilities role in helping businesses map the cloud spend to their business activities for a better more optimal views of how they get what they need out of their cloud expenditures Jr thanks very much for being on the connects here and once again I'm Peter burrows and thanks for listening to this acute conversation until next time [Music] you

>> From Palo Alto, California, it's theCUBE. Covering the Conference Boards Sixth Annual Innovation Master Class. >> Hey, welcome back here, everybody. Jeff Frick here with theCUBE. We're at the Innovation Mater Class at Xerox PARC in Palo Alto. Really excited to be here, never been here, surprisingly, for all the shows we do just up the hill next to VMware, and Tesla. This is kind of the granddaddy of locations and innovation centers, it's been around forever. If you don't know the history, get a couple books, you'll learn it pretty fast. So we're excited to be here and our next guess is Antony Brydon, four-time founder and CEO, which is not easy to do. Again, check the math on that, most people are successful a couple times, hard to do it four times. And now he's the co-founder and CEO of Directly. So Antony, great to see you. >> It's good to be here. >> So, Directly, what is directly all about for people aren't familiar with the company? >> Most companies are excited to, and pursuing, the opportunity of automating up to 85% of their customer service. That's the ambition, and giving customers a delightful answer in their first experience. Most of those companies are falling down out of the gates because there are content gaps, and data gaps, and training gaps, and empathy gaps in the systems. So we build a CX automation platform and it puts experts at the heart of AI, letting these companies build networks of product experts and then rewarding those experts for creating content for AI systems, for training AI systems, for resolving customer questions. >> Right. So let's back up a step. So Zendesk is probably one we're all familiar with. You send in a customer service node, a lot of the times it comes back, customer service to Zendesk. >> Yes. >> But you're not building kind of a competitor of Zendesk, you're more of a partner, if I believe, for those types of applications, to help those apps do a better job. >> We are, we're a partner for Zendesk, we're a partner for Microsoft Dynamics, for Service Cloud and the like, and, essentially, are building the automation systems that make their AI systems work and work better. >> Right. >> Those are pure technology systems that often lack the data and the content to deliver AI at scale and quality, and that's where our platform and the human network, the experts in the mix, come into play. >> We could probably go for a long, long time on this topic. So what are some of the key things that make them not work now? Besides just the fact that it's kind of like the old dial-in systems. It's like, I just want to hit 0000. I just want to talk to a person. I have no confidence or faith that going through these other steps is going to get me the solution. Do you still see that on the online world as well? >> No, there are very clear gaps. There are four or five areas where systems are falling down. AI project mortality, as I refer to it. Very few companies have the structured data that systems need to work at scale. >> On the back, to feed the whole thing. >> That's right. Labeled, structured, organized data. So that doesn't exist. Many companies don't have the content. That's a second area. They may have enterprised knowledge bases, but they're five years old, they're seven years old, they're outdated, they're not accurate. Many companies don't have the signal. When a automated answer's delivered, they have to wait for a customer to rate it, and that tends to be really poor signal on whether that answer was good or not. And then last, many companies just don't have the teams to maintain these algorithms and constantly tune them. And that is where experts at the heart of a platform can come into play, by building a network of product experts who know the products inside and out. These could be Airbnb hosts for one of our customers, these could by Microsoft Excel users in the Microsoft example. Those experts can create that content, train the data, and actually resolve questions, filling those gaps, solving those problems. >> Right. I'm just curious, on the expert side, how many--? I don't know if there's best practices or if there's kind of certain buckets depending on the industry. Of those expert answers are generated by people inside the company versus a really kind of active, engaged community where you've got third-party experts that are happy to participate and help provide that info. >> Over 99% of the answers and the content is actually generated by the external network. >> 99%? >> 99%. You start with sources of enterprise knowledge, but it's a long, hard, arduous process to create those internal knowledge bases, and companies really struggle to keep up, it's Britannica. By the time you ship it it's outdated and you have to start all over again. The external expert networks work more like Wikipedia. Content constantly being organically created, the successful content is promoted, the unsuccessful content is demoted, and