(upbeat music) >> Hello, everyone, and welcome to theCUBE's presentation of the "AWS Startup Showcase." This is season three, episode one. The focus of this episode is AI/ML: Top Startups Building Foundational Models, Infrastructure, and AI. It's great topics, super-relevant, and it's part of our ongoing coverage of startups in the AWS ecosystem. I'm your host, John Furrier, with theCUBE. Today, we're excited to be joined by Jay Marshall, VP of Business Development at Neural Magic. Jay, thanks for coming on theCUBE. >> Hey, John, thanks so much. Thanks for having us. >> We had a great CUBE conversation with you guys. This is very much about the company focuses. It's a feature presentation for the "Startup Showcase," and the machine learning at scale is the topic, but in general, it's more, (laughs) and we should call it "Machine Learning and AI: How to Get Started," because everybody is retooling their business. Companies that aren't retooling their business right now with AI first will be out of business, in my opinion. You're seeing massive shift. This is really truly the beginning of the next-gen machine learning AI trend. It's really seeing ChatGPT. Everyone sees that. That went mainstream. But this is just the beginning. This is scratching the surface of this next-generation AI with machine learning powering it, and with all the goodness of cloud, cloud scale, and how horizontally scalable it is. The resources are there. You got the Edge. Everything's perfect for AI 'cause data infrastructure's exploding in value. AI is just the applications. This is a super topic, so what do you guys see in this general area of opportunities right now in the headlines? And I'm sure you guys' phone must be ringing off the hook, metaphorically speaking, or emails and meetings and Zooms. What's going on over there at Neural Magic? >> No, absolutely, and you pretty much nailed most of it. I think that, you know, my background, we've seen for the last 20-plus years. Even just getting enterprise applications kind of built and delivered at scale, obviously, amazing things with AWS and the cloud to help accelerate that. And we just kind of figured out in the last five or so years how to do that productively and efficiently, kind of from an operations perspective. Got development and operations teams. We even came up with DevOps, right? But now, we kind of have this new kind of persona and new workload that developers have to talk to, and then it has to be deployed on those ITOps solutions. And so you pretty much nailed it. Folks are saying, "Well, how do I do this?" These big, generational models or foundational models, as we're calling them, they're great, but enterprises want to do that with their data, on their infrastructure, at scale, at the edge. So for us, yeah, we're helping enterprises accelerate that through optimizing models and then delivering them at scale in a more cost-effective fashion. >> Yeah, and I think one of the things, the benefits of OpenAI we saw, was not only is it open source, then you got also other models that are more proprietary, is that it shows the world that this is really happening, right? It's a whole nother level, and there's also new landscape kind of maps coming out. You got the generative AI, and you got the foundational models, large LLMs. Where do you guys fit into the landscape? Because you guys are in the middle of this. How do you talk to customers when they say, "I'm going down this road. I need help. I'm going to stand this up." This new AI infrastructure and applications, where do you guys fit in the landscape? >> Right, and really, the answer is both. I think today, when it comes to a lot of what for some folks would still be considered kind of cutting edge around computer vision and natural language processing, a lot of our optimization tools and our runtime are based around most of the common computer vision and natural language processing models. So your YOLOs, your BERTs, you know, your DistilBERTs and what have you, so we work to help optimize those, again, who've gotten great performance and great value for customers trying to get those into production. But when you get into the LLMs, and you mentioned some of the open source components there, our research teams have kind of been right in the trenches with those. So kind of the GPT open source equivalent being OPT, being able to actually take, you know, a multi-$100 billion parameter model and sparsify that or optimize that down, shaving away a ton of parameters, and being able to run it on smaller infrastructure. So I think the evolution here, you know, all this stuff came out in the last six months in terms of being turned loose into the wild, but we're staying in the trenches with folks so that we can help optimize those as well and not require, again, the heavy compute, the heavy cost, the heavy power consumption as those models evolve as well. So we're staying right in with everybody while they're being built, but trying to get folks into production today with things that help with business value today. >> Jay, I really appreciate you coming on theCUBE, and before we came on camera, you said you just were on a customer call. I know you got a lot of activity. What specific things are you helping enterprises solve? What kind of problems? Take us through the spectrum from the beginning, people jumping in the deep end of the pool, some people kind of coming in, starting out slow. What are the scale? Can you scope the kind of use cases and problems that are emerging that people are calling you for? >> Absolutely, so I think if I break it down to kind of, like, your startup, or I maybe call 'em AI native to kind of steal from cloud native years ago, that group, it's pretty much, you know, part and parcel for how that group already runs. So if you have a data science team and an ML engineering team, you're building models, you're training models, you're deploying models. You're seeing firsthand the expense of starting to try to do that at scale. So it's really just a pure operational efficiency play. They kind of speak natively to our tools, which we're doing in the open source. So it's really helping, again, with the optimization of the models they've built, and then, again, giving them an alternative to expensive proprietary hardware accelerators to have to run them. Now, on the enterprise side, it varies, right? You have some kind of AI native folks there that already have these teams, but you also have kind of, like, AI curious, right? Like, they want to do it, but they don't really know where to start, and so for there, we actually have an open source toolkit that can help you get into this optimization, and then again, that runtime, that inferencing runtime, purpose-built for CPUs. It allows you to not have to worry, again, about do I have a hardware accelerator available? How do I integrate that into my application stack? If I don't already know how to build this into my infrastructure, does my ITOps teams, do they know how to do this, and what does that runway look like? How do I cost for this? How do I plan for this? When it's just x86 compute, we've been doing that for a while, right? So it obviously still requires more, but at least it's a little bit more predictable. >> It's funny you mentioned AI native. You know, born in the cloud was a phrase that was out there. Now, you have startups that are born in AI companies. So I think you have this kind of cloud kind of vibe going on. You have lift and shift was a big discussion. Then you had cloud native, kind of in the cloud, kind of making it all work. Is there a existing set of things? People will throw on this hat, and then what's the difference between AI native and kind of providing it to existing stuff? 'Cause we're a lot of people take some of these tools and apply it to either existing stuff almost, and it's not really a lift and shift, but it's kind of like bolting on AI to something else, and then starting with AI first or native AI. >> Absolutely. It's a- >> How would you- >> It's a great question. I think that probably, where I'd probably pull back to kind of allow kind of retail-type scenarios where, you know, for five, seven, nine years or more even, a lot of these folks already have data science teams, you know? I mean, they've been doing this for quite some time. The difference is the introduction of these neural networks and deep learning, right? Those kinds of models are just a little bit of a paradigm shift. So, you know, I obviously was trying to be fun with the term AI native, but I think it's more folks that kind of came up in that neural network world, so it's a little bit more second nature, whereas I think for maybe some traditional data scientists starting to get into neural networks, you have the complexity there and the training overhead, and a lot of the aspects of getting a model finely tuned and hyperparameterization and all of these aspects of it. It just adds a layer of complexity that they're just not as used to dealing with. And so our goal is to help make that easy, and then of course, make it easier to run anywhere that you have just kind of standard infrastructure. >> Well, the other point I'd bring out, and I'd love to get your reaction to, is not only is that a neural network team, people who have been focused on that, but also, if you look at some of the DataOps lately, AIOps markets, a lot of data engineering, a lot of scale, folks who have been kind of, like, in that data tsunami cloud world are seeing, they kind of been in this, right? They're, like, been experiencing that. >> No doubt. I think it's funny the data lake concept, right? And you got data oceans now. Like, the metaphors just keep growing on us, but where it is valuable in terms of trying to shift the mindset, I've always kind of been a fan of some of the naming shift. I know with AWS, they always talk about purpose-built databases. And I always liked that because, you know, you don't have one database that can do everything. Even ones that say they can, like, you still have to do implementation detail differences. So sitting back and saying, "What is my use case, and then which database will I use it for?" I think it's kind of similar here. And when you're building those data teams, if you don't have folks that are doing data engineering, kind of that data harvesting, free processing, you got to do all that before a model's even going to care about it. So yeah, it's definitely a central piece of this as well, and again, whether or not you're going to be AI negative as you're making your way to kind of, you know, on that journey, you know, data's definitely a huge component of it. >> Yeah, you would have loved our Supercloud event we had. Talk about naming and, you know, around data meshes was talked about a lot. You're starting to see the control plane layers of data. I think that was the beginning of what I saw as that data infrastructure shift, to be horizontally scalable. So I have to ask you, with Neural Magic, when your customers and the people that are prospects for you guys, they're probably asking a lot of questions because I think the general thing that we see is, "How do I get started? Which GPU do I use?" I mean, there's a lot of things that are kind of, I won't say technical or targeted towards people who are living in that world, but, like, as the mainstream enterprises come in, they're going to need a playbook. What do you guys see, what do you guys offer your clients when they come in, and what do you recommend? >> Absolutely, and I think where we hook in specifically tends to be on the training side. So again, I've built a model. Now, I want to really optimize that model. And then on the runtime side when you want to deploy it, you know, we run that optimized model. And so that's where we're able to provide. We even have a labs offering in terms of being able to pair up our engineering teams with a customer's engineering teams, and we can actually help with most of that pipeline. So even if it is something where you have a dataset and you want some help in picking a model, you want some help training it, you want some help deploying that, we can actually help there as well. You know, there's also a great partner ecosystem out there, like a lot of folks even in the "Startup Showcase" here, that extend beyond into kind of your earlier comment around data engineering or downstream ITOps or the all-up MLOps umbrella. So we can absolutely engage with our labs, and then, of course, you know, again, partners, which are always kind of key to this. So you are spot on. I think what's happened with the kind of this, they talk about a hockey stick. This is almost like a flat wall now with the rate of innovation right now in this space. And so we do have a lot of folks wanting to go straight from curious to native. And so that's definitely where the partner ecosystem comes in so hard 'cause there just isn't anybody or any teams out there that, I literally do from, "Here's my blank database, and I want an API that does all the stuff," right? Like, that's a big chunk, but we can definitely help with the model to delivery piece. >> Well, you guys are obviously a featured company in this space. Talk about the expertise. A lot of companies are like, I won't say faking it till they make it. You can't really fake security. You can't really fake AI, right? So there's going to be a learning curve. They'll be a few startups who'll come out of the gate early. You guys are one of 'em. Talk about what you guys have as expertise as a company, why you're successful, and what problems do you solve for customers? >> No, appreciate that. Yeah, we actually, we love to tell the story of our founder, Nir Shavit. So he's a 20-year professor at MIT. Actually, he was doing a lot of work on kind of multicore processing before there were even physical multicores, and actually even did a stint in computational neurobiology in the 2010s, and the impetus for this whole technology, has a great talk on YouTube about it, where he talks about the fact that his work there, he kind of realized that the way neural networks encode and how they're executed by kind of ramming data layer by layer through these kind of HPC-style platforms, actually was not analogous to how the human brain actually works. So we're on one side, we're building neural networks, and we're trying to emulate neurons. We're not really executing them that way. So our team, which one of the co-founders, also an ex-MIT, that was kind of the birth of why can't we leverage this super-performance CPU platform, which has those really fat, fast caches attached to each core, and actually start to find a way to break that model down in a way that I can execute things in parallel, not having to do them sequentially? So it is a lot of amazing, like, talks and stuff that show kind of the magic, if you will, a part of the pun of Neural Magic, but that's kind of the foundational layer of all the engineering that we do here. And in terms of how we're able to bring it to reality for customers, I'll give one customer quote where it's a large retailer, and it's a people-counting application. So a very common application. And that customer's actually been able to show literally double the amount of cameras being run with the same amount of compute. So for a one-to-one perspective, two-to-one, business leaders usually like that math, right? So we're able to show pure cost savings, but even performance-wise, you know, we have some of the common models like your ResNets and your YOLOs, where we can actually even perform better than hardware-accelerated solutions. So we're trying to do, I need to just dumb it down to better, faster, cheaper, but from a commodity perspective, that's where we're accelerating. >> That's not a bad business model. Make things easier to use, faster, and reduce the steps it takes to do stuff. So, you know, that's always going to be a good market. Now, you guys have DeepSparse, which we've talked about on our CUBE conversation prior to this interview, delivers ML models through the software so the hardware allows for a decoupling, right? >> Yep. >> Which is going to drive probably a cost advantage. Also, it's also probably from a deployment standpoint it must be easier. Can you share the benefits? Is it a cost side? Is it more of a deployment? What are the benefits of the DeepSparse when you guys decouple the software from the hardware on the ML models? >> No you actually, you hit 'em both 'cause that really is primarily the value. Because ultimately, again, we're so early. And I came from this world in a prior life where I'm doing Java development, WebSphere, WebLogic, Tomcat open source, right? When we were trying to do innovation, we had innovation buckets, 'cause everybody wanted to be on the web and have their app and a browser, right? We got all the money we needed to build something and show, hey, look at the thing on the web, right? But when you had to get in production, that was the challenge. So to what you're speaking to here, in this situation, we're able to show we're just a Python package. So whether you just install it on the operating system itself, or we also have a containerized version you can drop on any container orchestration platform, so ECS or EKS on AWS. And so you get all the auto-scaling features. So when you think about that kind of a world where you have everything from real-time inferencing to kind of after hours batch processing inferencing, the fact that you can auto scale that hardware up and down and it's CPU based, so you're paying by the minute instead of maybe paying by the hour at a lower cost shelf, it does everything from pure cost to, again, I can have my standard IT team say, "Hey, here's the Kubernetes in the container," and it just runs on the infrastructure we're already managing. So yeah, operational, cost and again, and many times even performance. (audio warbles) CPUs if I want to. >> Yeah, so that's easier on the deployment too. And you don't have this kind of, you know, blank check kind of situation where you don't know what's on the backend on the cost side. >> Exactly. >> And you control the actual hardware and you can manage that supply chain. >> And keep in mind, exactly. Because the other thing that sometimes gets lost in the conversation, depending on where a customer is, some of these workloads, like, you know, you and I remember a world where even like the roundtrip to the cloud and back was a problem for folks, right? We're used to extremely low latency. And some of these workloads absolutely also adhere to that. But there's some workloads where the latency isn't as important. And we actually even provide the tuning. Now, if we're giving you five milliseconds of latency and you don't need that, you can tune that back. So less CPU, lower cost. Now, throughput and other things come into play. But that's the kind of configurability and flexibility we give for operations. >> All right, so why should I call you if I'm a customer or prospect Neural Magic, what problem do I have or when do I know I need you guys? When do I call you in and what does my environment look like? When do I know? What are some of the signals that would tell me that I need Neural Magic? >> No, absolutely. So I think in general, any neural network, you know, the process I mentioned before called sparcification, it's, you know, an optimization process that we specialize in. Any neural network, you know, can be sparcified. So I think if it's a deep-learning neural network type model. If you're trying to get AI into production, you have cost concerns even performance-wise. I certainly hate to be too generic and say, "Hey, we'll talk to everybody." But really in this world right now, if it's a neural network, it's something where you're trying to get into production, you know, we are definitely offering, you know, kind of an at-scale performant deployable solution for deep learning models. >> So neural network you would define as what? Just devices that are connected that need to know about each other? What's the state-of-the-art current definition of neural network for customers that may think they have a neural network or might not know they have a neural network architecture? What is that definition for neural network? >> That's a great question. So basically, machine learning models that fall under this kind of category, you hear about transformers a lot, or I mentioned about YOLO, the YOLO family of computer vision models, or natural language processing models like BERT. If you have a data science team or even developers, some even regular, I used to call myself a nine to five developer 'cause I worked in the enterprise, right? So like, hey, we found a new open source framework, you know, I used to use Spring back in the day and I had to go figure it out. There's developers that are pulling these models down and they're figuring out how to get 'em into production, okay? So I think all of those kinds of situations, you know, if it's a machine learning model of the deep learning variety that's, you know, really specifically where we shine. >> Okay, so let me pretend I'm a customer for a minute. I have all these videos, like all these transcripts, I have all these people that we've interviewed, CUBE alumnis, and I say to my team, "Let's AI-ify, sparcify theCUBE." >> Yep. >> What do I do? I mean, do I just like, my developers got to get involved and they're going to be like, "Well, how do I upload it to the cloud? Do I use a GPU?" So there's a thought process. And I think a lot of companies are going through that example of let's get on this AI, how can it help our business? >> Absolutely. >> What does that progression look like? Take me through that example. I mean, I made up theCUBE example up, but we do have a lot of data. We have large data models and we have people and connect to the internet and so we kind of seem like there's a neural network. I think every company might have a neural network in place. >> Well, and I was going to say, I think in general, you all probably do represent even the standard enterprise more than most. 'Cause even the enterprise is going to have a ton of video content, a ton of text content. So I think it's a great example. So I think that that kind of sea or I'll even go ahead and use that term data lake again, of data that you have, you're probably going to want to be setting up kind of machine learning pipelines that are going to be doing all of the pre-processing from kind of the raw data to kind of prepare it into the format that say a YOLO would actually use or let's say BERT for natural language processing. So you have all these transcripts, right? So we would do a pre-processing path where we would create that into the file format that BERT, the machine learning model would know how to train off of. So that's kind of all the pre-processing steps. And then for training itself, we actually enable what's called sparse transfer learning. So that's transfer learning is a very popular method of doing training with existing models. So we would be able to retrain that BERT model with your transcript data that we have now done the pre-processing with to get it into the proper format. And now we have a BERT natural language processing model that's been trained on your data. And now we can deploy that onto DeepSparse runtime so that now you can ask that model whatever questions, or I should say pass, you're not going to ask it those kinds of questions ChatGPT, although we can do that too. But you're going to pass text through the BERT model and it's going to give you answers back. It could be things like sentiment analysis or text classification. You just call the model, and now when you pass text through it, you get the answers better, faster or cheaper. I'll use that reference again. >> Okay, we can create a CUBE bot to give us questions on the fly from the the AI bot, you know, from our previous guests. >> Well, and I will tell you using that as an example. So I had mentioned OPT before, kind of the open source version of ChatGPT. So, you know, typically that requires multiple GPUs to run. So our research team, I may have mentioned earlier, we've been able to sparcify that over 50% already and run it on only a single GPU. And so in that situation, you could train OPT with that corpus of data and do exactly what you say. Actually we could use Alexa, we could use Alexa to actually respond back with voice. How about that? We'll do an API call and we'll actually have an interactive Alexa-enabled bot. >> Okay, we're going to be a customer, let's put it on the list. But this is a great example of what you guys call software delivered AI, a topic we chatted about on theCUBE conversation. This really means this is a developer opportunity. This really is the convergence of the data growth, the restructuring, how data is going to be horizontally scalable, meets developers. So this is an AI developer model going on right now, which is kind of unique. >> It is, John, I will tell you what's interesting. And again, folks don't always think of it this way, you know, the AI magical goodness is now getting pushed in the middle where the developers and IT are operating. And so it again, that paradigm, although for some folks seem obvious, again, if you've been around for 20 years, that whole all that plumbing is a thing, right? And so what we basically help with is when you deploy the DeepSparse runtime, we have a very rich API footprint. And so the developers can call the API, ITOps can run it, or to your point, it's developer friendly enough that you could actually deploy our off-the-shelf models. We have something called the SparseZoo where we actually publish pre-optimized or pre-sparcified models. And so developers could literally grab those right off the shelf with the training they've already had and just put 'em right into their applications and deploy them as containers. So yeah, we enable that for sure as well. >> It's interesting, DevOps was infrastructure as code and we had a last season, a series on data as code, which we kind of coined. This is data as code. This is a whole nother level of opportunity where developers just want to have programmable data and apps with AI. This is a whole new- >> Absolutely. >> Well, absolutely great, great stuff. Our news team at SiliconANGLE and theCUBE said you guys had a little bit of a launch announcement you wanted to make here on the "AWS Startup Showcase." So Jay, you have something that you want to launch here? >> Yes, and thank you John for teeing me up. So I'm going to try to put this in like, you know, the vein of like an AWS, like main stage keynote launch, okay? So we're going to try this out. So, you know, a lot of our product has obviously been built on top of x86. I've been sharing that the past 15 minutes or so. And with that, you know, we're seeing a lot of acceleration for folks wanting to run on commodity infrastructure. But we've had customers and prospects and partners tell us that, you know, ARM and all of its kind of variance are very compelling, both cost performance-wise and also obviously with Edge. And wanted to know if there was anything we could do from a runtime perspective with ARM. And so we got the work and, you know, it's a hard problem to solve 'cause the instructions set for ARM is very different than the instruction set for x86, and our deep tensor column technology has to be able to work with that lower level instruction spec. But working really hard, the engineering team's been at it and we are happy to announce here at the "AWS Startup Showcase," that DeepSparse inference now has, or inference runtime now has support for AWS Graviton instances. So it's no longer just x86, it is also ARM and that obviously also opens up the door to Edge and further out the stack so that optimize once run anywhere, we're not going to open up. So it is an early access. So if you go to neuralmagic.com/graviton, you can sign up for early access, but we're excited to now get into the ARM side of the fence as well on top of Graviton. >> That's awesome. Our news team is going to jump on that news. We'll get it right up. We get a little scoop here on the "Startup Showcase." Jay Marshall, great job. That really highlights the flexibility that you guys have when you decouple the software from the hardware. And again, we're seeing open source driving a lot more in AI ops now with with machine learning and AI. So to me, that makes a lot of sense. And congratulations on that announcement. Final minute or so we have left, give a summary of what you guys are all about. Put a plug in for the company, what you guys are looking to do. I'm sure you're probably hiring like crazy. Take the last few minutes to give a plug for the company and give a summary. >> No, I appreciate that so much. So yeah, joining us out neuralmagic.com, you know, part of what we didn't spend a lot of time here, our optimization tools, we are doing all of that in the open source. It's called SparseML and I mentioned SparseZoo briefly. So we really want the data scientists community and ML engineering community to join us out there. And again, the DeepSparse runtime, it's actually free to use for trial purposes and for personal use. So you can actually run all this on your own laptop or on an AWS instance of your choice. We are now live in the AWS marketplace. So push button, deploy, come try us out and reach out to us on neuralmagic.com. And again, sign up for the Graviton early access. >> All right, Jay Marshall, Vice President of Business Development Neural Magic here, talking about performant, cost effective machine learning at scale. This is season three, episode one, focusing on foundational models as far as building data infrastructure and AI, AI native. I'm John Furrier with theCUBE. Thanks for watching. (bright upbeat music)

>> theCUBE Presents UiPath FORWARD 5. Brought to you by UiPath. >> We're back in Las Vegas live. You're watching theCUBE's coverage of FORWARD 5 UiPath's customer event. My name is Dave Vellante. I'm here with David Nicholson. Our third Dave Espinoza is here, Director of Transformation at Cushman & Wakefield. And Marshall Sied is also here. He's the co-founder of Ashling Partners. Guys, thanks for coming on theCUBE. >> Thanks for having us. >> We know Cushman & Wakefield, huge real estate firm. We'll come back to that, wanted to dig into some of the industry trends. But Marshall, what is Ashling Partners all about? >> Great question, Dave. So Ashling Partners was founded with modern automation and continuous improvement in mind. So a lot of us used to implement large ERP systems, accounting transaction systems. We viewed RPA and broader intelligent automation as kind of the wave of the future. So everything we do has continuous process improvement and automation in mind together. So we don't want to decouple, we want bring those together in an agile way. >> It's interesting, Rob Enslin this morning on the stage was talking about the waves of industry tech that used ERP was where he started and you know, et cetera, internet and now automation. He's sort of drawing that analogy. It's interesting that you're seeing the same pattern. >> David: Were were you fist bumping in the back of the room? >> Marshall: Absolutely. >> Well, I mean there's a lot of opportunity there. A lot of money to be made on both ends. Dave, talk about your firm. What's going on in the industry specifically? You joined sort of as we're exiting the isolation economy. Right? So what's happening in the industry now? I mean, real estate has been up and down and, you know, wild ride, you know, with COVID. What are the big trends in the industry that are informing your automation strategy. >> And actually I joined probably like right in the middle of the isolation economy. So it was a really interesting time to like to, I'm sure for most people also onboarding into groups. But coming on Cushman, you know, Cushman itself is an organization that formed predominantly through acquisition and through merger, right? So three large companies came together. And so a lot of the times the sort of headaches and the opportunities that we find are probably no different than other legacy organizations have when they're merging three companies together, right? So lots of disparate process, lots of paper, lots of process that isn't really very standardized. And so really it's a lot about us trying to make sure that we're continuing to double down on really that continuous process improvement but also bringing technology, lots of different types of technologies to bear to solve different problems throughout the organization. >> Well is the pandemic a catalyst for the automation initiative? Or actually you guys started before that I think, Marshall started about 2018. But was it like a rocket booster during the pandemic or was it more sort of steady state? >> I think it was actually a little bit of both Dave. 'Cause the reality is there was already top down executive support at Cushman pre-pandemic. So Cushman was already moving on this in a big way and they had executive sponsorship across the C-suite. Pandemic came, never a good time for a pandemic, but it came at a decent time for Cushman because they were prepared. They had the foundation of governance, everything you need in a large enterprise to run a program. They had that in place so they were able to kind of just put kerosene on the fire when the pandemic hit with certain automation candidates. >> Because I often said that pre-pandemic, you know, digital transformation was kind of this buzzword. A lot of firms were sort of giving it lip service. But it sounds like Cushman actually had started down the digital transformation path and then obviously everybody was accelerated. If you weren't digital business, you were out of business. But but how tightly aligned, 'cause we heard this in the keynotes today, I'd like to test it. How tightly aligned is automation and digital transformation at Cushman. >> They're pretty synonymous really for us, right? So like it is really about bringing different types of technologies, whether it's like NLP. The other really interesting thing that we were talking about the keynote, right? There's just so much that is going into the UiPath platform that is enabling us and enabling the things that we want to do across the organization, right? So like natural language processing, document understanding, you know, cloud based items. Like there's just so much that we can leverage and it's really about that continuous process improvement. It's trying to make sure that we're aligning ourselves to the strategy that the organization is absolutely pushing, but making sure that we're doing it in smart ways, right? And that we're empowering our employees as we do it, right? So it's not just very top down from a COE, it's also very bottoms up, very citizen-led throughout the organization. >> So I think of this as a strategic initiative that happens over time. But how does Ashling, and Marshall, how do you engage with Cushman? Do you engage on a project by project basis? Do you have sort of a long term strategic arc that you're working to? >> Absolutely. >> How does that work? >> No, that's a great question. So we started project based, so we were a part of the co-establishment of the intelligent automation COE. So very outcome driven, top down approach as Dave mentioned. But we also had a wider aperture than just RPA. It was broader end to end automation experiences that was project based. We had so much kind of quantifiable evidence at that point that we wanted to go bigger with the program. Over time we matured into more of an agile DevOps methodology with the Cushman team. And Dave should certainly speak about the size of the Cushman team and how that's evolved over time, but- >> Because the two of you are in a partnership in terms of proving out the ROI of what you're doing. >> Oh, absolutely. >> Right? >> Marshall: Every day, every day. We all have numbers we got to hit, right? And that's just the reality of it. But in order to do that, you know, agile DevOps approach where you're, you know, releasing every two weeks into production, you need a dedicated team that has like a longer term roadmap that is coinciding with the Cushman objective. So that's what we have in place today, something we call build as a service and mROC. So kind of think of that as as plan, build, and then run. We're infused. You have to be infused with your clients if you're going to run an agile DevOps program. >> Is automation more self-funding? Marshall, I want to draw on your experience with ERP. Is automation more self-funding than other technology initiatives? And if so, why or if not, why not? >> It is, and it's a double edged sword actually. We talk about this all the time at Ashling. We've never worked in an enterprise technology space where there's more accountability to value delivered because it's so quantifiable and measurable. So every time a transaction runs you can measure- >> Dave: How are we doing? >> Exactly, I mean the ERP days, nobody questioned. They just, they thought we just have to move to S/4HANA, we just have to move to Oracle. >> We'll let you know in a couple years. >> That's it, yeah. >> I mean the stuff that we just saw earlier from Javier Castellanos, right, from Orange. It is very much like each transaction has a value associated to it. Each part of that transaction has a value associated to it. We're constantly monitoring the numbers of looking at our performance, right? There's very real value associated to maintaining business as usual for the 50 plus automations that we have in production, right? So like the business is really counting on us to maintain and to make sure that we're continuing to perform. But also that we're continuing to work with them to find additional value and additional opportunities, right? To make sure that we are saving money and finding dollars- >> But it's dropping hard dollars to the bottom line, right, that are quantifiable to your point. But what's the governor, what's the barrier to your ability to absorb whether it's new automation? Is it just expertise, talent, or you bandwidth? Is it the prioritization exercise and thinking intelligently about, you know not- >> Dave: All of that. >> So how do you, I guess you guys work together, but take us through that a little bit. >> I mean, we're constantly refining our approach. So we were just talking about our DevOps approach. You know, we started with I think maybe five or six different teams based on specific service lines. We modulated that recently to go to two teams, right? One specific to build and one specific to enhance. So we're constantly looking for and building new automations throughout the organization. And then also looking for incremental value to enhance the automations that we've got out there, right? So making them better, faster, making them more resilient so resolving technical debt, doing a lot of different things to make sure that we're as stable as we possibly can be. But it's not only that, it's really like making sure like we're just as pinched by everybody else in terms of like the great resignation and looking for talent. I think everybody here is basically looking for the exact same talent. And so it's really making sure that we have interesting work, we're doing interesting work, we're making people feel valued, and we're bringing value throughout the business. >> So I remember Bobby Patrick called me when he joined UiPath. He goes, "You're not going to believe what I'm doing now. You got to get on this train." And so I started looking to it and we actually downloaded, you know, the package and started playing with it. And we tried to do it with the competitors, we, you know, we couldn't. It was like call for pricing kind of thing. We're like, well that's interesting. But what we saw was my perspective, this bottoms up adoption. And I know there was top down as well. But then, I remember I was in the meeting when they announced the sort of process gold acquisition and then started, I said, "Okay, they're going for platform now." And then Microsoft came into the market like, okay, they got to differentiate there. Now you're seeing everybody, all the software companies think they should own every dollar that's ever spent on software. So SAP's doing it and ServiceNow. And so Marshall, from your perspective, how has this platform evolved? And then Dave, to the extent you can talk about it, how is that platform adoption taking shape within the organization? I mean, platforms are much more complicated than products and they require integration. How is UiPath doing there? >> I think they're doing fantastic in that category. If you think about, and it's been a natural evolution. They're not fighting inertia, they're following challenges of their clients, right? So RPA obviously came onto the scene hot, everybody understands the business rule driven automation value. Easy to, you know, make a quantifiable, tangible evidence with RPA. But exceptions happen in a business and upstream processes break that, you know, cause challenges with downstream automations. So what do you do? You have to go upstream. You have to have more automations, you have to have process discovery, process mining with process gold. You need to have the ability to have a better user experience interface, which we've definitely incorporated into Cushman when we didn't get adoption with certain automations that we like. You build low-code apps. People want that consumerization of technology in the enterprise and that allows them to adopt more of the automation which triggers the robots and then you report analytics on it. So that expansion's been pretty natural with UiPath and I think the next acquisition they just made with Re:infer's really interesting, 'cause now you're going even more upstream with communication mining, turning that into structure data that you potentially could automate or analyze so it's been natural. It's truly the only platform that we've encountered that can do all of this at this point. >> So a couple things there. You know, one is the nuance of adoptions, not just the function of the potential savings or, you know, revenue production or productivity. It's, you know, the experience because you got to have a great UI. And then what are you going to do with Re:infer? I don't know if you guys are adopting Re:infer but what do you see as the potential. Marshall and Dave, if you guys have visibility on it? >> I know we've talked about it Dave so I mean the potential's huge. I think it's going to be more of a question of change management for each organization just to feel comfortable with that. But I mean, think about all of the communication and the semi and unstructured data in an organization that comes, you know, via Slacks, Teams, emails. It's huge and it's significant if you can figure out the right identifiers that you want to trigger for your business. And then figure out is that something downstream we can automate or can we just analyze and make our business more effective, more efficient, or provide a better experience. So I think it's huge. We don't know how big this is yet, but we know that it's something that, I mean, think about Cushman, get brokers all day long that are communicating with clients and third parties. So it could be extremely significant. >> Sounds like a potential to eliminate email hell, but. >> Marshall: Heard those promises before. >> Maybe that's like the paperless office eventually. >> Well in our organizations, like 50, 40 to 50,000 people, you know, globally, right? And there are definitely service lines within our organization where probably it doesn't make sense for us to leverage UiPath and provide them the, you know, studio and low code, no code automation tools. But a lot of this NLP stuff and a lot of the content mining and the communication mining stuff, really has the ability for us to be able to sort of pinpoint opportunities at levels that we couldn't possibly do it before. So it was really very exciting to see the stuff that we were in there. I think when you start your organization, a lot of times you're a hammer looking for a nail, right? And you need to quickly move away from that. And so I think a lot of the stuff that UiPath is introducing, a lot of the stuff that they're bringing into their platform, really helps us to be moving away from that sort of orientation. >> Well when you think of this in terms of CI/CD, you know, people maybe have a better understanding of sort of the life cycles and, you know, the iteration calendar. Can you give us an example of something that went from an idea, something like, "Hey, I think we might be able to automate this process" through "Okay, yeah, let's do it." You try it, at some point there's sort of quality testing involved to make sure that it's achieving that we want to do. Can you give us an example of a process that you've gone through? And then how long do those things usually take? Are we talking weeks, months? What are we talking about from idea to establishing that, "Yeah, this is something we want to keep in place." >> Dave: We always want to make it faster. So we're especially always trying to find ways, especially upfront parts of the process. So a lot of the analysis, requirements gathering, you know, stuff that's not actual building. We want to make sure that we're shrinking that as much as possible, that we're also being comprehensive so that we're not building something that doesn't meet someone's needs, right? Or that just completely misses the mark. But I mean, invoice processing is a good example. We do that internally. Obviously, we have corporate accounting. We also do that on behalf of clients. And so a lot of times, you know, we're bringing some of the internal processes, we're using the technologies for document understanding, optical character reading, and machine learning. And we're doing that on behalf of clients, but we're also doing that internally. So to be able to use some of those processes and automations, sort of client facing plus internally, are big changes. Big changes for us. But I think the other thing too is like, we're always trying to make it faster and better. I think that's one of those also processes where we put something in place and we're constantly looking to enhance it, make it better based on the process that's out here. >> And you're applying automation to that upfront piece, the planning phase? Is that right? Or? >> Yeah, yeah, so a lot of it is about sort of the work that we do on behalf of clients. And there are teams who are specifically tasked to accounts. And so we're looking to find ways to make it easier for those accounts to get their bills paid, to get visibility into, you know, accounts payable, accounts receivable, their full end to end accounts lifecycle. And so yeah, we're doing that directly on behalf of clients and then we're doing that internally. >> How about the why UiPath question. Marshall, I think I heard you say that you're pretty much exclusively UiPath as your automation partner. Why? Why not play the field? Why UiPath? >> So I think it started in like 2017, 2018 for Ashling. We did an analysis of kind of an outside in of what, at that point was the big three of RPA, the vision and the roadmap and the open platform architecture of UiPath and just the self-awareness that, "Hey, we need to operate with other technologies in order for our clients to get the most value from automation." That was really the main reason, outside of the fact that we like working with UiPath, but it was just that complete vision of a platform as opposed to a tool. We felt like everybody else was more of a pointed tool and then UiPath had this platform approach and it was going to be necessary to go end to end like we all are trying to achieve. >> And UiPath continues to deepen that, right? They continues to support us with tons of new technology- >> How so? Can you be specific? >> I mean, when we're talking about document understanding, I mean, we're trying to leverage that for manual handwritten time sheets. We're also using it for, you know, Chronos integration, right? So like there's a lot of stuff that we're using it for and we can go to a single shop, right? To be able to do it, a single platform from a scalability and a supportability perspective, it's also a big game changer for us, right? As you start, you want to be able to scale, but you can't spend a ton of money supporting, you know, a hundred different platforms. You really got to invest and be smart about it. And UiPath for us was a really smart play. >> Are you budget limited relative, you're competing with other initiatives within the organization? Where's the funding come from? Is it from the business? Is it from IT? Is it a combination? >> It had been centrally funded and we are now moving into a different model. So we are constantly looking at, you know, the justification of value, speed to value, and proving it out to our business partners from all service lines and within all different functions of the organization. So we're at an interesting inflection point, but I think we also have a really good background that we're building on. >> I've been saying it all day, I've said it for years, at the UiPath events that they are awesome about putting customers on theCUBE and we love to hear from the customer stories because we get to sort of map what we hear in the keynotes and then test it, right, in the real world. And I also really love the fact that Marshall, UiPath always brings implementation partners so we can get the expertise and you have a wider observation space. So guys, thanks so much for coming on theCUBE and thanks for sharing your stories and good luck in the future. >> Thanks for having us. >> Appreciate it guys. >> Very welcome. >> Thank you. >> All right, keep it right there. Dave Nicholson and Dave Vellante live from Las Vegas UiPath FORWARD 5. We'll be right back right after this short break.