it's an evergreen cycle where it's constantly refreshing. Overwhelmingly external. >> Overwhelming. I mean, I could see where there's certain types of products. I was telling somebody else the other day about Harley-Davidson, one of the all-time great brands. People tattoo it on their body. Now, there aren't very many brands that people tattoo on their body. So easy to get people to talk about motorcycles or some of these types of things, but how do you do it for something that's really not that exciting? What are some of the tricks and incentives to engage that community? Or is there just always some little corps that you may or may not be aware of that are happy to jump in and so passionate about those types of products? >> There are definitely some companies where there's very little expertise and passion in the ecosystem around it. They're few and far between. If you find a product, if you find a company, you can find people that rely, love, and depend on that company. I gave some of the B to C examples, but we've also got networks for enterprise software companies, folks like SAP, folks like Autodesk. And those networks have experts that are developers, resellers, VARs, systems integrators, and the like. In the overwhelming majority of cases, the talent and the passion exists, you just have to have a simple platform to onboard and start tapping that talent and passion. >> So if I hear you right, you use kind of your Encyclopedia Britannica because that's what you have to start, to get the fly wheel moving, but as you start to collect inputs from third-party community, you can start to refine and get the better information back. And I ask specifically that way because you mentioned the human factors, and making people part of this thing, which is probably part of the problem with adoption, as I'd want confidence that there's some person behind this, even if the AI is smart. I'd want at least feel like there's some human-to-human contact when I reach out to this company. >> Yeah, that's critically important, because the empathy gap is real in almost all of the systems that are traditionally out there, which is when an automated answer's delivered, in a traditional system, it typically has a much lower CSAT than when it comes from a human being. What we found is when you have an expert author that content, when his or her face is shown next to the answer as it's presented to the user, and where he or she is there to back it up should that user still need more help, there you retain the human elements that personalize the contact, that humanize the experience, and immediately get big gains in CSAT. So It think that empathy piece is really important. >> Right. I wondered if you could share any specific examples of a customer that had an automated, kind of dumb system, I'll just use that word, compared to what they can do today, and some of the impacts when they put in some of the AI-powered systems like you guys support. >> So one of the first immediate impacts is often when we go in, a automated or unassisted system will be handling a very small percentage of the queries, and percentage of the customer questions coming in, and-- >> And people are going straight to zero, they're just like, I got to go to a person. >> Yeah, we're mostly in digital channels, so less phone, but yes, because the content there-- >> As an analogy, right. >> Because the content isn't there, it doesn't hit and resolve the question in that frequent a rate, or because the training and the signal isn't there, it's giving answers that are a little off-base. So the first and lowest hanging fruit is with a content library that's get created that can get 10, 50, 100 times broader that enterprise content pretty quickly. You're able to hit a much broader set of questions at a much higher rate. That's the first low-hanging fruit and kind of immediate impact. >> And is that helping them orchestrate, coordinate, collect data form this passionate ecosystem that's outside the four walls? Is that, essentially, what you're doing in that step? >> It essentially is. It is about companies having these ecosystems of these users, millions of hours of expertise in their head, millions of hours free time on their hands, and the ability to tap that in a systematic way. >> Wow. Shift gears a little bit, you are participating on a panel here at the event, talking about startups working with big companies and there's obviously a lot of challenges, starting with vendor viability issues, which is more kind of selling to big customers versus, necessarily, partnering with big companies. But what are some of the themes that you've seen that make that collaboration successful? Because, obviously, you've got different cultures, you got different kind of rates of the way things happen, you've got, beware the big company who eats you up in meetings all the time when you're a little start-up, they'll kill you accidentally just by scheduling so many meetings. What are some of the secrets of success that you're going to share here at the event? >> So we've got experience in that. Microsoft is a partner of ours, Microsoft Ventures is an investor. I think the single biggest key is an aligned vision and a complementary approach. The aligned vision where both the start-up and the partner are aiming for a similar point on the horizon. For example, the belief that automation can delight a very large set of customers by providing them a good, instant answer, but complementary approaches where the core skillsets of the companies round out each other and become less competitive. In this case, we've partnered with-- Microsoft is best in class AI platform and cognitive services, and we're able to tap and leverage that. We're also able to bring something unique to the equation by putting experts at the heart of it. So I think that architectural structure, in the first place, is a great example of kind of getting it right. >> Right. And your experience, that's been pretty easy to establish at the head-end of the process, so that you have kind of smooth sailing ahead? >> No, I don't think it's easy to establish at the head of the process, and I think that's where all of the good work and investment needs to happen. Upfront, on that kind of shared vision, and on that kind of complementary approach. And I think it is probably 20% building that together, but it's also 80% just finding it. The selection criteria by which a corporate partner picks a startup and the startup partner picks the corporate partner. I think just selecting right is the majority of the challenge, rather than trying to craft it kind of midstream. >> If it doesn't feel good at the beginning, it's probably not going to to work out. >> Right, it's about finding it. It's a little bit like the Venture analogy. Do they find great companies, or do they build great companies? Probably a little of both, but that finding that great company is a large part of the equation. >> Yeah, helps. So, Antony, finally get a last question. So, again, four successful startups. That does not happen very often with the same team. And look at your background, you're a psychology and philosophy major, not an engineer. So I'd just love to get kind of your thoughts about being a non-tech guy starting, running, and successfully exiting tech companies here in silicon valley. What's kind of the nice thing being from a slightly different background that you've used to really drive a number of successes? So I think the-- I think two things, I think one, coming from a non-tech and coming from a psych background has given us an appreciation of the human elements in these systems that tech alone can't do it. I'd say, personally, one of the impacts of being a non-tech founder in this valley is a heck of a lot of appreciation for what teams can do. And realizing that what teams can do is far more important than what individuals can do. And I say that because as a non-tech founder, there's literally nothing I could accomplish without being a part of a team. So that, I think, non-tech founders have that in spades. A harsh and frank realization that it's about team and they can't do anything on their own. >> Well, Antony, thanks for taking a minute out of your time. Good luck on the panel this afternoon and we'll keep an eye, watch the story unfold again. >> Yep, I appreciate it. Thanks very much. >> He's Antony, I'm Jeff, you're watching theCUBE. We're at the Master at the Master Innovation Class at Xerox PARC, thanks for watching.

>> Live from Las Vegas, it's theCUBE! Covering AWS re:Invent 2018. Brought to you by Amazon web services, Intel, and their ecosystem partners. >> Hey welcome back everyone, here at AWS re:Invent 2018, their sixth year of theCUBE coverage, two sets wall-to-wall coverage here, two more sets in other locations, getting all the content, bringing it in, ingesting it into our video cloud service on AWS, ah, Dave, >> Lot of content, John. >> Lot of people don't know that we have that video cloud service, but we're going to have a lot of fun, ton of content, ton of stories, and a special analyst segment, Jerry Chen, guest here today, CUBE alumni, famous Venture Capitalist and Greylock partners, partnering with Reid Hoffman, the founder of LinkedIn, great set of partners at Greylock , great firm, tier one, doing a lot of great deals, Rockset, recent one. >> Thanks, yeah. >> You're also, on the record, these six years ago, calling the shot of Babe Ruth predicting the future. You've got a good handle on, you've got VM where you have the cloud business, now you're making investments, you're seeing a lot of stuff on the landscape, certainly, as a Venture Capitalist, you're funding projects, what better time now of innovation to actually put money to work, to hit market share, and then the big guys are getting bigger, they're creating more robust platforms, game is changing big-time, want to get your perspective, Dave, so, Jerry, what's your take on the announcements, slew of announcements, which ones jumped out at you? >> I think there's kind of two or three areas, there's definitely the hybrid cloud story with the Outpost, there's a bunch of stuff around ML and AI services, and a bunch of stuff on data and storage, and for me I think what they're doing around the ML services, the prediction, the personalization, the text OCR, what Amazon's doing at that app layer is now creating AI building blocks for modern application, so you want to do forecasts, you want to do personalization, you want to do text analysis, you have a