>> Narrator: Live from San Juan, Puerto Rico It's theCUBE. Covering Blockchain Unbound. Brought to you by Blockchain Industries. (latin music) >> Hello and welcome back to theCUBE exclusive coverage in Puerto Rico for Blockchain Unbound I'm John Furrier, your host, here covering all the action in Puerto Rico as the global society and industry come together. Our next guest is Marhall Taplits he's the Chief Strategy Officer and Co Founder of Nynja.biz, check out their site, Nynja.biz. Marshall, thanks for joining me. >> Thank you. >> So tell about what you guys do. You guys are doing some disruptive stuff, tell us about what you guys do, then it will jam into a conversation. >> Sure, so are you familiar with WeChat in China, for example? >> Yeah. >> Okay great. So I've personally been living in China 15 years, so we've watched kind of the birth of the Chinese internet, which as we know, is a little different than the regular internet. >> A lot of mobile users. >> A lot of mobile users, 800 million China mobile subscribers alone. WeChat, basically, is a platform that started off as just a messenger but basically what it's done is it's integrated into every facet of Chinese society. To give you an example, you go to a restaurant, you scan the QR code, the menu comes up, you pick the food, you pay for the food, it comes, you walk out. Everything like that is in China. Everything like that is in Wuzhen China. So what we've done is we've kind of taken this concept, and we're working on a global version of it, that's cryptocurrency based, and we are working specifically with Chinese companies in order to help them go global as part of the China One Belt One Road program and working with companies like Alibaba, what have you, in order to help Chinese companies go overseas and take what they've built in China but operate globally with cryptocurrency. >> Are you guys in China? Cause it's been hard for companies to start companies in China. So you're living in China or you're working in China? >> Yeah so because we live in Shenzhen, right next to it is Hong Kong. Hong Kong is where our company is based. Hong Kong, as you know, previous British colony, the legal system, and the financial system-- >> And you domicile in Hong Kong, that's where you're based? >> Me personally in Shenzhen, but the company is in Hong Kong. So we also have a Wyoming corporation in the US. >> That's where all the action is. >> That's right >> That's where WeChat is >> That's right >> Alibaba's got Alipay and then there's more business to business with their app. So I get that WeChat's been highly successful. In fact we have a huge following on WeChat, Sou Kanai, Niki Bond, free content. But that brings up the question of Chinese kind of showing the way with mobile expansion, so their users are heavily mobile savy. >> Marshall: That's right. >> This is pretty obvious when you think about it, but in America and around the world, that's going to translate to the new user experience. So in your opinion, how would you describe the expectations that users have? Because you're living on the front end of the wave of what mobile's doing, I mean there's a lot of gamification going on, some if it's kind of creepy, but what is your view of the expectations that users have and what's different about what's currently available in the webstac, and the 20 year old e-commerce stacks, that are out there? >> Sure, I think the most important thing is reducing friction, all right. You don't want to be using platforms where you can not do it wherever you are whenever you are, you don't want to have to go through payment processes, you don't want to have to re-authenticate yourself across whatever platforms you use. And interestingly, when I first went to China, it was all about copying what was in the west over to there, but actually it's kind of the opposite now, right, so we basically want to take this concept of the frictionless digital life, and make it a global opportunity. And especially with BlockChain and cryptocurrency you have that really as an opportunity, because if you look at all the apps that are out there, and the platforms that are out there, the only ones that have gone past a billion users, WhatsApp, Instagram, whatever are the free ones. But as soon as you layer in payment, it becomes very locked. And as big as WeChat is, and as big as LINE is, but ultimately it's locked into the Rem and B system or Reo in Korea, what have you, so the cryptocurrency is really the first opportunity that the world's had to create platforms that can get up to a billion, two billion, three billion users that are able to pay. And we just think that's a once in a lifetime opportunity and we want to be part of it. >> So I got to ask you about the impact that cloud computing has had on this, obviously we've seen cloud computing destroy the data center model, allow people to get time to value faster, mobile on top, big data analytics using data, all this stuff's awesome stuff. So the question is, is that, that's kind of a horizontally disruptive view, so these stacks that are built old way where I got to own the stack end to end, yeah there's some standardization on the lower end of the stack. But now you're thinking about more of a horizontal, I got jurisdictions, I got regions, I got countries, I got sovereignty, all these things are in the melting pot of the cryptocurrency BlockChain, de-centralized applications, are major impacts to all those things. How do you see that playing out because, that's kind of what developers worry about, oh shit will this work on that chain? I got Neo I got this I got that, so the plumbing is totally a moving train right now. >> Marshall: That's right. >> But the business models are pretty obvious. So there's like a business ops thing going on. What Dev opts did for Cloud, you got this new abstraction thing going on with this world. What's your view on that, do you agree? Or what's your take? >> Yeah well you pretty much nailed it. I mean basically what's happening is over the last 10 or 15 years people have finally accepted that having your own server is kind of silly, you know, and most people now will just spin up whatever they need in terms of resources on TheCloud. But over the last couple years, you're really going more toward Edge Cloud, where the way the clouds work, is that basically it's pushing to get the least amount of latency and store the data as close to the user as possible. And then there's also regulatory in some countries now in terms of, if your users are from this country, you have to legally store the data in this area. So this is all kind of evolving. And if you look at the BlockChain technology, I think it's the payment version of that. So for example, everyone's always concerned about getting in and out of Fiat Currency, and how am I going to get back to dollars, and this and that, but I think what's going to wind up happening, is this is going to get pushed towards the edges and there will be opportunities and ways with exchanges and what have you to get in and out. But more importantly, it's going to be like, just other currencies, so for example, I live in China but I come to the US a few times a year, I also travel to Europe, I have some dollars, I have some Euros, I have some Rem and B, when I leave China, I don't immediately sell all of my Rem and B, I just keep it because at some point I'm going to need it. And I think what's going to happen in the cryptocurrency space is, especially on the larger BlockChains, like Ethereum and Neo and what have you, is people are just going to get used to keeping some of it and they're going to stop worrying about what the exact exchange rate is and how am I going to get in and out, and this and that, and they're just going to start treating it as part of their currency stack that they keep. >> Yeah as long as there's some level of stability. It's just like, I remember when I was growing up, there was no Euro, every country had their own currency. You had the French Franc, the Swiss Francs, the Deutsche Mark, Lira, etc, etc. But you're seeing that the viability of the money aspect, cause at the end of the day there's two things that we've identified in analysis, and I was talking about it last night, talked about it this morning on theCUBE, is the killer apps for BlockChain cryptocurrency, these sorts of apps is two things, money and marketplaces. >> Marshall: That's right. >> Everything else is just kind of circling around those two. >> Well there's more but certainly that's the main part of it >> Money, moving around. So the UK just announced with coin based, the Financial Conduct Authority, reading the news yesterday, has essentially said we're going to allow for the fast payment system to convert to Fiat. This is a government, the UK is a nation. This is the beginning, to your point, that if they don't get up to speed, the edge of the network will democratize them and kind of circle the wagons, if you will, so it's already happening. >> Yeah and I think what governments are starting to realize is hey guys this is just a technology and not only do you don't really have jurisdiction to control it, but also that you don't even have the technical means. So Wyoming is a good example of regulation coming into play, that just kind of accepts the presence that this now exists, right. And they're not going to try to make it something and fit it into the old way. So, and in terms of the stability of these coins, I think it is important because people want stability, but in other ways, if you don't look at the exchange rate, it's actually way more stable than the current system, and I'll give an example. In the last month or two, the prices of cryptocurrency have dropped almost 40%. Now if the stock markets and the global affects markets drop 40%, you'd have blood in the streets. But the crypto market is asset based instead of debt based and because it's so structurally sound it's able to handle these wild swings without actually collapsing the system, so in may ways, it's way more stable, and then as the market gaps and the buy in of these currencies get bigger and bigger, of course it's going to be more stable over time. >> Well I mean its stable from a fail standpoint, but a lot of emotional instability. People losing money for the first time. >> But that's just because they're-- >> That's a lot of speculation, right? >> There's a lot of speculating and then if they're down they feel like they lost but, that's life. >> People that are into the game, like you, were long on this. So what would you explain to someone, cause I have two, a lot of friends that have two schools of thought, that's a total scam, don't associate with that, to oh my god, that's the next biggest wave, lets get our surfboards out there and lets get on this, there's a multiple set coming in, it's the biggest thing we've seen, and everything in between. How do you explain it to people for the first time? >> It's just your traditional curve, there's early adopters and what have you, and if you were one of the guys buying up domaine names in the early 90s, you know some people would say I can't believe you're spending $100,000 buying up domaine names, but some of them now are worth, you know, tens of millions of dollars. But again, this is the speculatory piece of it. And there's no shortage of opportunities for speculation and I encourage everybody to speculate a little bit because what it does is it gets you a taste of the technology. And usually, when you have some money on the line, you pay more attention, so if speculation is what gets people interested, and it gets them watching it and understanding the technology and using it, then I'm all for it, but people shouldn't be speculating with money they don't have. Anything could happen in the short term. Nobody knows what's going to happen with any specific currency. But in terms of the technology itself, this is a revolution way bigger than the internet itself. This is where you're getting, not only, communications like the internet, but financing governance and all as one. Programmable money, programmable contracts, that wipes out finance, it wipes out legal, it whites out governance in many ways. So this is a huge evolution in human society, and we've termed this Open Unity actually. And so we believe that society has to reach a state of open unity in order to go into the singularity as we would envision it wanting to be, as something that's under our control. >> Yeah and I think one of the things, first of all that's a great statement, well said. I'll just kind of put some reality on that, connect the dots, is that if you look at the trajectory of cloud computing, Amazon Web Services was laughed at years ago. S3 came out, compute storage building, basic building blocks and a slew more services. What Cloud did for software developers, and what they've disrupted from a business standpoint, dev ops, it's proven. What open source has done, even going back to the old red-hat days and linux, is that now a tier one global citizen in software, you look at those two trends, you can connect that dots to what you just said. And what made Cloud great was they made application developers have access to programmable infrastructure. >> Marshall: Exactly. >> You're talking about a whole nother level of software programmability, money, marketplace, society, >> Yeah you hit it on the head. >> We're there right? >> That's exactly right, so when a programmer wants to start a business, instead of going to create an LLC, and getting their EIN Tax ID or whatever, and when they want to go into Europe, and dealing with that and then trying to open a bank account, which is almost impossible, internationally now, instead of that, you just have your SDKs and your APIs or whatever and you've got access to money, program adding, you can take money, you can move money around, globally, frictionless, permissionless, with governancy, smart contracts-- >> They might not not need an SDK dashboard, its a console, click, click, click, smart contracts, governance, turn key. >> And one of the things we're working on with Nynja in particular, is this kind of on-demand marketplace and putting together a de-centralized teams for work. And this is all driven by smart contracts. So one of the issues with the economy is the huge booms and busts that people have in the economy. And if you look at the root cause of that, my personal opinion, is that it's because of payment terms. So for example, if I do work for you, and then there's an invoice, but it's not due for 30 days, now your business may be structurally sound, but the truth is your cashflow is all over the place. With BlockChain technology, we can actually do real time payments. You could be paid minute by minute, hour by hour. Real time, program, contract. So we're going to create very flat even money flows through the entire economy globally, and we're going to just completely remove these booms and busts that are really nothing more than just cashflow issues that are compounded and compounded at a global level. >> I mean I lived through the dot com bubble, I was actually part of it on the front end, on the euphoria side, as well as on the crash. Part of the whole search paradigm, google right there. Key words, all that stuff happening, growth, massive growth. So I saw that, the scammers in there, or the bubble people, that's what we called them. But the reality is, everything happened. It was pet foods online, you could get shopping delivered to your house. So again, to your point, it's a little euphoric right now, but what's different is, is you have now, community data. See what I see happening is, it's not a major bubble crash, because self government, self governing, self governance, is a community dynamic. So I think there's going to be a lot of self healing, inside the networks themselves. You're already seeing it here, a lot of people, bad act is being identified, investors flight to quality, looking at quality deals. Interesting times, your thoughts? >> Well I mean you know, we've been through many evolutions of society, we've had surf-dom, we've had monarchies, we've had representative democracies, we have all these things, and I just think the next evolution is decentralized governance. And we don't even know what that means yet, because it's just starting, but I think we can all, if we can close our eyes and really think about it. I think it's pretty obvious what the issues are with our current system and not just the US, but globally, and I think we have an opportunity here to build in organic program governance. And what's really special about BoxChain technology is if I program it to do X, it's going to do X. So we don't need to, I don't need to know who you are to trust you. I don't need to worry about where we're going to sue each other, or we're going to have arbitration if things go wrong. We're just going to make an agreement, and we're going to program it that way, and that's it. And now the next phase is, I could build on top of that trusting that that's just going to happen. So you can create these chains of trust, and that can happen anywhere in the world. So I think this is a whole nother-- >> Sounds like a bunch of web services. >> Well in many ways, in terms of the architecture, sure you could absolutely think of it like that. >> The reusability, the leverage is amazing. All right, so I want to just end the segment Marshall, take a minute to end the segment, to talk about what you're working on, Nynja coin, Nynja, N-Y-N-J-A .biz, you guys have a product, you got a BlockChain enabled platform, you got a coin, take a minute to explain what you're working on. >> Basically we want to provide the tools and services to help people live in this new reality. So in order to basically function in the world that we're entering into, we're going to need tools that far surpass what's currently available in terms of the messengers, the web sites, all these things. We need to be operating at a level that takes communication completely frictionless, payment completely frictionless, and governance completely frictionless. And we have to put this all together, and that's what we're doing with Nynja. We're staring with a global communicator, which is basically, if you want to take WeChat, telegram, whatever, but we have about 50 additional features that really take communications to the next level. And then on top of it, creating the baseline with cryptocurrency payment, and also smart contract wizards and helping people kind of get these teams going and get paid and organize their financial life in a de-centralized way. So we're just basically going to be the next generation of these messenger type platforms with BlockChain integrated. And what you're going to see is that over the next couple years you're going to get to the first companies that are achieving not just a billion or two billion or three billion users, but paying users, and we're going to be one of the probably three to five platforms that are offering tools at the global level like this. >> And have you got an IC already or not? >> We've just started our private ICO about two weeks ago. We're getting tremendous support in Asia. Quite frankly, the US is not seeing it as much-- >> Is it a utility token or security? >> Utility Token, and I think it's really telling, interesting, coming here. It's the first time I've been doing the presenting. We spoke yesterday at the d10e and we also spoke at d10e in Korea a week or two ago, and the response is incredible. And I think the reason is because-- >> The Asian market gets it. >> Well they're already living in this world within their own confines in terms of the messenger with their payment and governance built in, so when I tell them that we're going to do this globally with crypto, immediately they get it. I'm having trouble here, especially in these five minute pitches which is ridiculous, it's like a chop shop, I don't know how to communicate the idea within this short time frame, so, what I'm looking for while we're here this week is just to find people who really want to take an hour or two or even people like yourself who want to do interviews and just kind of really talk to people and really explain-- >> Well platform is complex, a lot of pieces to it. It's a system, but the value you offer is essentially offering developers, who are building products, for tools that you've built so they can scale faster. That sounds like your value. >> That's right and although I can't say specifically, we're also working on a deal that's going to get us started with about 15 million active users on day one, so that's very exciting and we're really really excited about that. >> And the coins will be utility of measures, what? >> Sorry? >> Well your utility coins going to be measuring what, what's the main token economics that drives the-- >> For the ICO economics? >> Your Nynja Coin. >> So basically we're releasing 5 billion tokens, 45% of them will be sold. There's five cents a token, so the hard cap, by definition is about 112 million, actually we're planning to do the public sale in April, but we may cancel it or postpone it just because the private sale is going really well, but we'll see how that goes. But in terms of once it's live, this will basically be the utility token of the entire eco-system, so anybody, not just within our Nynja App or platform, but even people, I don't know if you know XMPP federation, like back in the day-- >> Yeah you know about real messaging >> If you could think of us as the next version of XMPP federation, but using cryptocurrency in order to avoid bad actors by making it very expensive to do bad things, and very cheap to do good things and globally. >> So it's like Twitter you can create a bot instantly, but if there's coins involved, you'd have to spend to get it. >> That's right and also people could spin up nodes that are basically their own Twitters and decide if those Twitters of their own, their Nynja boxes of their own, are either just internally, or you could specify specifically context or group of context-- >> We agree, that's a great way to get bad actors out because it costs them money. And it's de-centralized, there's no single spot. >> That's right, if email came out today, when cryptocurrency existed, there would be no spam. Because it would be expensive as hell to send more than a few a second, but it would still be free and for everybody generally, and you wouldn't even have spam. So we think we can do that for messaging globally. >> Great. Marshall, thanks so much for coming on theCUBE, really appreciate it, check out Nynja. Marshall Taplits is the Chief Strategy Officer and co-founder of Nynja.biz, check them out online. Check out the website, it's in Asia, bringing that culture of mobile and fast moving, real time apps, to the rest of the developers. This is theCUBE coverage in Puerto Rico for BlockChain Unbound exclusive two days of coverage. We'll be right back with more, after this short break, thanks for watching.