simple API to basically build these modern apowered apps, he's doing to the app infrastructure layer what he's done to the cloud infrastructure layer, by deconstructing these services. >> And API is also the center, that's what web services are, so question for you is, do you see that the core cloud players, Aussie, Amazon, Bigly, Google, Microsoft, others, it's a winner take most, you called that six years ago, and that's true, but as they grow there's going to be now a new cloudification going on for business apps, new entrepreneurs coming to market, who's vulnerable, who wins, who loses, as this evolution continues because it's going to enable a lot of opportunity. >> Yeah, well I mean Amazon in cloud in general is going to create a lot of winners and losers, like you said, so I think you have a shift of dollars from on prem and old legacy vendors, databay storage, compute, to the cloud, so I think there's a shift of dollars, there are winner and losers, but I think what's going to happen is, with all these services around AI, ML, and Cloud as a distribution model, a lot of applications are going to be rebuilt. So I think that the entire application stack from all the big SaaS players to small SaaS companies, you're going to see this kind of a long tale of new SaaS applications being built on top of the Cloud that you didn't see in the past. >> And the ability to get to markets faster, so the question I have for you is, if you're an entrepreneur out there, looking for funding and I can to market quicker, what's the playbook, and two, Jassie talked on stage about a new persona, a new kind of developer, one that can rethink and reimagine and reinvent something that someone else has already done, so if you're an entrepreneur, you got to think to take someone else's territory, so how does an entrepreneur go out and identify whose lunch to eat, so if I want to take down a company, I got to have a strategy, how do I use the cloud to >> I think it's always a combination when a founder in a thing attacks your market it's a combination of where are the dollars, where can I create some advantage IP or advantage angle, and thirdly where do I have a distribution advantage, how can I actually get my product in the hands of the users differently? And so I think those are the three things, you find intersection of a great market, you have a unique angle, and you have a unique route to market, then you have a powerful story. So, you think about cloud changing the game, think about the mobile app you can consist of, for consumers, that is also a new platform, a new distribution method, the mobile app stores, and so what happened, you had a new category of developers, mode developers, creating this long tale, a thousand thousand apps, for everything from groceries to cars to your Fantasy Football score. So I think you're going to see distribution in the cloud, making it easy to get your apps out there, going to see a bunch of new markets open up, because we're seeing verticals like healthcare, construction, financial services, that didn't have special apps beforehand, be disrupted with technology. Autodesk just bought PlanGrid for 800 million dollars, I mean that's unheard of, construction software company. So you can see a bunch of new inverdics like that be opened up, and then I think with this cloud technology, with compute storage network becomes free and you have this AI layer on top of it, you can power these new applications using AI, that I think is pretty damn exciting. >> Yes, you described this sort of, we went from client server to the cloud, brought a whole bunch of new app providers, obviously Salesforce was there but Workday, Service Now, what you described is a set of composeable digital services running on top of a cloud, so that's ripe for disruption, so do I have to own my own data centers if I'm big SaaS company, what happens to those big guys? >> I don't think you have to, well, you don't have to own your own data center as a company, but you could if you wanted to, right, so at some point in scale, a lot of big players build their own data centers, like AirBNB is on Amazon, but Dropbox built their own storage on Amazon early, then their own data center later. Uber has their own data center, right, so you can argue that at some point of scale it makes sense to build your own, so you don't need to be on Amazon or Google as your start, but it does give you a head start. Now the question is, in the future, can you build a SaaS application entirely on Amazon, Azure, or Google, without any custom code, right, can you hide read write call private SaaS, like a single instance of my SaaS application for you, John, or for you, Dave, that's your data, your workflow, your information personalized for you, so instead of this multi-tenet CRM system like Salesforce, I have a custom CRM system just for Dave, just for Jeff, just for Jerry, just for theCUBE, right? >> I think yes, for that, I think that's definitely a trend I would see happening. >> It's what Infor is trying to do, right, Charles Phillips says "Friends don't let friends "build data centers," but they've still got a big loss in legacy there, but it's an interesting model, focused on verticals or microverticals or like the healthcare example that