(upbeat music) >> Welcome back, everyone. It's theCUBE's coverage here in Las Vegas. I'm John Furrier, your host of theCUBE with re:MARS. Amazon re:MARS stands for machine learning, automation, robotics, and space. Lot of great content, accomplishment. AI meets meets robotics and space, industrial IoT, all things data. And we've got two great guests here to unpack the AI side of it. Manoj Suvarna, Managing Director at AI Ecosystem at Deloitte and Arte Merritt, Conversational AI Lead at AWS. Manoj, it's great to see you CUBE alumni. Art, welcome to theCUBE. >> Thanks for having me. I appreciate it. >> So AI's the big theme. Actually, the big disconnect in the industry has been the industrial OT versus IT, and that's happening. Now you've got space and robotics meets what we know is machine learning and AI which we've been covering. This is the confluence of the new IoT market. >> It absolutely is. >> What's your opinion on that? >> Yeah, so actually it's taking IoT beyond the art of possible. One area that we have been working very closely with AWS. We're strategic alliance with them. And for the past six years, we have been investing a lot in transformations. Transformation as it relate to the cloud, transformation as it relate to data modernization. The new edge is essentially on AI and machine learning. And just this week, we announced a new solution which is more focused around enhancing contact center intelligence. So think about the edge of the contact center, where we all have experiences around dealing with customer service and how to really take that to the next level, challenges that clients are facing in every part of that business. So clearly. >> Well, Conversational AI is a good topic. Talk about the relationship with Deloitte and Amazon for a second around AI because you guys have some great projects going on right now. That's well ahead of the curve on solving the scale problem 'cause there's a scale and problem, practical problem and then scale. What's the relationship with Amazon and Deloitte? >> We have a great alliance and relationship. Deloitte brings that expertise to help folks build high quality, highly effective conversational AI and enterprises are implementing these solutions to really try to improve the overall customer experience. So they want to help agents improve productivity, gain insights into the reasons why folks are calling but it's really to provide that better user experience being available 24/7 on channels users prefer to interact. And the solutions that Deloitte is building are highly advanced, super exciting. Like when we show demos of them to potential customers, the eyes light up and they want those solutions. >> John: Give an example when their eyes light up. What are you showing there? >> One solution, it's called multimodal interfaces. So what this is, is when you're call into like a voice IVR, Deloitte's solution will send the folks say a mobile app or a website. So the person can interact with both the phone touching on the screen and the voice and it's all kept in sync. So imagine you call the doctor's office or say I was calling a airline and I want to change my flight or sorry, change the seat. If they were to say, seat 20D is available. Well, I don't know what that means, but if you see the map while you're talking, you can say, oh, 20D is the aisle. I'm going to select that. So Deloitte's doing those kind of experiences. It's incredible. >> Manoj, this is where the magic comes into play when you bring data together and you have integration like this. Asynchronously or synchronously, it's all coming together. You have different platforms, phone, voice, silo databases potentially, the old way. Now, the new ways integrating. What makes it all work? What's the key to success? >> Yeah, it's certainly not a trivial feat. Bringing together all of these ecosystems of relationships, technologies all put together. We cannot do it alone. This is where we partner with AWS with some of our other partners like Salesforce and OneReach and really trying to bring a symphony of some of these solutions to bear. When you think about, going back to the example of contact center, the challenges that the pandemic posed in the last couple of years was the fact that who's a humongous rise in volume of number of calls. You can imagine people calling in asking for all kinds of different things, whether it's airlines whether it is doctor's office and retail. And then couple with that is the fact that there's the labor shortage. And how do you train agents to get them to be productive enough to be able to address hundreds or thousands of these calls? And so that's where we have been starting to, we have invested in those solutions bringing those technologies together to address real client problems, not just slideware but actual production environments. And that's where we launched this solution called TrueServe as of this week, which is really a multimodal solution that is built with preconceived notions of technologies and libraries where we can then be industry agnostic and be able to deliver those experiences to our clients based on whatever vertical or industry they're in. >> Take me through the client's engagement here because I can imagine they want to get a practical solution. They're going to want to have it up and running, not like a just a chatbot, but like they completely integrated system. What's the challenge and what's the outcome first set of milestones that you see that they do first? Do they just get the data together? Are they deploying a software solution? What's the use cases? >> There's a couple different use cases. We see there's the self-service component that we're talking about with the chatbots or voice IVR solutions. There's also use cases for helping the agents, so real-time agent assist. So you call into a contact center, it's transcribed in real time, run through some sort of knowledge base to give the agents possible answers to help the user out, tying in, say the Salesforce data, CRM data, to know more about the user. Like if I was to call the airline, it's going to say, "Are you calling about your flight to San Francisco tomorrow?" It knows who I am. It leverages that stuff. And then the key piece is the analytics knowing why folks are calling, not just your metrics around, length of calls or deflections, but what were the reasons people were calling in because you can use that data to improve your underlying products or services. These are the things that enterprise are looking for and this is where someone like Deloitte comes in, brings that expertise, speeds up the time to market and really helps the customers. >> Manoj, what was the solution you mentioned that you guys announced? >> Yeah, so this is called Deloitte TrueServe. And essentially, it's a combination of multiple different solutions combinations from AWS, from Salesforce, from OneReach. All put together with our joint engineering and really delivering that capability. Enhancing on that is the analytics component, which is really critical, especially because when you think about the average contact center, less than 10% of the data gets analyzed today, and how do you then extract value out of that data and be able to deliver business outcomes. >> I was just talking to some of the other day about Zoom. Everyone records their zoom meetings, and no one watches them. I mean, who's going to wade through that. Call center is even more high volume. We're talking about massive data. And so will you guys automate that? Do you go through every single piece of data, every call and bring it down? Is that how it works? >> Go ahead. >> There's just some of the things you can do. Analyze the calls for common themes, like figuring out like topic modeling, what are the reasons people are calling in. Summarizing that stuff so you can see what those underlying issues are. And so that could be, like I was mentioning, improving the product or service. It could also be for helping train the agents. So here's how to answer that question. And it could even be reinforcing positive experiences maybe an agent had a particular great call and that could be a reference for other folks. >> Yeah, and also during the conversation, when you think about within 60 to 90 seconds, how do you identify the intonation, the sentiments of the client customer calling in and be able to respond in real time for the challenges that they might be facing and the ability to authenticate the customer at the same time be able to respond to them. I think that is the advancements that we are seeing in the market. >> I think also your point about the data having residual values also excellent because this is a long tail of value in this data, like for predictions and stuff. So NASA was just on before you guys came on, talking about the Artemis project and all the missions and they have to run massive amounts of simulations. And this is where I've kind of seen the dots connect here. You can run with AI, run all the heavy lifting without human touching it to get that first ingestion or analysis, and then iterating on the data based upon what else happens. >> Manoj: Absolutely. >> This is now the new normal, right? Is this? >> It is. And it's transverse towards across multiple domains. So the example we gave you was around Conversational AI. We're now looking at that for doing predictive analytics. Those are some examples that we are doing jointly with AWS SageMaker. We are working on things like computer vision with some of the capabilities and what computer vision has to offer. And so when you think about the continuum of possibilities of what we can bring together from a tools, technology, services perspective, really the sky is the limit in terms of delivering these real experiences to our clients. >> So take me through a customer. Pretending I'm a customer, I get it. I got to do this. It's a competitive advantage. What are the outcomes that they are envisioning? What are some of the patterns you're seeing with customers? What outcomes are they expecting and what kind of high level upside you see them envisioning coming out of the data? >> So when you think about the CxOs today and the board, a lot of them are thinking about, okay, how do you build more efficiency in those system? How do you enable a technology or solution for them to not only increase their top line but as well as their bottom line? How do you enhance the customer experience, which in this case is spot on because when you think about, when customers go repeat to a vendor, it's based on quality, it's based on price. Customer experience is now topping that where your first experience, whether it's through a chat or a virtual assistant or a phone call is going to determine the longevity of that customer with you as a vendor. And so clearly, when you think about how clients are becoming AI fuel, this is where we are bringing in new technologies, new solutions to really push the art to the limit and the art of possible. >> You got a playbook too to do this? >> Yeah, yeah, absolutely. We have done that. And in fact, we are now taking that to the next level up. So something that I've mentioned about this before, which is how do you trust an AI system as it's building up. >> Hold on, I need to plug in. >> Yeah, absolutely. >> I put this here for a reason to remind me. No, but also trust is a big thing. Just put that trustworthy. This is an AI ethics question. >> Arte: It's a big. >> Let's get into it. This is huge. Data's data. Data can be biased from coming in >> Part of it, there are concerns you have to look at the bias in the data. It's also how you communicate through these automated channels, being empathetic, building trust with the customer, being concise in the answers and being accessible to all sorts of different folks and how they might communicate. So it's definitely a big area. >> I mean, you think about just normal life. We all lived situations where we got a text message from a friend or someone close to us where, what the hell, what are you saying? And they had no contextual bad feelings about it or, well, there's misunderstandings 'cause the context isn't there 'cause you're rapid fire them on the subway. I'm riding my bike. I stop and text, okay, I'm okay. Church response could mean I'm busy or I'm angry. Like this is now what you said about empathy. This is now a new dynamic in here. >> Oh, the empathy is huge, especially if you're say a financial institution or building that trust with folks and being empathetic. If someone's reaching out to a contact center, there's a good chance they're upset about something. So you have to take that. >> John: Calm them down first. >> Yeah, and not being like false like platitude kind of things, like really being empathetic, being inclusive in the language. Those are things that you have conversation designers and linguistics folks that really look into that. That's why having domain expertise from folks like Deloitte come in to help with that. 'Cause maybe if you're just building the chat on your own, you might not think of those things. But the folks with the domain expertise will say like, Hey, this is how you script it. It's the power of words, getting that message across clearly. >> The linguistics matter? >> Yeah, yeah. >> It does. >> By vertical too, I mean, you could pick any the tribe, whatever orientation and age, demographics, genders. >> All of those things that we take for granted as a human. When you think about trust, when you think about bias, when you think about ethics, it just gets amplified. Because now you're dealing with millions and millions of data points that may or may not be the right direction in terms of somebody's calling in depending on what age group they're in. Some questions might not be relevant for that age group. Now a human can determine that, but a bot cannot. And so how do you make sure that when you look at this data coming in, how do you build models that are ethically aware of the contextual algorithms and the alignment with it and also enabling that experience to be much enhanced than taking it backwards, and that's really. >> I can imagine it getting better with as people get scaled up a bit 'cause then you're going to have to start having AI to watch the AI at some point, as they say. Where are we in the progress in the industry right now? Because I know there's been a lot of news stories around, ethics and AI and bias and it's a moving train actually, but still problems are going to be solved. Are we at the tipping point yet? Are we still walking in before we crawl or crawling before we walk? I should say, I mean, where are we? >> I think we are in between a crawling or walk phase. And the reason for that is because it varies depending on whether you're regulated industry or unregulated. In the regulated industry, there are compliance regulations requirements, whether it's government whether it's banking, financial institutions where they have to meet Sarbanes-Oxley and all kinds of compliance requirements, whereas an unregulated industry like retail and consumer, it is anybody's gain. And so the reality of it is that there is more of an awareness now. And that's one of the reasons why we've been promoting this jointly with AWS. We have a framework that we have established where there are multiple pillars of trust, bias, privacy, and security that companies and organizations need to think about. Our data scientists, ML engineers need to be familiar with it, but because while they're super great in terms of model building and development, when it comes to the business, when it comes to the client or a customer, it is super important for them to trust this platform, this algorithm. And that is where we are trying to build that momentum, bring that awareness. One of my colleagues has written this book "Trustworthy AI". We're trying to take the message out to the market to say, there is a framework. We can help you get there. And certainly that's what we are doing. >> Just call Deloitte up and you're going to take care of them. >> Manoj: Yeah. >> On the Amazon side, Amazon Web Services. I always interview Swami every year at re:Invent and he always get the updates. He's been bullish on this for a long time on this Conversational AI. What's the update on the AWS side? Where are you guys at? What's the current trends that you're riding? What wave are you riding right now? >> So some of the trends we see in customer interest, there's a couple of things. One is the multimodal interfaces we we're just chatting about where the voice IVA is synced with like a web or mobile experience, so you take that full advantage of the device. The other is adding additional AI into the Conversational AI. So one example is a customer that included intelligent document processing as part of the chatbot. So instead of typing your name and address, take a photo of your driver's license. It was an insurance onboarding chatbot, so you could take a photo of your existing insurance policy. It'll extract that information to build the new insurance policy. So folks get excited about that. And the third area we see interest is what's called multi-bot orchestration. And this is where you can have one main chatbot. Marshall user across different sub-chatbots based on the use case persona or even language. So those things get people really excited and then AWS is launching all sorts of new features. I don't know which one is coming out. >> I know something's coming out tomorrow. He's right at corner. He's big smile on his face. He wouldn't tell me. It's good. >> We have for folks like the closer alliance relationships, we we're able to get previews. So there a preview of all the new stuff. And I don't know what I could, it's pretty exciting stuff. >> You get in trouble if you spill the beans here. Don't, be careful. I'll watch you. We'll talk off camera. All exciting stuff. >> Yeah, yeah. I think the orchestrator bot is interesting. Having the ability to orchestrate across different contextual datasets is interesting. >> One of the areas where it's particularly interesting is in financial services. Imagine a bank could have consumer accounts, merchant accounts, investment banking accounts. So if you were to chat with the chatbot and say I want to open account, well, which account do you mean? And so it's able to figure out that context to navigate folks to those sub-chatbots behind the scenes. And so it's pretty interesting style. >> Awesome. Manoj while we're here, take a minute to quickly give a plug for Deloitte. What your program's about? What customers should expect if they work with you guys on this project? Give a quick commercial for Deloitte. >> Yeah, no, absolutely. I mean, Deloitte has been continuing to lead the AI field organization effort across our client base. If you think about all the Fortune 100, Fortune 500, Fortune 2000 clients, we certainly have them where they are in advanced stages of multiple deployments for AI. And we look at it all the way from strategy to implementation to operational models. So clients don't have to do it alone. And we are continuing to build our ecosystem of relationships, partnerships like the alliances that we have with AWS, building the ecosystem of relationships with other emerging startups, to your point about how do you continue to innovate and bring those technologies to your clients in a trustworthy environment so that we can deliver it in production scale. That is essentially what we're driving. >> Well, Arte, there's a great conversation and the AI will take over from here as we end the segment. I see a a bot coming on theCUBE later and there might be CUBE be replaced with robots. >> Right, right, right, exactly. >> I'm John Furrier, calling from Palo Alto. >> Someday, CUBE bot. >> You can just say, Alexa do my demo for me or whatever it is. >> Or digital twin for John. >> We're going to have a robot on earlier do a CUBE interview and that's Dave Vellante. He'd just pipe his voice in and be fun. Well, thanks for coming on, great conversation. >> Thank you. Thanks for having us. >> CUBE coverage here at re:MARS in Las Vegas. Back to the event circle. We're back in the line. Got re:Inforce and don't forget re:Invent at the end of the year. CUBE coverage of this exciting show here. Machine learning, automation, robotics, space. That's MARS, it's re:MARS. I'm John Furrier. Thanks for watching. (gentle music)

>> From theCUBE studios in Palo Alto in Boston, bringing you data driven insights from theCUBE and ETR. This is "Breaking Analysis" with Dave Vellante. >> Snowflake Summit 2022 underscored that the ecosystem excitement which was once forming around Hadoop is being reborn, escalated and coalescing around Snowflake's data cloud. What was once seen as a simpler cloud data warehouse and good marketing with the data cloud is evolving rapidly with new workloads of vertical industry focus, data applications, monetization, and more. The question is, will the promise of data be fulfilled this time around, or is it same wine, new bottle? Hello, and welcome to this week's Wikibon CUBE Insights powered by ETR. In this "Breaking Analysis," we'll talk about the event, the announcements that Snowflake made that are of greatest interest, the major themes of the show, what was hype and what was real, the competition, and some concerns that remain in many parts of the ecosystem and pockets of customers. First let's look at the overall event. It was held at Caesars Forum. Not my favorite venue, but I'll tell you it was packed. Fire Marshall Full, as we sometimes say. Nearly 10,000 people attended the event. Here's Snowflake's CMO Denise Persson on theCUBE describing how this event has evolved. >> Yeah, two, three years ago, we were about 1800 people at a Hilton in San Francisco. We had about 40 partners attending. This week we're close to 10,000 attendees here. Almost 10,000 people online as well, and over over 200 partners here on the show floor. >> Now, those numbers from 2019 remind me of the early days of Hadoop World, which was put on by Cloudera but then Cloudera handed off the event to O'Reilly as this article that we've inserted, if you bring back that slide would say. The headline it almost got it right. Hadoop World was a failure, but it didn't have to be. Snowflake has filled the void created by O'Reilly when it first killed Hadoop World, and killed the name and then killed Strata. Now, ironically, the momentum and excitement from Hadoop's early days, it probably could have stayed with Cloudera but the beginning of the end was when they gave the conference over to O'Reilly. We can't imagine Frank Slootman handing the keys to the kingdom to a third party. Serious business was done at this event. I'm talking substantive deals. Salespeople from a host sponsor and the ecosystems that support these events, they love physical. They really don't like virtual because physical belly to belly means relationship building, pipeline, and deals. And that was blatantly obvious at this show. And in fairness, all theCUBE events that we've done year but this one was more vibrant because of its attendance and the action in the ecosystem. Ecosystem is a hallmark of a cloud company, and that's what Snowflake is. We asked Frank Slootman on theCUBE, was this ecosystem evolution by design or did Snowflake just kind of stumble into it? Here's what he said. >> Well, when you are a data clouding, you have data, people want to do things with that data. They don't want just run data operations, populate dashboards, run reports. Pretty soon they want to build applications and after they build applications, they want build businesses on it. So it goes on and on and on. So it drives your development to enable more and more functionality on that data cloud. Didn't start out that way, you know, we were very, very much focused on data operations. Then it becomes application development and then it becomes, hey, we're developing whole businesses on this platform. So similar to what happened to Facebook in many ways. >> So it sounds like it was maybe a little bit of both. The Facebook analogy is interesting because Facebook is a walled garden, as is Snowflake, but when you come into that garden, you have assurances that things are going to work in a very specific way because a set of standards and protocols is being enforced by a steward, i.e. Snowflake. This means things run better inside of Snowflake than if you try to do all the integration yourself. Now, maybe over time, an open source version of that will come out but if you wait for that, you're going to be left behind. That said, Snowflake has made moves to make its platform more accommodating to open source tooling in many of its announcements this week. Now, I'm not going to do a deep dive on the announcements. Matt Sulkins from Monte Carlo wrote a decent summary of the keynotes and a number of analysts like Sanjeev Mohan, Tony Bear and others are posting some deeper analysis on these innovations, and so we'll point to those. I'll say a few things though. Unistore extends the type of data that can live in the Snowflake data cloud. It's enabled by a new feature called hybrid tables, a new table type in Snowflake. One of the big knocks against Snowflake was it couldn't handle and transaction data. Several database companies are creating this notion of a hybrid where both analytic and transactional workloads can live in the same data store. Oracle's doing this for example, with MySQL HeatWave and there are many others. We saw Mongo earlier this month add an analytics capability to its transaction system. Mongo also added sequel, which was kind of interesting. Here's what Constellation Research analyst Doug Henschen said about Snowflake's moves into transaction data. Play the clip. >> Well with Unistore, they're reaching out and trying to bring transactional data in. Hey, don't limit this to analytical information and there's other ways to do that like CDC and streaming but they're very closely tying that again to that marketplace, with the idea of bring your data over here and you can monetize it. Don't just leave it in that transactional database. So another reach to a broader play across a big community that they're building. >> And you're also seeing Snowflake expand its workload types in its unique way and through Snowpark and its stream lit acquisition, enabling Python so that native apps can be built in the data cloud and benefit from all that structure and the features that Snowflake is built in. Hence that Facebook analogy, or maybe the App Store, the Apple App Store as I propose as well. Python support also widens the aperture for machine intelligence workloads. We asked Snowflake senior VP of product, Christian Kleinerman which announcements he thought were the most impactful. And despite the who's your favorite child nature of the question, he did answer. Here's what he said. >> I think the native applications is the one that looks like, eh, I don't know about it on the surface but he has the biggest potential to change everything. That's create an entire ecosystem of solutions for within a company or across companies that I don't know that we know what's possible. >> Snowflake also announced support for Apache Iceberg, which is a new open table format standard that's emerging. So you're seeing Snowflake respond to these concerns about its lack of openness, and they're building optionality into their cloud. They also showed some cost op optimization tools both from Snowflake itself and from the ecosystem, notably Capital One which launched a software business on top of Snowflake focused on optimizing cost and eventually the rollout data management capabilities, and all kinds of features that Snowflake announced that the show around governance, cross cloud, what we call super cloud, a new security workload, and they reemphasize their ability to read non-native on-prem data into Snowflake through partnerships with Dell and Pure and a lot more. Let's hear from some of the analysts that came on theCUBE this week at Snowflake Summit to see what they said about the announcements and their takeaways from the event. This is Dave Menninger, Sanjeev Mohan, and Tony Bear, roll the clip. >> Our research shows that the majority of organizations, the majority of people do not have access to analytics. And so a couple of the things they've announced I think address those or help to address those issues very directly. So Snowpark and support for Python and other languages is a way for organizations to embed analytics into different business processes. And so I think that'll be really beneficial to try and get analytics into more people's hands. And I also think that the native applications as part of the marketplace is another way to get applications into people's hands rather than just analytical tools. Because most people in the organization are not analysts. They're doing some line of business function. They're HR managers, they're marketing people, they're sales people, they're finance people, right? They're not sitting there mucking around in the data, they're doing a job and they need analytics in that job. >> Primarily, I think it is to contract this whole notion that once you move data into Snowflake, it's a proprietary format. So I think that's how it started but it's usually beneficial to the customers, to the users because now if you have large amount of data in paket files you can leave it on S3, but then you using the Apache Iceberg table format in Snowflake, you get all the benefits of Snowflake's optimizer. So for example, you get the micro partitioning, you get the metadata. And in a single query, you can join, you can do select from a Snowflake table union and select from an iceberg table and you can do store procedure, user defined function. So I think what they've done is extremely interesting. Iceberg by itself still does not have multi-table transactional capabilities. So if I'm running a workload, I might be touching 10 different tables. So if I use Apache Iceberg in a raw format, they don't have it, but Snowflake does. So the way I see it is Snowflake is adding more and more capabilities right into the database. So for example, they've gone ahead and added security and privacy. So you can now create policies and do even cell level masking, dynamic masking, but most organizations have more than Snowflake. So what we are starting to see all around here is that there's a whole series of data catalog companies, a bunch of companies that are doing dynamic data masking, security and governance, data observability which is not a space Snowflake has gone into. So there's a whole ecosystem of companies that is mushrooming. Although, you know, so they're using the native capabilities of Snowflake but they are at a level higher. So if you have a data lake and a cloud data warehouse and you have other like relational databases, you can run these cross platform capabilities in that layer. So that way, you know, Snowflake's done a great job of enabling that ecosystem. >> I think it's like the last mile, essentially. In other words, it's like, okay, you have folks that are basically that are very comfortable with Tableau but you do have developers who don't want to have to shell out to a separate tool. And so this is where Snowflake is essentially working to address that constituency. To Sanjeev's point, and I think part of it, this kind of plays into it is what makes this different from the Hadoop era is the fact that all these capabilities, you know, a lot of vendors are taking it very seriously to put this native. Now, obviously Snowflake acquired Streamlit. So we can expect that the Streamlit capabilities are going to be native. >> I want to share a little bit about the higher level thinking at Snowflake, here's a chart from Frank Slootman's keynote. It's his version of the modern data stack, if you will. Now, Snowflake of course, was built on the public cloud. If there were no AWS, there would be no Snowflake. Now, they're all about bringing data and live data and expanding the types of data, including structured, we just heard about that, unstructured, geospatial, and the list is going to continue on and on. Eventually I think it's going to bleed into the edge if we can figure out what to do with that edge data. Executing on new workloads is a big deal. They started with data sharing and they recently added security and they've essentially created a PaaS layer. We call it a SuperPaaS layer, if you will, to attract application developers. Snowflake has a developer-focused event coming up in November and they've extended the marketplace with 1300 native apps listings. And at the top, that's the holy grail, monetization. We always talk about building data products and we saw a lot of that at this event, very, very impressive and unique. Now here's the thing. There's a lot of talk in the press, in the Wall Street and the broader community about consumption-based pricing and concerns over Snowflake's visibility and its forecast and how analytics may be discretionary. But if you're a company building apps in Snowflake and monetizing like Capital One intends to do, and you're now selling in the marketplace, that is not discretionary, unless of course your costs are greater than your revenue for that service, in which case is going to fail anyway. But the point is we're entering a new error where data apps and data products are beginning to be built and Snowflake is attempting to make the data cloud the defacto place as to where you're going to build them. In our view they're well ahead in that journey. Okay, let's talk about some of the bigger themes that we heard at the event. Bringing apps to the data instead of moving the data to the apps, this was a constant refrain and one that certainly makes sense from a physics point of view. But having a single source of data that is discoverable, sharable and governed with increasingly robust ecosystem options, it doesn't have to be moved. Sometimes it may have to be moved if you're going across regions, but that's unique and a differentiator for Snowflake in our view. I mean, I'm yet to see a data ecosystem that is as rich and growing as fast as the Snowflake ecosystem. Monetization, we talked about that, industry clouds, financial services, healthcare, retail, and media, all front and center at the event. My understanding is that Frank Slootman was a major force behind this shift, this development and go to market focus on verticals. It's really an attempt, and he talked about this in his keynote to align with the customer mission ultimately align with their objectives which not surprisingly, are increasingly monetizing with data as a differentiating ingredient. We heard a ton about data mesh, there were numerous presentations about the topic. And I'll say this, if you map the seven pillars Snowflake talks about, Benoit Dageville talked about this in his keynote, but if you map those into Zhamak Dehghani's data mesh framework and the four principles, they align better than most of the data mesh washing that I've seen. The seven pillars, all data, all workloads, global architecture, self-managed, programmable, marketplace and governance. Those are the seven pillars that he talked about in his keynote. All data, well, maybe with hybrid tables that becomes more of a reality. Global architecture means the data is globally distributed. It's not necessarily physically in one place. Self-managed is key. Self-service infrastructure is one of Zhamak's four principles. And then inherent governance. Zhamak talks about computational, what I'll call automated governance, built in. And with all the talk about monetization, that aligns with the second principle which is data as product. So while it's not a pure hit and to its credit, by the way, Snowflake doesn't use data mesh in its messaging anymore. But by the way, its customers do, several customers talked about it. Geico, JPMC, and a number of other customers and partners are using the term and using it pretty closely to the concepts put forth by Zhamak Dehghani. But back to the point, they essentially, Snowflake that is, is building a proprietary system that substantially addresses some, if not many of the goals of data mesh. Okay, back to the list, supercloud, that's our term. We saw lots of examples of clouds on top of clouds that are architected to spin multiple clouds, not just run on individual clouds as separate services. And this includes Snowflake's data cloud itself but a number of ecosystem partners that are headed in a very similar direction. Snowflake still talks about data sharing but now it uses the term collaboration in its high level messaging, which is I think smart. Data sharing is kind of a geeky term. And also this is an attempt by Snowflake to differentiate from everyone else that's saying, hey, we do data sharing too. And finally Snowflake doesn't say data marketplace anymore. It's now marketplace, accounting for its application market. Okay, let's take a quick look at the competitive landscape via this ETR X-Y graph. Vertical access remembers net score or spending momentum and the x-axis is penetration, pervasiveness in the data center. That's what ETR calls overlap. Snowflake continues to lead on the vertical axis. They guide it conservatively last quarter, remember, so I wouldn't be surprised if that lofty height, even though it's well down from its earlier levels but I wouldn't be surprised if it ticks down again a bit in the July survey, which will be in the field shortly. Databricks is a key competitor obviously at a strong spending momentum, as you can see. We didn't draw it here but we usually draw that 40% line or red line at 40%, anything above that is considered elevated. So you can see Databricks is quite elevated. But it doesn't have the market presence of Snowflake. It didn't get to IPO during the bubble and it doesn't have nearly as deep and capable go-to market machinery. Now, they're getting better and they're getting some attention in the market, nonetheless. But as a private company, you just naturally, more people are aware of Snowflake. Some analysts, Tony Bear in particular, believe Mongo and Snowflake are on a bit of a collision course long term. I actually can see his point. You know, I mean, they're both platforms, they're both about data. It's long ways off, but you can see them sort of in a similar path. They talk about kind of similar aspirations and visions even though they're quite in different markets today but they're definitely participating in similar tam. The cloud players are probably the biggest or definitely the biggest partners and probably the biggest competitors to Snowflake. And then there's always Oracle. Doesn't have the spending velocity of the others but it's got strong market presence. It owns a cloud and it knows a thing about data and it definitely is a go-to market machine. Okay, we're going to end on some of the things that we heard in the ecosystem. 'Cause look, we've heard before how particular technology, enterprise data warehouse, data hubs, MDM, data lakes, Hadoop, et cetera. We're going to solve all of our data problems and of course they didn't. And in fact, sometimes they create more problems that allow vendors to push more incremental technology to solve the problems that they created. Like tools and platforms to clean up the no schema on right nature of data lakes or data swamps. But here are some of the things that I heard firsthand from some customers and partners. First thing is, they said to me that they're having a hard time keeping up sometimes with the pace of Snowflake. It reminds me of AWS in 2014, 2015 timeframe. You remember that fire hose of announcements which causes increased complexity for customers and partners. I talked to several customers that said, well, yeah this is all well and good but I still need skilled people to understand all these tools that I'm integrated in the ecosystem, the catalogs, the machine learning observability. A number of customers said, I just can't use one governance tool, I need multiple governance tools and a lot of other technologies as well, and they're concerned that that's going to drive up their cost and their complexity. I heard other concerns from the ecosystem that it used to be sort of clear as to where they could add value you know, when Snowflake was just a better data warehouse. But to point number one, they're either concerned that they'll be left behind or they're concerned that they'll be subsumed. Look, I mean, just like we tell AWS customers and partners, you got to move fast, you got to keep innovating. If you don't, you're going to be left. Either if your customer you're going to be left behind your competitor, or if you're a partner, somebody else is going to get there or AWS is going to solve the problem for you. Okay, and there were a number of skeptical practitioners, really thoughtful and experienced data pros that suggested that they've seen this movie before. That's hence the same wine, new bottle. Well, this time around I certainly hope not given all the energy and investment that is going into this ecosystem. And the fact is Snowflake is unquestionably making it easier to put data to work. They built on AWS so you didn't have to worry about provisioning, compute and storage and networking and scaling. Snowflake is optimizing its platform to take advantage of things like Graviton so you don't have to, and they're doing some of their own optimization tools. The ecosystem is building optimization tools so that's all good. And firm belief is the less expensive it is, the more data will get brought into the data cloud. And they're building a data platform on which their ecosystem can build and run data applications, aka data products without having to worry about all the hard work that needs to get done to make data discoverable, shareable, and governed. And unlike the last 10 years, you don't have to be a keeper and integrate all the animals in the Hadoop zoo. Okay, that's it for today, thanks for watching. Thanks to my colleague, Stephanie Chan who helps research "Breaking Analysis" topics. Sometimes Alex Myerson is on production and manages the podcasts. Kristin Martin and Cheryl Knight help get the word out on social and in our newsletters, and Rob Hof is our editor in chief over at Silicon, and Hailey does some wonderful editing, thanks to all. Remember, all these episodes are available as podcasts wherever you listen. All you got to do is search Breaking Analysis Podcasts. I publish each week on wikibon.com and siliconangle.com and you can email me at David.Vellante@siliconangle.com or DM me @DVellante. If you got something interesting, I'll respond. If you don't, I'm sorry I won't. Or comment on my LinkedIn post. Please check out etr.ai for the best survey data in the enterprise tech business. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching, and we'll see you next time. (upbeat music)

>>Fly from San Francisco. It's the cube covering RSA conference, 2020 San Francisco brought to you by Silicon angle media. >>Hi everyone. Welcome back to the cubes coverage here at in San Francisco, the Moscone center for RSA conference 2020 I'm job for your host. We are the very special guests, the COO of VMware, Sanjay Poonen, cube alumni. When you talk about security, talk about the modern enterprise as it transforms new use cases, new problems emerge. New opportunities exist here to break it down. Sanjay, welcome back. Thank you John. Always a pleasure to be on your show and I think it's my first time at RSA. We've talked a number of times, but nice to see you here. Well, it's a security guard. Well, this is really why I wanted you to talk, talk to you because operations is become now the big conversation around security. So you know, security was once part of it. It comes out and part of the board conversation, but when you look at security, all the conversations that we're seeing that are the most important conversations are almost a business model conversation. >>Almost like if you're the CEO of the company, you've got HR people, HR, organizational behavior, collaboration, technology, stack compliance and risk management. So the threat of cyber has to cut across now multiple operational functions of the business. It's no longer one thing, it's everything. So this is really kind of makes it the pressure of the business owners to be mindful of a bigger picture. And the attack velocity is happening so much faster, more volume of attacks, milliseconds and nanosecond attacks. So this is a huge, huge problem. I need you to break it down for me. >> Good. But then wonderful intro. No, I would say you're absolutely right. First off, security is a boardroom topic. Uh, audit committees are asking, you know, the CIO so often, you know, reports a report directly, sometimes, often not even to the CIO, to the head of legal or finance and often to the audit. >>So it's a boardroom topic then. You're right, every department right now cares about security because they've got both threat and security of nation state, all malicious, organized crime trying to come at them. But they've also got physical security mind. I mean, listen, growing a virus is a serious threat to our physical security. And we're really concerned about employees and the idea of a cyber security and physical security. We've put at VMware, cybersecurity and, and um, um, physical security. One guy, the CIO. So he actually runs vote. So I think you're absolutely right and if you're a head of HR, you care about your employees. If you're care ahead of communications, you care about your reputation and marketing the same way. If you're a finance, you care about your accounting systems and having all of the it systems that are. So we certainly think that holistic approach does, deserves a different approach to security, which is it can't be silo, silo, silo. >>It has to be intrinsic. And I've talked on your show about why intrinsic and how differentiated that intrinsic security, what I talked about this morning in my keynote. >> Well, and then again, the connect the dots there. It's not just security, it's the applications that are being built on mobile. For instance, I've got a mobile app. I have milliseconds, serious bond to whether something's yes or no. That's the app on mobile. But still the security threat is still over here and I've got the app over here. This is now the reality. And again, AirWatch was a big acquisition that you did. I also had some security. Carbon black was a $2 billion acquisition that VMware made. That's a security practice. How's it all coming together? Can you think of any questions? Blame the VMware because it's not just security, it's what's around it. >> Yeah. I think we began to see over the course of the last several years that there were certain control points and security that could help, you know, bring order to this chaos of 5,000 security vendors. >>They're all legitimate. They're all here at the show. They're good vendors. But you cannot, if you are trying to say healthy, go to a doctor and expect the doctor to tell you, eat 5,000 tablets and sailed. He just is not sustainable. It has to be baked into your diet. You eat your proteins, your vegetables, your fruit, your drink, your water. The same way we believe security needs to become intrinsically deeper parts, the platform. So what were the key platforms and control points? We decided to focus on the network, the endpoint, and you could think of endpoint as to both client and workload identity, cloud analytics. You take a few of those and network. We've been laboring the last seven years to build a definitive networking company and now a networking security company where we can do everything from data center networking, Dell firewalls to load balancing to SDN in this NSX platform. >>You remember where you bought an nice syrup. The industry woke up like what's VM ever doing in networking? We've now built on that 13,000 customers really good growing revenue business in networking and and now doing that working security. That space is fragmented across Cisco, Palo Alto, FIU, NetScaler, checkpoint Riverbed, VMware cleans that up. You get to the end point side. We saw the same thing. You know you had an endpoint management now workspace one the sequel of what AirWatch was, but endpoint security again, fragmented. You had Symantec McAfee, now CrowdStrike, tenable Qualis, you know, I mean just so many fragmented IOM. We felt like we could come in now and clean that up too, so I have to worry about to do >> well basically explaining that, but I want to get now to the next conversation point that I'm interested in operational impact because when you have all these things to operationalize, you saw that with dev ops and cloud now hybrid, you got to operationalize this stuff. >>You guys have been in the operations side of the business for our VMware. That's what you're known for and the developers and now on the horizon I gotta operationalize all the security. What do I do? I'm the CSO. I think it's really important that in understanding operations of the infrastructure, we have that control point called vSphere and we're now going to take carbon black and make it agentless on the silverside workloads, which has never been done before. That's operationalizing it at the infrastructure level. At the end point we're going to unify carbon black and workspace one into a unified agent, never been done before. That's operationalizing it on the client side. And then on the container and the dev ops site, you're going to start bringing security into the container world. We actually happened in our grade point of view in containers. You've seen us do stuff with Tansu and Kubernetes and pivotal. >>Bringing that together and data security is a very logical thing that we will add there. So we have a very good view of where the infrastructure and operations parts that we know well, a vSphere, NSX workspace one containers with 10 Xu, we're going to bring security to all of them and then bake it more and more in so it's not feeling like it's a point tool. The same platform, carbon black will be able to handle the security of all of those use cases. One platform, several use cases. Are you happy with the carbon black acquisition? Listen, you know, you stay humble and hungry. Uh, John for a fundamental reason, I've been involved with number of acquisitions from my SAP VMware days, billion dollar plus. We've done talking to us. The Harvard business review had an article several years ago, which Carney called acquisitions and majority of them fail and they feel not because of process of product they feel because good people leave. >>One of the things that we have as a recipe does acquisition. We applied that to AirWatch, we apply the deny Sera. There is usually some brain trust. You remember in the days of nice area, it was my team Cosato and the case of AirWatch. It was John Marshall and that team. We want to preserve that team to help incubate this and then what breve EV brings a scale, so I'm delighted about Patrick earlier. I want to have him on your show next time because he's now the head of our security business unit. He's culturally a fit for the mr. humble, hungry. He wants to see just, we were billion dollar business now with security across networking endpoint and then he wants to take just he's piece of it, right? The common black piece of it, make it a billion dollar business while the overall security business goes from three to five. >>And I think we're going to count them for many years to come to really be a key part of VMware's fabric, a great leader. So we're successful. If he's successful, what's my job then? He reports to me is to get all the obstacles out of the way. Get every one of my core reps