you're giving, and lot of potential for something. >> Well here's why I think I like this because, I think, and I said this before in theCUBE maybe it's not the best way to say it is that, if you look at the benefit of AI, data-driven, the quality of the data and the power of the compute has to be there. AI will work well with all that stuff, but it's also specialized around the application's use case. So you have specialism around the application, but you don't have to build a full stack to do that, you could use a horizontally scalable cloud distribution system in your word, and then only create custom unique workloads for the app, where machine learning's involved, and AI, that's unique to the app, that's differentiation, that could be the business model, or the utility. So, multitenancy could exist in theory, at the scalable level, but unique at the top of the level so yes I would say I'd want that hosted in the most customized, agile, flexible way. So I would argue that that's the scenario. >> I think that's the future, I mean one of my, I think you were saying, Dave, friends don't let friends build data centers anymore, it's you probably don't need to build a data center anymore because you can actually build your own application on top of one of the two or three large cloud providers. So it's interesting to see what happens the next three, four years, we're going to see kind of a thousand flowers bloom of different apps, not everyone's going to make it, not everyone's going to be a huge Salesforce-like outcome, but there'll be a bunch of applications out there. >> And the IoT stuff is interesting to me, so observing a lot of what the IT guys are doing, it reminds me of people trying to make the Windows mobile phone, they're just trying to force IT standards down the IoT, what I've seen from AWS today is more of a bottoms up approach, build applications for operations technology people, which I think is the right way to go, what do you see in an IoT, IoT apps, what's the formula there? >> I think what Amazon announced today with their time series database, right, their Timestream prediction engine, plus their Outpost offering with the Vmware themselves, you're really seeing a combination of IoT and Edge, right, it's the whole idea is, one, there's a bunch of use cases for time series in IoT, because sentry data, cameras, self-driving cars, drones, et cetera, there's more data coming at you, it adds all of that. >> And Splunk has proven that big-time. >> Correct, Splunk's let 18 billion Marcap company, all on time series data, but number two, what's happening is, it's not necessarily centralized data, right, it's happening at the edge, your self-driving car, your cell phone, et cetera, so Outpost is really a way for Amazon to get closer to the edge, by pushing their compute towards your data center, towards remote office, branch office, and get closer to where the data is, so I think that'll be super interesting. >> Well the Elastic Inference engine is critical, now we got elasticity around inference, and then they got the chip set that worked Inferentia, that can work with the elastic service. That's a powerful combination. >> The AI plumbing war between Google and TetraFlow as technology there's like PyTorch, Google TPUs versus what Amazon is doing with inference chips today, versus what I'm sure Microsoft and else is doing, is fascinating to watch in terms of how you had a kind of a Intel Nvidia duopoly for a long time, and now you have Intel, Nvidia, and then everyone from Amazon, Google, Microsoft doing their own soul again, it's pretty fascinating to watch. >> What was the stat, he said 85% of the TensorFlow, cloud TensorFlow's running on AWS? >> Makes a lot of sense, I think he said Aurora's customers logoslide doubled, but let's break down real quick, to end the segment with the key areas that we see going on, at least my perspective, I want to get your reaction. Storage, major disruption, he emphasized a lot of that in the keynote, spent a lot of time on stores, actually I think more than EC2 if you look at it, two, databases, database war, storage rate configuration, and a holy trinity of networking, storage, and compute, that's evolving, databases, SageMaker, machine learning. All there and then over the top, yesterday's announcement of satellite as a service, that essentially kills the edge of the network, cause there is no edge if we have space satellites shooting connectivity to any device the world is now, there's no more edge, it's everywhere. So, your thoughts, those areas. Which one pops out as the most surprising or most relevant? >> I think it's consistent Amazon strategy, on the lowest layer they're trying to draw the cost to zero, so on storage, cheaper cheaper cheaper, they're driving the bottom layer to zero to get all your data. I think the second thing, the database layer, it makes sense, it's not open-source, right, time scale or time series, it's not, Timestream's not their open-source database, it's their own, so open-source, low cost, the lowest layer, their database stuff is mostly their own, Aurora, Dynamo, Timestream, right, because there's some level lock in there, which I think customers are worried about, so that's clever, it's not by accident, that's all