to sell carbon black. Every one of the partners like Dell to sell carbon black. So one of the deals we did within a month is Dell has now announced that their preferred solution on at Dell laptops, this carbon bike, they will work in the past with silence and crowd CrowdStrike. Now it's common black every day laptop now as a default option. That's called blank. So as we do these, John, the way we roll is one on here to basically come in and occupy that acquisition, get the obstacles out of the way, and that let Patrick scaled us the same way. >>Martine Casado or jumbo. So we have a playbook. We're gonna apply that playbook. Stay humble and hungry. And you ask me that question every year. How are we doing a carbon black? I will be saying, I love you putting a check on you. It will be checking in when we've done an AirWatch. What do you think? Pretty good. Very good. I think good. Stayed line to the radar. Kept growing. It's top right. Known every magic quadrant. That business is significant. Bigger than the 100 million while nice here. How do we do a nice hero? NSX? It's evolved quite a bit. It's evolved. So this is back to the point. VMware makes bets. So unlike other acquisitions where they're big numbers, still big numbers, billions or billions, but they're bets. AirWatch was a good bet. Turned out okay. That the betting, you're being conservative today anyway. That's it. You're making now. >>How would you classify those bets? What are the big bets that you're making right now? Listen, >> I think there's, um, a handful of them. I like to think of things as no more than three to five. We're making a big bet. A multi-cloud. Okay. The world is going to be private, public edge. You and us have talked a lot about VMware. AWS expanded now to Azure and others. We've a big future that private cloud, public cloud edge number two, we're making a big bet on AB motorization with the container level 10 zoos. I think number three, we're making a big bet in virtual cloud networking cause we think longterm there's going to be only two networking companies in matter, VMware and Cisco. Number four, we're making a big bet in the digital workspace and build on what we've done with AirWatch and other technologies. Number five, and make it a big bet security. >>So these five we think of what can take the company from 10 to 20 billion. So we, you know, uh, we, we've talked about the $10 billion Mark. Um, and the next big milestone for the company is a 20 billion ball Mark. And you have to ask yourself, can you see this company with these five bets going from where they are about a 10 billion revenue company to 20. Boom. We hope again, >> Dave, a lot that's doing a braking and now he might've already shipped the piece this morning on multi-cloud. Um, he and I were commenting that, well, I said it's the third wave of cloud computing, public cloud, hybrid multi-cloud and hybrids, the first step towards multi-cloud. Everyone kind of knows that. Um, but I want to ask you, because I told Dave and we kind of talked about this is a multi-decade growth opportunity, wealth creation, innovation, growth, new opportunity multicloud for the generation. >>Take the, this industry the next level. How do you see that multicloud wave? Do you agree on the multigenerational and if so, what specifically do you see that unfolding into this? And I'm deeply inspired by what Andy Jassy, Satya Nadella, you know, the past leading up to Thomas Korea and these folks are creating big cloud businesses. Amazon's the biggest, uh, in the iOS pass world. Azure is second, Google is third, and just market shares. These folks collectively are growing, growing really well. In some senses, VM-ware gets to feed off that ecosystem in the public cloud. So we are firm believers in what you're described. Hybrid cloud is the pot to the multicloud. We coined that term hybrid thought. In fact, the first incantation of eco there was called via cloud hybrid service. So we coined the term hybrid cloud, but the world is not multi-cloud. The the, the key though is that I don't think you're gonna walk away from those three clouds I mentioned have deep pockets. >>Then none of them are going away and they're going to compete hard with each other. The market shares may stay the same. Our odd goal is to be a Switzerland player that can help our customers take VM or workloads, optimize them in the private cloud first. Okay? When a bank of America says on their earnings caller, Brian Warren and said, I can run a private cloud better than a public cloud and I can save 2 billion doing that, okay? It turns off any of the banks are actually running on VMware. That's their goal. But there are other companies like Freddie Mac, we're going all in with Amazon. We want to ride the best of both worlds. If you're a private cloud, we're going to make you the most efficient private cloud, VMware software, well public cloud, and going to Amazon like a Freddie Mac will help you ride your apps into that through VMware. >>So sometimes history can be a predictor of future behavior. And just to kind of rewind the computer industry clock, if you looked at mainframe mini-computers, inter networking, internet proprietary network operating systems dominated it, but you saw the shift and it was driven by choice for customers, multiple vendors, interoperability. So to me, I think cloud multicloud is going to come down to the best choice for the workload and then the environment of the business. And that's going to be a spectrum. But the key in that is multi-vendor, multi, a friend choice, multi-vendor, interoperability. This is going to be the next equation in the modern error. It's not gonna look the same as mainframe mini's networking, but it'll create the next Cisco, the create the next new brand that may or may not be out there yet that might be competing with you or you might be that next brand. >>So interoperability, multi-vendor choice has been a theme in open systems for a long time. Your reactions, I think it's absolutely right, John, you're onto something there. Listen, the multicloud world is almost a replay of the multi hardware system world. 20 years ago, if you asked who was a multi hardware player before, it was Dell, HP at the time, IBM, now, Lenovo, EMC, NetApp, so and so forth and Silva storage, networking. The multicloud world today is Amazon, Azure, Google. If you go to China, Alibaba, so on and so forth. A Motiva somebody has to be a Switzerland player that can serve the old hardware economy and the new hardware economy, which is the, which is the cloud and then of course, don't forget the device economy of Apple, Google, Microsoft, there too. I think that if you have some fundamental first principles, you expressed one of them. >>Listen where open source exists, embrace it. That's why we're going big on Kubernetes. If there are multiple clouds, embrace it. Do what's right for the customer, abstract away. That's what virtualization is. Managed common infrastructure across Ahmed, which is what our management principles are, secure things. At the point of every device and every workload. So those are the principles. Now the engineering of it changes. The way in which we're doing virtualization today in 2020 is slightly different from when Diane started the company and around the year 2020 years ago. But the principals are saying, we're just not working just with the hardware vendors working toward the cloud vendors. So using choices where it's at, the choice is what they want. Absolutely, absolutely. And you're right. It's choice because it was the big workloads. We see, for example, Amazon having a headstart in the public cloud markets, but there's some use cases where Azure is applicable. >>Some use his word, Google's applicable, and to us, if the entire world was only one hardware player or only one cloud player, only one device player, you don't need VMware. We thrive in heterogeneity. It's awesome. I love that word. No heterogeneity provides not 3000 vendors. There's almost three, three of every kind, three silver vendors, three storage vendors, three networking vendors, three cloud vendors, three device vendors. We was the middle of all of it. And yeah, there may be other companies who tried to do that too. If they are, we should learn from them, do it better than them. And competition even to us is a good thing. All right. My final question for you is in the, yeah, the Dell technologies family of which VMware is a part of, although big part of it, the crown jewel as we've been calling them the cube, they announced RSA is being sold to a private equity company. >>What's the general reaction amongst VMware folks and the, and the Dell technology family? Good move, no impact. What we support Dell and you know, all the moves that they've made. Um, and from our perspective, you know, if we're not owning it, we're going to partner it. So I see no overlap with RSA. We partner with them. They've got three core pillars, secure ID, net witness and Archer. We partnered with them very well. We have no aspirations to get into those aspects of governance. Risk and compliance or security has been, so it's a partner. So whoever's running it, Rohit runs on very well. He also owns the events conference. We have a great relationship and then we'll keep doing that. Well, we are focused in the areas I described, network, endpoint security. And I think what Michael has done brilliantly through the course of the last few years is set up a hardware and systems company in Dell and allow the software company called Vima to continue to operate. >>And I think, you know, the movement of some of these assets between the companies like pivotal to us and so on and so forth, cleans it up so that now you've got both these companies doing well. Dell has gone public, we Hammer's gone public and he has said on the record, what's good for Dell is good, what's good for VMware and vice versa and good for the customer. And I think the key is there's no visibility on what cloud native looks like. Hybrid, public, multi, multi, not so much. But you get almost, it's an easy bridge to get across and get there. AI, cyber are all big clear trends. They're waves. Sasha. Great. Thank you. Thanks for coming on. Um, your thoughts on the security show here. Uh, what's your, what's your take to, uh, definitive security shows? I hope it stays that way. Even with the change of where RSA is. >>Ownership goes is this conference in black hat and we play in both, uh, Amazon's conference. I was totally starting to, uh, reinforce, reinforce cloud security will show up there too. Uh, but we, we think, listen, there's what, 30,000 people here. So it's a force. It's a little bit like VMworld. We will play here. We'll play a big, we've got, you know, it just so happens because the acquisition happened before we told them, but we have two big presences here. We were at carbon black, um, and it's an important business for us. And I said, like I said, we have $1 billion business and security today by 30,000 customers using us in a security network, endpoints cloud. I want to take that to be a multi, multiple times that size. And I think there's a pot to do that because it's an adjacent us and security. So we have our own kind of selfish motives here in terms of getting more Mindshare and security. >>We did a keynote this morning, which was well received with Southwest airlines. She did a great job. Carrie Miller, she was a fantastic speaker and it was our way of showing in 20 minutes, not just to our point of view, because you don't want to be self serving a practitioner's point of view. And that's what's really important. Well finally on a personal note, um, you know, I always use the term tech athlete, which I think you are one, you really work hard and smart, but I got to get your thoughts. But then I saw you're not on Twitter. I'm on. When IBM announced a new CEO, Arvin, um, fishnet Indian American, another CEO, this is a pattern. We're starting to see Indian American CEOs running cup American companies because this is the leadership and it's really a great thing in my mind, I think is one of the most successful stories of meritocracy of all time. >>You're quick. I'm a big fan of oven, big fan of Shantanu, Sundar Pichai, something that Ellen, many of them are close friends of mine. Uh, many of them have grown up in Southern India. We're a different ages. Some of them are older than me and in many cases, you know, we were falling behind other great players like Vino Cosla who came even 10 to 15 years prior. And you know, it's hard for an immigrant in this country. You know, um, when I first got here and I came as an immigrant to Dartmouth college, there may have been five or 10 Brown skin people in the town of Hanover, New Hampshire. I don't know if you've been to New Hampshire. I've been there, there's not many at that time. And then the late 1980s, now of course, there's much more, uh, so, you know, uh, we stay humble and hungry. >>There's a part of our culture in India that's really valued education and hard work and people like Arvin and some of these other people are products. I look up to them, the things I learned from them. And um, you know, it's true of India. It's a really good thing to see these people be successful at name brand American companies, whether it's IBM or Microsoft or Google or Adobe or MasterCard. So we're, we're, I'm in that fan club and there's a lot I learned from that. I just love being around people who love entrepreneurship, love innovation, love technology, and work hard. So congratulations. Thank you so much for your success. Great to see you again soon as you put in the COO of VM-ware here on the ground floor here at RSA conference at Moscone, sharing his insight into the security practice that is now carbon black and VMware. All the good things that are going on there. Thanks for watching.

(upbeat music) >> Hey, welcome back everybody. Jeff Frick with The Cube. If you can't tell over my shoulder, we are at Oracle Park. It's a glorious day. The marine layer is burning off and it is really spectacular. We're happy to be here. Haven't been here since, I think 2014. It's an interesting event called Sports Tech Tokyo World Demo Day. About 25 technology companies in the sports area are giving demos all day today. It's a huge program, and we're excited to have our next guest coming from the analytics side. She's Sezin Aksoy, Global Data Strategy and Analytics for AXS. >> Correct. >> Welcome. >> Thank you. >> Absolutely. >> Glad to be here. >> So Global Data Strategy. Everything's all about data. >> Correct. >> So, somebody's really happy to have you on board. What are so... What do you, what are you working on, what was top of line. >> Sure, so it's going to sound cheesy but data is the power of the world. >> Yes. >> It's going to empower people making better decisions, so that's kind of my role is at AXS. So AXS is the ticketing platform for live entertainment events. We operate in the US, Europe, as well as in Japan. And, if you think about it, when a consumer comes to your website, that's the first touchpoint that you have. Whether they buy the ticket or don't. Whether they buy or sell, and transfer the ticket, or they attend the event, all those are various touchpoints that we are collecting. So that we can inform our clients to make better decisions with data. >> Right. >> Whether it's pricing decisions, or marketing decisions, or scanning an event, which gates will be more busier than others. So, that's kind of what my team works on. >> Excellent. So, let's jump into a little bit on the dynamic pricing. >> Sizen: Hm mm. >> Because we saw, we've seen dynamic pricing. And you said you were in the airline industry. >> Correct. >> We've seen it in the hotel industry. >> Yup. >> My father in law talks about when he was doing dynamic pricing as a young kid. >> Sizen: Okay. Just making a call when somebody came through the door, at eleven o'clock. >> Sizen: Yeah. (laughs) >> Jeffrey: What's my marginal cost... >> Okay, yep. >> Jeffrey: with somebody in that room or not. There's really slow to get beyond, kind of the entertain, oh excuse me, the travel industry for other people... >> Hm mm. Yep. >> To kind of get on board the dynamic pricing. >> Yeah. We saw the Giants here... >> Yep. >> Actually a couple of years ago. We came by, they were starting to do dynamic pricing. >> Sizen: Hm mm. >> A Friday night Dodger game, compared to a Tuesday day... >> Sizen: Yep. >> Milwaukee game, very, very different. >> Sizen: Hm mm. >> So, what are some of the factors going in, what are some of the resistance, >> Sizen: Yeah. >> that had to be overcome for people to actually accept that it's okay to charge more for a Friday night Dodger game, than a Tuesday afternoon Milwaukee game. >> Yep, so yeah, so my background start with the airlines, which is where dynamic pricing, revenue management started at, specifically the American Airlines. If you think about there are a lot of similarities between airlines and live entertainments. Fixed costs, you have to, flight has to go, or the game has to be played no matter how many people are there. So, you really have a limited time to really maximize your revenue. And you kind of have a product that the demand level is different by day, whether it's a Tuesday game or Friday game. It really something you have to study the sort of the behavior from the consumers when they buy their tickets. What are the factors they put into play to make that decision? And in that mix, San Francisco Giants was one of the first teams that actually incorporated dynamic pricing about ten years ago, that slowly. The challenges with it is we are not as the consumer, not as trained to know that the price may change. Hotels, airlines been doing it for years and years. >> Right. >> And for them, also it didn't start from like doing all the flights in day one. So it's really needs to be a phased approach. It needs to be a lot of education for the public, and to think about the right way to think about it is, you want incentivize people to buy early. And you want to make sure they are the ones that getting the best price, and not necessarily the people that are buying last minute. >> Right. >> If you're buying last minute, then you must accept that it maybe the available today you're not looking for or the price not you looking for. But I will say though that plans change, people decide to not attend the game. The reason is that, potential for finding other seats for that similar game. But, really for you, have your plans. It's better to buy early, and that's kind of what the industries needs to be trained on, more and more. >> Right. >> Was there more opportunity in getting additional value out of that high demand game? Or was the bigger opportunity in getting, kind of lowering the prices on the less desirable games, and getting kind of marginal revenue on that side. Where was the easy money made, >> Yeah. >> Jeffrey: On dynamic pricing? I mean the immediate impact is from the high value seats for the high value games, cause that's really is your premium product at that point. But in the meantime, there's always a low number of seats that you have in your premium area. And if you find the right price, and if you start earlier. And really the goal is to sell all the seats, and to fill all the seats. >> Right. >> Also, just selling the seats is not, doesn't get you far enough. You want to make sure people actually come to the game, and they're the people that are going to attend the game. Right? >> Right. >> So, if you kind of, the lower level has many more seats, so it's really has to be both ways. It can't be in one area, either dynamic pricing and you don't do it. It's just all about training the public and consumers. >> Right. Now, the other interesting you said in your kind of intro, was keeping track of... What are the busiest turnstiles? And where people coming? And the flow within the game. >> Sizen: Yep. >> What are some of the analytics that you do there, >> Sizen: Yep. >> And how are teams using those... >> Sizen: Yep. >> that information to provide a better fan experience? >> Yeah, so we have scanned data, and we actually have it real time. So, we are able to provide the teams. We have kineses streams, not to go too technical, to kind of empower them to do their game operations in a certain way. So example would be, you could study the past games and understand where people came from. Typically for a Friday game verse a Tuesday game, your crowd will look different, right. The Friday game, maybe the more the families or Saturday or Sunday. But Tuesday may be more corporate world, right. So understanding they're patterns, but also than having that data accessible to you to real time. So, that way you're able to see how many people are coming in from this one gate to other. You can man the gates differently that way. And the real time data is not something that comes just easily. There's a lot of infrastructure built for it. >> Right. >> But we've done it at AXS, and we've been able to provide to the teams so they can manage their getting in better. >> Right. >> So real time's interesting cause you know a lot of these conversations about real time, and I would say, "How do you define real time?" And in my mind, it's in time to do something about it. >> Exactly. >> So, using real time, I mean are there things they can do in real time to either lighten the load at an overdone gate, or... >> Sizen: Yeah. >> What are some of the real time impacts that people are using this data to do? >> Yeah, so exactly the example you provided. Like making sure there are more people at this one gate as opposed to others. But also, like knowing who's coming into the arena. So AXS's I-D ticketing, I-D based ticketing platform, so we actually know who's coming in. It's a rotating barcode, so if you just copy-paste the ticket, and text your friend. That doesn't work, that eliminates fraud as well. But because we know who's coming in, you can actually empower your sales reps as a team to make sure you are, you know, if they are coming to a suite or a premium area. So in so actually just scanned in, so you kind of come up with ideas for sales reps. As well as some of the marketing activations, like... It could be that you have people that typically come in late. You want to incentivize them. You could actually come up with promotions on merch and food and beverage to incentivize them early, right? Or at the same time you can actually, there are some platforms that do marketing activation. You may have had a lot of hotdogs left that you couldn't sell. Towards the late quarter, you could send a message to everyone saying, "Okay, ya know, hot dogs are 20 percent off." >> Right, right. >> So that, you need real time for it, for data for that. Cause you again need to know how many people scanned in. You may want to know how many people scanned out. So for some conferences and other type events, you want to make sure there's a Fire Marshall rules, so you want to make sure. So all the real time data is helpful for that if you just look at the purchaser data, you're not going to get that specifically there. >> That's really interesting cause I was going to say, What are some of the next things that we can expect to see dynamic pricing applied to, and you just went through them which are really situational specific. >> Yep. >> Opportunities to clear inventory, to do whatever. >> Exactly, it's not just a ticket purchase. It could be applied to other things as well. >> Right, Right. >> Yeah. >> How cool. So what other kind of data sets are you looking at to help teams that maybe we're not thinking about. >> Sure, just when people buy their tickets. What marketing may have they done, so that we can understand the web traffic, and did they buy the ticket when you send out that email. Or did they buy it three days later. So that's one area. As well as sort of, the inventory that you have available for that game. Does it sell faster for that Friday game versus a Tuesday game? We also, we're a comprehensive marketplace where we have both primary and secondary in the same map. To give the convenience back to the consumers, so you kind of have a chance to see all the inventory available in front of you. So, a bit of understanding how tickets transact in the secondary marketplace is helpful for the teams to really price their product better. Cause sometimes we have... I work for a team, so I have that background where you may have just 20 price points, and you've done it for 20 years but it's been certainly changing then. But now that you have all these different data points on the second, you also you kind of maybe is like, 'Okay I need 40 price points really because there's that much differentiation demand. >> Wow, really sophisticated analysis... >> Yeah, it's a passion area for me, so... >> And doing the real time, real time data flow and everything. >> Yeah, yeah. A really interesting, interesting conversation. >> Yeah. >> To go so far beyond just dynamic pricing. >> Exactly. >> It uses more sophisticated methods to get more value, provide better experience for the fans. >> And actually in Japan, they do more about dynamic pricing. So they utilize our platform to actually able to price every seat differently if they wanted to. We've just went out with on sales for Big League teams, and that's how they apply that. So it's been used elsewhere, maybe in the U-S in sports. It's definitely catching up, and it's much much big difference from the 10 years ago. But, I think Japan has already been kind of doing that. >> Excellent. >> Mm hm. >> Well Sizen, thanks for taking a few minutes, and sharing those stories. There's a lot going on behind the scenes that may not be conscious of, but hopefully we're getting the benefit of. >> Yeah, thank you. >> All right. Sizen, and I'm Jeff. Yes, we're live. They're banging on something down there. I'm not sure what, but keep watching. We'lls be here at Oracle Park in San Francisco. Thanks for watching, and see ya next time. (upbeat music)

>> live from Boston, Massachusetts. It's the Cube covering A W s reinforce 2019 brought to you by Amazon Web service is and its ecosystem partners. >> Hey, welcome back, everyone to Cubes. Live coverage here in Boston, Massachusetts for eight of us reinforced Amazon Web service is inaugural event around Cloud Security. I'm Jeffrey Day Volante. Two days of coverage. We're winding down Day two. We're excited to have a year in The Cube Special guest, part of Big and that one of the big announcements. Well, I think it's big. Nerdy Announcement is the automated reasoning. Byron Cook, director of the Automated Reasoning Group within AWS. Again, this is part of the team that's gonna help figure out security use automation to augment humans. Great to have you on big part of show here. Thanks very much to explain the automated reasoning group. Verner Vogel had a great block post on All things distributed applies formal verification techniques in an innovative way to cloud security and compliance for our customers. For our own there's developers. What does that mean? Your math? >> Yeah, let me try. I'll give you one explanation, and if I puzzle, you all try to explain a different way. 300 So do you know the Pythagorean Theorem? Yeah, sure, Yeah. So? So that the path I agree in theory is about all triangles that was proved in approximately B. C. It's the proof is a finite description in logic as to why it's true and holds for all possible triangles. So we're basically using This same approach is to prove properties of policies of networks of programs, for example, crypto virtualization, the storage, et cetera. So we write software. This finds proofs in mathematics and this the proofs are the same as what you could found for thuggery and should apply into >> solve problems that become these mundane tasks of checking config files, making sure things are that worries kind of that's I'll give you an example. So so that's two in which is the T. L s implementation used, for example, in history. But the large majority >> of AWS has approximately 12,000 state holding elements, so that with if you include the stack of the heat usage, so the number >> of possible >> states it could reach us to to the 12,000. And if you wanted to show that the T. L s handshake Implementation is correct or the H Mac implementation is correct. Deterministic random bit generator implementation is correct, which is what we do using conventional methods like trying to run tests on it. So you would need, if you have, like, 1,000,000 has, well, microprocessors and you would need many more lifetimes in the sun is gonna admit light at 3.4 $4,000,000,000 a year to test to exhaustively test the system. So what we do is we rather than just running a bunch of inputs on the code, we we represent that as the mathematical system and then we use proof techniques, auto automatically search for a proof and with our tools, we in about 10 minutes or able to prove all those properties of s two in the way of your intimidates. And then we apply that to pieces of s three pieces of easy to virtual ization infrastructure on. Then, uh, what we've done is we've realized that customers had a lot of questions about their networks and their policies. So, for example, they have a complicated network worldwide different different availability zones, different regions on. They want to ask. Hey, does there exist away for this machine to connect to this other machine. Oh, are you know, to do all this all SS H traffic coming in that eventually gets to my Web server, go through a bastion host, which is the best, best practice. And then we can answer that question again, using logic. So we take the representation that semantics of easy to networking the policy, the network from the customer, and then the question we're asking, expressing logic. And we throw a big through their call ifthere improver, get the answer back. And then same for policy. >> So you're analyzing policies, >> policies, networks, programs, >> networks, connections. Yeah, right. And it to the tooling is sell cova. Eso >> eso eso basically way come with We come with an approach and then we have many tools that implement the approach on different, different problems. That's how you apply Volkova all underneath. It's all uses of a kind of tool called SMT inside. So there's a south's over, uh, proves theorems about formula and proposition. A logic and SMT is sat modular theories. Those tools can prove properties of problems expressed in first order logic. And so what we do is we take the, for example, if you have a question about your policies answering, answering semantic level questions about policies is actually a piece space problem. So that's harder than NP complete. We express the question in logic and then call the silvery and they get their answer back on Marshall it back. And that's what Volkova does. So that's calling a tool called CVC four, which is which is an open source. Prove er and we wenzel Koval. We take the policy three question encoded to logic. Call a Silver and Marshall answer back. >> What's the What's the root of this? I mean, presumably there's some academic research that was done. You guys were applying it for your specific use case, But can you share with this kind of He's the origination of this. >> So the first Impey complete problem was discovered by a cook and not not me. Another cook the early seventies on. So he proved that the proposition a ll satisfy ability problem is impeccably and meanwhile, there's been a lot of research from the sixties. So Davis and Putnam, for example, I think a paper from the mid sixties where they were, we're trying to answer the question of can we efficiently solved this NP complete problem proposition will satisfy ability on that. Researchers continue. There have been a bunch of breakthroughs, and so now we're really starting to see very from. There's a big breakthrough in 2001 on, then some and then some further breakthroughs in the 5 4008 range. So what we're seeing is that the solvers air getting better and better. So there's an international competition of Let's Save, usually about 30 silvers. And there's a study recently where they took all of the winners from this competition each year 2001 5 4008 30 2002 to 2011 and compared them on the same bench marks and hardware, and the 2002 silver is able to solve 1/4 of the benchmarks in the 2011 solved practically all of them and then the the 2019 silvers, or even better. Nowadays they can take problems and logic that have many tens of millions of variables and solve them very efficiently. So we're really using the power of those underlying solvers and marshaling the questions to those to those overs, codifying thinking math. And that's the math. The hour is you gave a talk in one sessions around provable security. Kind of the title proves provable. >> What's what is that? What is that? Intel. Can you just explain that concept and sure, in the top surfaces. So, uh, uh, >> so mathematical logic. You know, it's 2000 years old, right? So and has refined Sobule, for example, made logic less of a philosophical thing and more of a mathematical thing. Uh, and and then automated reasoning was sort of developed in the sixties, where you take algorithms and apply algorithms to find proofs and mathematical logic. And then provable security is the application of automated reasoning to questions and security and compliance. So we you wanna prove absence of memory, corruption errors and C code You won't approve termination of of event handling routines that are supposed to handle security events. All of those questions, their properties of your program. And you can use these tools to automatically or uh oh, our find proofs and then check The proofs have been found manually. That's what that's >> where approvable security fix. What was the makeup of the attendee list where people dropping this where people excited was all bunch of math geeks. You have a cross section of great security people here, and they're deep dive conversations Not like reinvent this show. This is really deep security. What was some of the feedback and makeup of the attendees? >> Give you two answers because I actually gave to talks. And the and the answers are a little bit different because the subject of the talk So there was one unprovable security, which was a basically the foundation of logic And how we how Cheers since Volkova and our program, because we also prove correctness of crypto and so on. So those tools and so that was largely a, uh uh, folks who had heard about it. And we're wanting to know more, and we're and we're going to know how we're using it and trying to learn there was a second talk, which was about the application of it to compliance. So that was with Tomic, Andrew, who is the CEO of Coal Fire, one of the third party auditors that AWS uses in a lot of customers used and also Chad Wolf, who's vice president of security, focused on compliance. And so the three of us spoke about how we're using it internally within eight of us to automate, >> uh, >> certification compliance, sort of a commission on. So that crowd was really interesting mixture of people interested in automated reasoning and people interested in compliance, which are two communities you wouldn't think normally hang together. But that's sort of like chocolate and peanut butter. It turns out to be a really great application, >> and they need to work together to, because it is the world. The action is they don't get stuck in the compliance and auditing fools engineering teams emerging with old school compliance nerds. So there's a really interesting, uh, sort of dynamic to proof that has a like the perfect use casing compliance. So the problem of like proving termination of programs is undecided ble proving problems and proposition a logic is np complete as all that sounds very hard, difficult and you use dearest six to solve this problem. But the thing is that once you've found a proof replaying, the proof is linear and size of the proof, so actually you could do extremely efficiently, and that has application and compliance. So one could imagine that you have, for example, PC I hip fed ramp. You have certain controls that you want to prove that the property like, for example, within a W s. We have a control that all data dressed must be encrypted. So we are using program verification tools, too. Show that of the code base. But now, once we've run that tool that constructs a proof like Euclid founded the sectarian serum that you can package up in a file hand to an auditor. And then a very simple, easy to understand third party open source tool could replay that proof. And so that becomes audit evidence. It's a scale of total examples >> wth e engineering problem. You're solving a security at scale. The business problem. You're solving it. Yeah. His customers are struggling. Just implementing There just >> aren't enough security professionals to hire right? So the old day is, the talk explains. It's out there all on YouTube's. The people watching the show can go check it out. But I am by the way I should I should make a plug for if you Google a W s provable security. There's a Web page on eight of us that has papers and videos and lots of information, so you might wanna check that out. I can't remember what I was answering now, but >> it's got links to the academic as >> well. Oh, yes. Oh, yes. That was the point that Tommy Kendra is pointing out, as in the old days, you would do an audit would come in to be a couple minutes box that we win this box. You check a few things to be a little network. Great. But now you have machines across the world, extremely complex networks, interaction between policies, networks, crypto, etcetera. And so there's There's no way a human or even a team of human could come in and have any reasonable chance of actually deeply understanding the system. So they just sort of check some stuff and then they call it success. And these tools really allow you to actually understand the entire system buyer and you guys doing some cutting edge work, >> folks watching and want to know how math translates into the real world with all your high school kids out their parents. This is stuff you learn in school like you could be played great work. I think I think this is cutting edge. I think math and the confidence of math intersects with groups. The compliance example audited example shows that world's gonna come together with math. I think this is a big mega trend. It's gonna not eliminate the human element. It's going augment that so great stuff, its final question just randomly. And while you're here, since your math guru we're always interested, we always covering our favorite topic of Blockchain, huh? We believe that a security conference is gonna soon have a Blockchain component because because of the mutability of it, there's a lot of math behind it. So as that starts to mature certainly Facebook entering him at their own currency. Whole nother conversation you don't want to have here is bring a lot of attention. So we see the intersection of security being a supply chain problem in the future. Your thoughts on that just generally. So So the problem of proving programs is undecided, and that means that you can't build a general solution. What you're gonna have to do is look >> for niche areas like device drivers, networks, policies, AP, I used to dream crypto et cetera, and then make the tools work for that area, and you will have to be comfortable with the idea that occasionally the tools aren't gonna be able to find an answer. And so the Amazon culture of being customer obsessed and working as closely as possible with the customer has been really helpful to my community of of logic, uh, full methods, practitioners, because they were really forced to work with a customer, understand the problem. So what I've been doing is listening to the customer on finding out what the problems with concerns. They are focusing my attention on that. And I haven't yet heard of, uh, of customers asking for mathematical proof on crypto currency Blockchain sorts of stuff. But I'm I I await further and you're intrigued. Yeah, I'm s I always like mathematics, but where we have been hearing customers asked for help is for Temple. We're working on free Our toss s o i o T applications Understand the networks that are connecting up the coyote to the cloud, understanding the correctness of machine learning. So why, why So I reused. I've done some machine learning. I've constructed a model. How do I know what it does? And is it compliant? Does it respect hip fed ramp PC, i et cetera, and some other issues like that. >> There's a lot of talk in the industry about quantum computing and creating nightmares for guys like you. How much thought given that you have any thing that you can share with us? >> Yes. Oh, there's there's work in the AWS crypto team preparing for the post quantum world. So imagine Adversary has quantum computer. And so there are proposals on eight of us has a number of proposals, and we've and those proposals have been implemented. So their standards and we've our team has been doing proof on the correctness of those. So, actually, in the one of my talks, I think the talk not with Chad and Tom. I show a demo of our work to prove the correctness of someplace quantum code. >> So, Byron, thank you for coming on the inside. Congratulations on the automated reason. Good to see it put in the practice and appreciate the commentary. Thank you very much. Thank you. Here for the first inaugural security cloud security event reinforced AWS is putting on cube coverage. I'm John Fairy with Day Volonte. Thanks for watching

from the silicon angle media office in Boston Massachusetts it's the queue now here's your host David on tape hi everybody this is Dave lotta a and this is the cube the leader in live tech coverage this cube conversation I'm really excited Craig Hibbert is here he's a vice president of infinite at and he focuses on strategic accounts he's been in the storage business for a long time he's got great perspectives correct good to see you again thanks for coming on good to say that good to be back so there's a there's a saying don't fight fashion well you guys fight fashion all the time you got these patents you got this thing called neuro cache you're your founder and chairman mo che has always been - cutting against the grain and doing things his own way but I'd love for you to talk about some of those things the patents that you have some architecture the neuro cache fill us in on all that sure so when we go in we talk to customers and we say we have a hundred and thirty-eight patents a lot of them say well that's great but you know how does that relate to me a lot of these are and or gates and certain things that they don't know how it fits into the day to day life so I think this is a good opportunity to talk about several of those that do and so obviously the neural cache is something that is is dynamic instead of having a key in a hash which all the other vendors have just our position in that table allows us to determine all the values and things we need from it but it also monitors this is an astounding statement but from the moment that array is powered on every i/o that flows through it we track data for the life of the reins for some of these customers it's five and six years so you know those blocks of data are they random are they sequential are they hot are they cold when was the last time was accessed and this is key information because we bring intelligence to the lower level block layer where everybody else has just done they just ship things things come into acutely moving they have no idea what they are we do and the value around that is that we can then predict when workloads are aging out today you have manual people writing things in in things like easy tier or faster or competing products or two stories right and all these things that that manage all these problems are the human intervention we do it dynamically and that feeds information back into the Ray and helps to determine which virtual ray group it should reside on and where on the discipline Dalls based upon the age of the the application how it's trending the these are very powerful things in a day where we need eminent information send in to a consumer in a store I'd it's all all this dynamic processing and the ability to bring that in so that's that's one of the things we do another one is that the catalyst for our fast rebuilds we can rebuild two failed full 12 terabyte drives in under 80 minutes if those drives are half full then it's nine minutes and this is by understanding where all the data is and sharing the rebuild process from the drives that's another one of our patterns perhaps one of the most challenging that we have is that storage vendors tend to do error correction at the fibre channel layer once that data enters into the storage array there is no mechanism to check the integrity of that data and a couple of vendors have an option to do this but they can only do it for the first right and they also recommend you to turn that feature off because it slows down the box so we're infinite out is unique and I think this is for me one of the the most important paths that we have is that every time we ride a 64k slice in the system we assign some metadata to that and obviously it has a CRC check sum but more importantly it has the locality of reference so if we subsequently go back and do a reread and the CRC matches but the location has changed we know that corruption has happened sometimes a bit flipped on right all of these things that constitute sound data corruption that's not just the impressive part what we do at that point is we dynamically deduce that the data has been corrupt and using the parity in the quorum where it were a raid 6 like a dual parity configuration we rebuild that data on the fly without the application or the end-user knowing that there was a problem and that way served back the data that was actually written we guarantee that were the only array that does that today there's massive for our customers I mean the time to rebuild you said 12 terabyte drive I mean I yeah I would have thought I mean they always joke how long do you think it takes to rebuild a 30 terabyte drive because eventually you know sure you know it's like a month with us it's the same so if you look at our three terabyte drives it was 18 minutes the four terabyte drives 18 minutes the 618 minutes 812 will be good all the way up to 20 terabyte drives figuration we have no what I came back to a conversation we've had many many times we've shown you guys we were early on in the flash storage trend and we saw the prices coming down we done like high-speed spinning disks were there days were numbered and sure correct in that prediction but then you know disk drives have kept that distance yeah you guys have a skewed going all flash because the economics but help us understand this because you've got this mechanical device and you yet you guys are able to claim performance that's equal to or oftentimes much much better than a lot of your all flash competitors and I want to understand that a little bit it suggests to me that there's so much other overhead going on and other ball necks in the system that you guys are dealing with both architectural II and through your intelligence software can you talk about that absolutely absolutely the software is the key right we are a software company and we have some phenomenal guys that do the software piece so as far as the performance goes the the backend spinning discs are really obfuscated by two layers of virtualization and we ensure that because we have massive amounts of DRAM that all of that data flows into DRAM it will sit in DRAM for an astonishing five minutes I say astonishing because most of our vendors try to evict cache straight away so they've got room for the next one and that does not facilitate a mechanism by which you can inspect those dumb pieces of data and if you get enough dumb data you can start to make him intelligent right you can go get discarded data from cell phone towers and find out we know where people go to work and what time they worker because of that what demographic at the end and you know now you're predicting the election based upon discarding itself on talladega so so if you can take dumb data and put patterns around it and make it sequential which we do we write out a log structured right so we're really really fast at the front-end and some customers say well how do you manage that on the backend here's something that our designers and architects did very very well the the speed of the of ddr3 is about 15k per second which is what Cindy REM right now we have 480 spindles on the backend if you say each one of them can do a hundred 100 mics per second which they can do more than that 200 that gives us a forty eight gigabit gigabyte sorry per second backplane D stage ability which is three times faster than the DRAM so when you look at it the box has been designed all the way so there is no bottleneck through flowing through the DRAM anything that still been access that comes out of that five minute window once it's D stays to all the spindles incidentally analog structured right so right now it over 480 spindles all the time and then you've got the random still on the SSD which will help to keep that response time around about 2 milliseconds and just one last point on there I have a customer that has 1.2 petabytes written on a 1.3 a petabyte box and is still achieving a 2 millisecond response time and that's unheard of because most block arrays as you fill them up to 60 70 % that the performance starts going in the tank so I go down memory lane here so the most successful you know storage array in the history of the industry my opinion probably fact it was symmetric sand mosha a designed that he eschewed raid5 everybody was on the crazy about raid 5 is dead no no just mirror it yeah and that's gonna give us the performance that we need and he would write they would write 2d ran and then then of course you'd think that the D stage bandwidth was the bottleneck because they had such a back high a large number of back-end spindles the bandwidth coming out of that DRAM was enormous you just described something actually quite similar so that I was going to ask you is it the D stage bandwidth the bottleneck and you're saying no because your D stage being what there's actually three tighter than the D rate up it is so with the symmetric some typical platforms you would have a certain amount of disk in a disk group and you would assign a phase and Fiber Channel ports to that and there'd be certain segments in cash that would dedicated those discs we have done away with that we have so many well with two layers of the virtualization at the front as we talked about but because nothing is a bottleneck and because we've optimized each component the DRAM and I talked about the SSDs we don't write heavily over those we write in a sequential pattern to the SSD so that the wear rate is elongated and so because of that and we have all the virtualized raid groups configured in cache so what happens is as we get to that five-minute window we're about 2 D state all of the raid groups the al telling the cash how to lay out the virtual raid structure based on how busy or the raid groups are at the time so if you were to pause it and ask us where it's going we can tell you it's the Machine line it's the artificial intelligence of saying this raid group just took a D stage you know or there's a lot of data in the cache that's heading for these but based upon the the prediction of the heart the cold that I talked about a few