proprietary, and then ML Services, on top of that, that's all cares with developers, and it's API locking, so clearly low-cost open-source for the bottom, proprietary data services that they're trying to own, and then API's on top of it. And so the higher up in the stack, the more and more Amazon, you look, the more and more Amazon you have to adopt as kind of a lock in stack, so it's a brilliant strategy the guys have been executing for the past six, seven years as you guys have seen firsthand, I think the most exciting thing, and the most shocking thing to me is this move towards this battle for the AI front, this ML AI front, I think we saw ML's the new sequel, right, that's the new war, right, against Amazon, Google, and Microsoft. >> And that's the future of applications, cause this is >> But you're right on, it's a knife fight for the data, and then you layer on machine intelligence on top of that, and you get cloud scale, and that's the innovation engine for the next 10 years. >> Alright Jerry Chen just unpacked the State of the Union of cloud, of course as an investor I got to ask the final question, how are you investing to take advantage of this wave, versus being on the wrong side of history? >> I have framers for everything, there's a framer on how to attack the cloud vendors, and so I'm looking at a couple things, one, a seams in between the clouds, right, or in between services, because they can't do everything well, and there were kind of these large continents, Amazon, Google, Azure, so I'm looking for seams between the three of them, I'm looking for two, deep areas of IP that they're not going into that you actually have proprietary tap, and then verticals of data, like source of the data, or workflows that these guys aren't great, and then finally kind of cross-data cross-cloud solution, so, something that gives you the ability to run on prem, off prem, Microsoft, Google, Azure. >> Yeah, fill in the white spaces, there are big white spaces, and then hope that could develop into, good. Jerry Chen, partner in Greylock , partners formerly Vmware part of the V Mafia, friend of theCUBE, great guest analysis here, with Dave Vellante and John Furrier, thanks for watching us, stay with us, more live coverage, day two of three days of wall-to-wall coverage at re:Invent, 52,000 people, the whole industry's here, you can see the formations, we're getting all of the data, we're bringing it to you, stay with us.

(logo snapping) >> Hey, welcome back everybody. Jeff Frick here with the Cube. We're in downtown San Francisco with International Women's Day. Stuff going on all around the world. Check social media. It's pretty exciting and definitely a movement. We decided to come down to the Accenture event. 400 people here at the hotel, Nikko. A lot of panels, a lot of great content, and we're excited to have our next quests. We have Paul Chapman. He's the CIO of Box. Paul, it's great to see you. And J.D. Sassaman, Senior Workshop Manager at Autodesk for the Pier 9 Tech Center. So J.D. jump in. I have to ask, J.D. What is the Pier 9 Tech Center? >> Yeah, it's a fantastic place, right here in downtown San Francisco. We have a wood shop, metal shop, robot arms, digital fabrication, rapid prototyping. It's literally a physical place to fabricate, prototype, iterate, and research within Autodesk. >> It's so cool. I don't think most people think of Autodesk as, you think as a software company, but not necessarily that you can touch, shave, >> Yeah. >> and drill, you know play with toys. >> Absolutely. It's where the rubber hits the road. You can design all day but if you can't make it, and we can't test what the customers are doing with the software to valid that we're making software that drives that thing getting build in the world, then we missing something. So there's where these centers, you know, they help Autodesk be authentically in touch with what our clients are doing. >> So part of today's topic was to put out this report, there's forty kind of factors that influence people, businesses, and culture, and diversity. And one of the big three buckets is about culture and leadership, be bold leadership. And it's pretty interesting in your panel, you talked about Box and being that kind of millennial-lead company. A lot of millennials compared to HP and some of the older companies. You had a quote. I wrote it down. You talked about a maniacal focus on culture fit. So Paul, I wonder if you can dive into a little bit about why that's important and how does it manifest itself in the day-to-day operations at Box? >> Something that we always done from the very beginning is, we've always been a people first company. And so what's really important is part of that is when you're hiring people into the company, they also have to fit the culture of the company. As we know, one of the hardest thing to hold on to when you're growing scale a company, is the culture. And so we not only hire for in sort of experience and capability, but also for the culture fit. And we maniacally do focus on that. Now, it can slow down our hiring process, but ultimately it's about preserving that culture. And the culture people first is very much about inclusion. It's very much about our employee resource groups. It's very much about the way we recruit, the way we hire, where we hire from as well. You know I think that millennials, you mentioned having driven millennial culture, millennials will actually interview the company for their values, for their views, for you know. Inclusion would be one of those things as well. So it's, >> Jeff: Right. >> actually even, I think it's going to become harder for companies to even recruit in the future if they don't have a, you know, diversity inclusion as, not as a side project, not as something that happens on the side, but as something that's baked into the company's cultures. >> Right. This is kind of ying and yang, right? 'Cause like you said, it probably slows down your hiring process. There's a lot of pressure to hire people knowing >> Paul: Yeah. >> you can get all the talent they want, but at the other time, you want retention. And you want people that are going to be around for awhile >> Paul: Yeah. >> when you do hire them, will be good contributors to the company >> Paul: That's right. >> for a long, long time. So, I image short-term lost, long-term gain when you stick to that. >> That's absolutely right. Who you work with and who work for is very, very important. And we have a very open social, collaborative culture. And I think generally what that does, and I worked in a number of organizations, is that it creates for a very motivated workforce and very productive workforce. >> J.D., I want to ask you kind about the growth of purpose-driven. You know, we've see it >> J.D.: Yep. >> again and again, I give a lot of credit to the younger kids coming up in terms of purpose being much higher on their rank of priorities of how they make their decisions. I wonder if you can talk, have you seen that in Autodesk in some of your new hires and is it changing the way you guys do things? >> Yeah, sure. And I think even more, more visibly for us, we have such a turn of residences who come and do work in research and prototyping at the shop. That we see a bigger volume there than I do in hiring, and what I really see is a similar. They want to know that we have a commitment to a culture of collaboration. That innovation isn't just a buzz word but is really going to be facilitated. By putting people in the place, with the machines, with the technical capabilities, but also with other people, who are going to think about their problem differently. And I think, you know we back that up with physical practices. We do a lot as a technical team that supports all those residences. By creating spaces to be curious and to learn, and irregardless how much technical expertise you have coming in, we want to learn from you and you want to learn from us. And when the team that's supporting that space really embodies that, people feel it. And they know that it's real. And they know this is a place that I come and ask questions I don't know answers to and not feel dumb about it. But go on the journey with you to find the answers. And that's really what we're facilitating, is people coming in with good questions. >> Right. _ And making a space where you could possibly find an answer you don't expect. And that comes from that culture. So we see that with the turn of people coming through the space, that they need to get it, and they need to know this is a place that they can really push the limits of where they've been before. >> And then how, have you seen the kind of top down push for that culture, in terms of supporting it, evolving it, you know, >> Paul: Yeah. >> over time, from the very top levels? >> J.D.: That's interesting. >> No, now I'll take a run if. Even just go to our company's values, and everybody, you know, has an employee badge. We have our company values in the back of every single badge. And one of our company values, there's a couple, actually we have 10 values in there. And I think they're all great values. One of them is make Mom proud. Okay, it's about, you know, before you make any decision, before you do anything, is this a decision that would make your Mom proud? The next one that is, I think, also goes to the culture of our company, is bring your blank self to work. And you can fill in blank with whatever you want to fill it in with. So these are values that have been thought through from the top of the company, that permeates all the way through the organization. And as you know, an organization, your values and your mission are very, very important to that culture. >> Jeff: Right. >> So we even just reworked our recruitment philosophy based upon hiring on diversity and inclusion as well. So these are things that are absolutely supported from the top down inside our organization. >> And how has that manifested? Do people quote the values in reference to company awards? Do people, how does it actually go from just the back of, you know, the back of your badge to implementation to everyday world? >> We have them in performance reviews. When people are, you know people sort of do their performance reviews in, and part of that is, how is this person upholding our values. And so, we've installed this, you know, deep understanding of the values of the company because that's what effectively holds us together from a culture stand point as well. >> Jeff: Right. >> Yeah, it's interesting I think with Pier 9 we've seen a real chicken and egg. Pier 9 was an experiment when it started five years ago. And I think what's happened is the experiment went well. And that leadership started to see this kind of experiment is bringing in a value that as a software company, we haven't been able to reach before, which is having people in the space innovating and collaborating building community in that way. So it's been interesting to see it trickle up. And I say it's been really been grass-root, and what I see is that now, you know, when they're recruiting at Autodesk, they bring the people to Pier 9 because it's an employee benefit. So, and we see how the videos that Pier 9 are getting made from the marketing department and has influenced how the videos are getting made when we talk about all throughout the company. So it's been very interesting, you know, they brought, they started the experiment that they thought would be valuable, and now the company is found out more and more what that value is. And now they're looking at it. I do we expand that with our network of technology centers? I do we reach more people? And what else does this feed back to the larger corporation? >> Right >> Yeah. >> If anything you just touched on it with your be the underscore person is, is even diversity within the regular, just the regular hires that maybe, just the regular white guy from 10 years ago, >> Paul: Yeah. >> before it would be fit in a box, right. We hired you, now fit in a box. We talked about, it's amazing to me the impact of clothing. >> Paul: Yeah. >> We talked about it in an earlier interview. You know, you're a great person. You do all this stuff. Now we hired you, we'll put you in a box. >> Yeah. >> Yeah. >> As oppose to now, there's kind of whole person concept, which is even diverse inside of the attributes >> Paul: That's it. >> that you're leveraging from the individual >> Paul: Yeah. >> employees to get more value. Seems to be just a really >> Paul: Yeah. >> significant trend that then is going to drive that innovation. To use that whole asset. >> Yeah, you know, I'll even add that, as I mentioned earlier, employee resource groups, right. Heavy support for creating employee resource groups. In fact, we just created a new one for, Belong, you know, this is for people that are maybe immigrants into the country that are now under fear and concern with the what's going on with certain immigration policies and laws and so on. >> Jeff: Right, right. >> And we have Box Women's Network, Box Women's Technology Network, we have Black Excellence Network, we have all these various different employee resource groups, but also what's happening is that these groups are also helping people to get connected with other people across the organization. And as companies grow and you have thousands of employees, how do you get connected with other people across your organization that are in a similar situation as yourself. And we're finding that it's helping build relations, helping to build connections. I think our cognitive thought, our problem-solving, and so on is actually significantly improved because of this. >> Alright, so we're getting the wrap sign. It's a busy day. I want to give you the last word before we cut off. If we sit down a year from now, at International Women's Day, what are you working on, what are your priorities, both as individually as well as, you know, from a company point of view for the next 12 months? J.D., I'll start with you. >> Yeah. I'm actually launching an organization right now called, The Workbench Alliance. It's a professional organization for women, gender non-binary folks, trans-women, super inclusive, working at the intersection of craft, technology, and design. It's a lot of what we facilitate at Pier 9, and I'm looking at how we build a professional network to promote, create visibility, and really more and more community around these sort of converging industries. Supporting each other and you know, kind of employee resource group, but outside the corporation, which I think it's going to benefit, certainly benefit Autodesk, but benefit everybody. >> Jeff: Right, right. >> You know, I'll go on one topic and that's machine learning. I think we that we're at a point, it's almost the tip of an iceberg, but we have over the last few years created more, and more, and more data. And now we're mining that data for intelligence. Machine learning is getting smarter, and smarter, and smarter. So not only are we looking at leveraging that ourselves at Box to add more value to the content that our customers store with us, but also I think it's an opportunity to do things around hiring on diversity. You know, I think there's a lot of learning we can do to weed out unconscious bias. How we screen, the screening process, the finding process, the recruitment process. So I'm a big believer of machine learning helping us in a lot of different ways. >> Alright. Well, J.D., Paul, thanks for taking a minute, >> Alright. >> from your day. I really enjoyed the conversation. >> Alright, thank you >> Great, thank you. >> I'm Jeff Frick, we're at the International Women's Day. The Accenture at downtown San Francisco. Thanks for watching. Catch you next time. (electronic beat theme music)