months ago and so it will make a determination to use a different virtual rater and that's all done in memory as opposed to to rely on the disk so we're not we don't have the concept of spare disk we have the concept of spare capacity it's all shared and because it's all shared it's this very powerful pool that just doesn't get bogged down and continues to operate all the way up to the full capacity so I'm struggling with this there is no bottleneck because there's always a problem that can assure them so where is the bottleneck the ball net for us is when the erase fault so if you overwrite the maximum bandwidth and that historically you know in in 2016-2017 was a roughly 12 cube per second we got that in the fall 2018 to roundabout 15 and we're about to make the announcement that we've made tectonic increases in that where will now have right bandwidth approach in 16 gig per second and also read bandwidth about 25 K per second that 16 is going to move up to 20 remember what I said we release a number and we gradually grow into it and and and maximize and tweak that software when you think that most or flash arrays can do maybe one and a half gig per second sustained writes that gives us a massive leg up over our competition instead of buying an all flash array for this and another mid-tier array for this and coal social this you can just buy one platform that services at all all the protocols and they're all access the same way so you write an API one way mark should almost as big fan of this about writing code obviously was spinnaker and some of those other things that he's been involved in and we do the same thing so our API is the same for the block as it is for the NAS as it is for the ice cozy so it's it's very consistent you write it once and you can adapt multiple products well I think you bring about customers for short bit everybody talks about digital transformation and it's this big buzzword but when you talk to customers they're all going through some kind of digital transformation oh they want to get digital right let's put it that way yeah I don't want to get disrupted they see Amazon buying grocers and while getting into the financial services and content and it's all about the data so there's a real disruption scenario going on for every business and and the innovation engine seems to be data okay but data just sitting there and a data swamp is no good so you got to apply machine intelligence for that to that data and you got to have scale mm-hmm do you guys make a big deal about about petabyte scale yeah what are your customers telling you about the importance of that and how does it fit into that innovation sandwich that I just laid out sure no it's great question so we have some very because we're so have 70 petabytes of production over those 70 yep we have a couple of those both financial institutions very very good at what they do we worked with them previously with a with another product that really kind of introduced another one of most Shea's products that was XIV that introduced the concepts of self-healing and no tuning and things like we don't even talked about that there's no tuning knobs on the infinite I probably should mention that but our customers said have said to us we couldn't scale you know we had a couple hundred terabyte boxes before there were okay you know you've brought you've raised the game by bringing in a much higher level of availability and much higher capacity we can take one of our but I'm in this process right now the customer we can take one of our boxes and collapse three vmax 20 of VMAX 40s on it we have numerous occassions gone into establishments that have 11 12 23 inch cabinets two and a half thousand spindles of the old DMC VMO station we've replaced it with one 19-inch rack of arts right that's a phenomenal state when you think about it and that was paid for you think some of these v-max 47 it's 192 ports on them Fiber Channel ports we have 24 so the fibre channel port reduction the power heating and cooling over an entire row down to one eight kilowatt consumption by the way our power is the same whether it's three four terabytes six eight twelve they all use the same power plan so as we increase the geometry capacity of the drives we decrease the cost per usable well we're actually far more efficient than all fly sharing with the most environmentally friendly hybrids been in this planet on the array so asking about cloud so miss gray on the planet that would be yeah so when cloud first sort of came out of the division Financial Services guys are like no clouds that's a bad word they're definitely you know leaning into that adopting it more but still there's a lot of workloads that they're gonna leave on Prem they want to that cloud experience to the data what are you hearing from the financial services customers in particular and I and I've single them out because they're they're very advanced they're very demanding they are they a lot of dough and so what do you see in terms of them building cloud hybrid cloud and and what it means for for them and specifically the storage industry yeah so I'm actually surprised that they've adopted it as much as they have to be honest with you and I think the the economics are driving that but having said that whenever they want to get the data back or they want to bring it back home prime for various reasons that's when they're running into problems right it's it's like how do I get my own data back well you've got to open up the checkbook and write big checks so I think infini debt has a nice strategy there where we have the same capabilities that you have on prime you having the cloud don't forget nobody else has that one of the encumbrances to people move into the cloud has been that it lacks the enterprise functionality that people are used to in the data center but because our cost point is so affordable we become not only very attractive or four on Prem but for cloud solutions as well of course we have our own new tricks cloud offering which allows people to use as dr or replications and so however you want to do it where you can use the same api's and code that your own dis and extrapolate that out to the cloud I was there which is which is very helpful and so we have the ability if you take a snapshot on Amazon it may take four hours and it's been copied over to an s3 device that's the only way they can make it affordable to do it and then if you need that data back it's it's not it's not imminent you've got to rehydrate from s3 and then copy it back over your snapshot with infinite data its instantaneous we do not stop i/o when we do snapshots and another one the patterns we use the time synchronous mechanism every every AO the rise has a timestamp and we when we take a snapshot we just do a point in time and in a timestamp that's greater than that instantiation point is for the volume and previous is for the snapshot we can do that in the cloud we can instantly recover hundreds of terabytes worth of databases and make them instantly available so our story again with the innovation our innovation wasn't just for for on pram it was to be facilitated anyway you are and that same price point carries forward from here into the cloud when Amazon and Microsoft wake up and realized that we have this phenomenal story here I think they'll be buying from us in leaps and bounds it's it's the only way to make the cloud affordable for storage vendors so these are the things you talk about you know bringing bringing data back and bringing workloads back and and there are tool chains that are now on Prem the kubernetes is a great example that our cloud like and so when you bring data back you want to have that cloud experience so automated operations plays into that you know automation used to be something that people are afraid of and they want to do do manual tearing member they wanted their own knobs to turn those days are gone because people want to drive digital transformations they don't want to spend time doing all this heavy lifting I'm talk about that a little bit and where you guys fit yeah I mean you know I say to my customers to not to knock our competition but you can't have a service processor as the inter communication point between what the customer wants and it deciding where it's going to talk to the Iranian configure it's going to be instantaneous and so we all we have we don't have any Java we don't have any flash we don't have any hosts we don't have massive servers around the data center collecting information we just have an html5 interface and so our time to deployment is very very quick when we land on the customer's dark the box goes in we hook up the power we put the drives in we're Haiti's the word V talk because it brings back memories for a lot of course I am now we're going back in time right knowing that main here and so we're very dynamic both in how we forward face the customers but also on the backend for ourselves we eat our own dog food in the sense that we are we have an automation team we've automated our migration from non infinite out platforms towards that uses some level of artificial intelligence we've also built a lot of parameters around things like going with ServiceNow and custom sites because well you can do with our API what other people take you know page and page of code I'll give you an example one of our customers said I need OC i the the let-up management product we called met up and they said hey listen you know it usually takes six months to get an appointment and that it takes at least six months to do the comb we said no no we're not like any other storage render we don't have all these silly raid groups and spare disk capacity you know this weave three commands we can show in the API and we showed them the light Wow can you send us an array we said no we can do something better we were designed SDS right when when infinite out was coded there was no hardware and the reason we did that is because software developers will always code to the level of resilience of the hardware so if you take away that Hardware the software developers have to code to make something to withstand any type of hardware that comes in and at the end of the coding process that's when we started bringing in the hardware pieces so we were written STS we can send vendors and customers a an OVA a virtual appliance of our box they were able to the in a week they told the custom we have to go through full QA no reason why it wouldn't work and they did it for us and got it was a massive customer of theirs and ours that's a powerful story the time to deployment for your homegrown apps as well as things like ServiceNow an MCI incredible infinite out three API calls we were done so you guys had a little share our partnership with met up in the field we did yeah I mean was great they had a massive license with this particular customer they wanted our storage on the platform and we worked very very quickly with them they were very accommodating and we'd love to get our storage qualified behind their behind their heads right now for another customer as well so yeah there's definitely some sooner people realize what we have a Splunk massive for us what we're able to do was plunk in one box where people the competitors can't do in a row so it so it's very compelling what we actually bring in how we do it and that API level is incredibly powerful and we're utilizing that ourselves I would like to see some integration with canonical Marshall what these guys have done a great job with SDS plays we'd like to bring that here do spinnaker do collect if I could do some of those things as well that we're working on the automation we just added another employee another FTE to the automation team and infinite out so we do these and we engage with customers and we help you get out of that trench that is antiquity and move forward into the you know into the vision of how you do one thing well and it permeates the cloud on primary and hybrid all those guys well that API philosophy that you have in the infrastructure is code model that you just described allows you to build out your ecosystem in a really fast way so Greg thanks so much for coming on thank you and doing that double click with this really I'd love to have you back great thanks a lot Dave all right thank you welcome thank you for watching you're watching the cube and this is Dave Volante we'll see you next time

>> Announcer: Live from Washington, D.C., it's theCUBE, covering .conf2017. Brought to you by Splunk. >> Welcome back. Here on theCUBE, we continue our coverage of .conf2017, Splunk's get together here with some 7,000 plus attendees, 65 countries, we're right on the showfloor. A lot of buzz happening down here and it's all good. Along with Dave Vellante, I'm John Walls. We are live, as I said, in our nation's capital, and we're joined by a guest who represents her organization that is a member of the Splunk4Good program. We're going to explain that in just a little bit, but Sherrie Caltagirone is the founder and executive director of the Global Emancipation Network, and Sherry, thanks for being with us. We appreciate your time. >> Thanks so much for having me on, John. >> So your organization has to do with countering and combating global trafficking, human trafficking. >> That's right. >> We think about sex trafficking, labor trafficking, but you're a participant in the Splunk4Good program, which is their ten year pledge to support organizations such as yours to the tune of up to $100 million over that ten years to all kinds of organizations. So first off, let's just talk about that process, how you got involved, and then we want to get into how you're actually using this data that you're mining right now for your work. So first off, how'd you get involved with Splunk? >> Absolutely. It was really organic in that it's a really small community. There are a lot of people in the tech space who I found really want to use their skills for good, and they're very happy to make connections between people. We had a mutual friend actually introduce me to Monzy Merza, who's the head of security here at Splunk, and he said, "I'm really passionate about trafficking, I want to help "fight trafficking, let me connect you with Corey Marshall "at Splunk4Good." The rest is really history, and I have to tell you, yes, they have pledged up to $100 million to help, and in products and services, but what's more is they really individually care about our projects and that they are helping me build things, I call them up all the time and say, "Hey let's brainstorm an idea, "let's solve a problem, "let's figure out how we can do this together, and they really are, they're part of my family. They're part of GEN and Global Emancipation Network. >> That's outstanding. The size of the problem struck me today at the keynote when we talked about, first off, the various forms of trafficking that are going on; you said up to two dozen different subsets of trafficking, and then the size and the scale of 25 to 40-some million people around the globe are suffering. >> Yeah. >> Because of trafficking conditions. That puts it all in a really different perspective. >> You're right. Those weren't even numbers that we can really fathom what that means, can we? We don't know what 20 million looks like, and you're right, there's such a wide discrepancy between the numbers. 20 million, 46 million, maybe somewhere in between, and that is exactly part of the problem that we have is that there is no reliable data. Everyone silos their individual parts of the data that they have for trafficking, all the the different stakeholders. That's government, NGOs, law enforcement, academia, it's all kinds. It runs the gamut, really, and so it's really difficult to figure out exactly what the truth is. There's no reliable, repeatable way to count trafficking, so right now it's mostly anecdotal. It's NGOs reporting up to governments that say, "We've impacted this many victims," or, "We've encountered so-and-so who said that the "trafficking ring that they escaped from had 20 other people "in it," things like that, so it's really just an estimate, and it's the best that we have right now, but with a datalet approach, hopefully we'll get closer to a real accurate number. >> So talk more about the problem and the root of the problem, how it's manifesting itself, and we'll get into sort of what we can do about it. >> Yeah. It's really interesting in that a lot of the things that cause poverty are the same things that cause trafficking. It really is, you know, people become very vulnerable if they don't have a solid source of income or employment, things like that, so they are more willing to do whatever's necessary in order to do that, so it's easy to be lured into a situation where you can be exploited, for example, the refugee crisis right now that's happening across Europe and the Middle East is a major player for trafficking. It's a situation completely ripe for this, so people who are refugees who perhaps are willing to be smuggled out of the country, illegally, of course, but then at that point they are in the mercy and the hands of the people who smuggled them and it's very easy for them to become trafficked. Things like poverty, other ways that you're marginalized, the LGBTQ community is particularly vulnerable, homeless population, a lot of the same issues that you see in other problems come up, creates a situation of vulnerability to be exploited, and that's all trafficking really is: the exploitation of one individual through force, coercion, fraud, position of authority, to benefit another person. >> These individuals are essentially what, enslaved? >> Yeah. It's modern day slavery. There's lots of different forms, as you mentioned. There's labor trafficking, and that's several different forms; it can be that you're in a brick factory, or maybe you're forced into a fishing boat for years and years. Usually they take away your passport if you are from another country. There's usually some threats. They know where your family lives. If you go tell anyone or you run away, they're going to kill your family, those sorts of things. It is, it's modern day slavery, but on a much, much bigger scale, so it's no longer legal, but it still happens. >> How does data help solve the problem? You, as an executive director, what kind of data, when you set the North Star for the organization from a data perspective, what did that look like, and how is it coming into play? >> Well, one of the benefits that we have as an organization that's countering trafficking is that we are able to turn the tables on traffickers. They are using the internet in much the way that other private enterprises are. They know that that's how they move their product, which in this case is sadly human beings. They advertise for victims online. They recruit people online. They're using social media apps and things like Facebook and Kick and Whatsapp and whatnot. Then they are advertising openly for the people that they have recruited into trafficking, and then they are trying to sell their services, so for example, everyone knows about Backpage. There's hundreds of websites like that. It runs the gamut. They're recruiting people through false job advertisements, so we find where those sites are through lots of human intelligence and we're talking to lots of people all the time, and we gather those, and we try to look for patterns to identify who are the victims, who are the traffickers, what can we do about it? The data, to get back to your original question, is really what is going to inform policy to have a real change. >> So you can, in terms of I guess the forensics that you're doing, or whatever you're doing with that data, you're looking at not only the websites, but also the communications that are being spawned by those sites and looking to where those networks are branching off to? >> Yeah. That's one of the things that we really like to try to do. Instead of getting a low-level person, we like to try to build up an entire network so we can take down an entire ring instead of just the low fish. We do, we extract all the data from the website that we can to pull out names, email addresses, physical addresses, phone numbers, things like that, and then begin to make correlations; where else have we seen those phone numbers and those addresses on these other websites that we're collecting from, or did this person make a mistake, which we love to exploit mistakes with traffickers, and are they using the same user handle on their personal Flickr page, so then we can begin to get an attribution. >> John: That happens? >> Absolutely. >> It does, yeah. >> Sherrie: Without giving away all my secrets, exactly. >> Yeah, I don't to, don't give away the store, here. How much, then, are you looking internationally as opposed to domestically, then? >> We collect right now from 22 different countries, I think 77 individual cities, so a lot of these websites are usually very jurisdictionally specific, so, you know, like Craigslist; you go into Washington state and click on Seattle, something like that. We harvest from the main trafficking points that we can. We're collecting in six different languages right now. A lot of the data that we have right now is from the U.S. because that's the easier way to start is the low-hanging fish. >> What does your partner ecosystem look like? It comprises law enforcement, local agencies, federal agencies, presumably, NGOs. Will you describe that? >> Yeah. We do, we partner with attorneys general, we partner with law enforcement, those are the sort of operational partners we look for when we have built out intelligence. Who do we give it to now, because data is useless unless we do something with it, right? So we we build out these target packages and intelligence and give it to people who can do something with it, so those are really easy people to do something with. >> How hard is that, because you've got different jurisdictions and different policies, and it's got to be like herding cats to get guys working with you. >> It is, and it's actually something that they're begging for, and so, it's a good tool that they can use to deconflict with each other, 'cause they are running different trafficking-related operations all the time, and jurisdictions, they overlap in many cases, especially when you're talking about moving people, and they're going from one state to another state, so you have several jurisdictions and you need to deconflict your programs. >> Okay, so they're very receptive to you guys coming to them with they data. >> They are; they really want help, and they're strapped for resources. These are for the most part, not technically savvy people, and this is one of the good things about our nonprofit is that it is a staff of people who are very tech-savvy and who are very patient in explaining it and making it easy and usable and consumable by our customers. >> So if I'm an NGO out there, I'm a non-profit out there, and I'm very interested in having this kind of service, what would you say to them about what they can pursue, what kind of relationship you have with Splunk and the value they're providing, and what your experience has been so far. >> It's been wonderful. I've been over at the Splunk4Good booth all day helping out and it's been wonderful to see not only just the non-profits who have come up and said, "Hey, I run a church, "I'm trying to start a homeless shelter for drug-addicted "individuals, how can you help me," and it's wonderful when you start to see the light bulbs go off between the non-profit sector and the tech sector, between the philanthropic organizations like Splunk4Good, the non-profits, and then, we can't forget the third major important part here, which is, those are the tech volunteers, these are the people who are here at the conference and who are Splunk employees and whatnot and teaching them that they can use their skills for good in the non-profit sector. >> Has cryptocurrency, where people can conduct anonymous transactions, made your job a lot more difficult? >> No, it hasn't, and there's been a lot of research that has gone into block chain analysis, so for example, Backpage, all the adds are purchased with Bitcoin, and so there's been a wonderful amount of research then, trying to time the post to when the Bitcoin was purchased, and when the transactions happen, so they've done that, and it's really successful. There are a couple of other companies who do just that, like Chainalysis, that we partner with. >> You can use data to deanonymize? >> That's correct. It's not as anonymous as people think it is. >> Love it. >> Yeah, exactly. We love to exploit those little things like that. A lot of the websites, they put their wallets out there, and then we use that. >> Dave: You're like reverse hackers. >> That's right. It's interesting that you say that, because a lot of our volunteers actually are, they're hacker hunters. They're threat and intel analysts and whatnot, and so, they've learned that they can apply the exact same methods and techniques into our field, so it's brilliant to see the ways in which they do that. >> Dave: That's a judo move on the bad guys. >> Exactly. How long does this go on for you? Is this a year-to-year that you renew, or is it a multi-year commitment, how does that work? >> It's a year-to-year that we renew our pledge, but they're in it for the long haul with us, so they know that they're not getting rid of me and nor do they want me to, which is wonderful. It's so good, because they help, they sit at the table with me, always brainstorming, so it's year-to-year technically, but I know that we're in it together for the long haul. >> How about fundraising? A big part of your job is, you know. >> Of course it is. >> Fundraising. You spend a lot of time there. Maybe talk about that a little bit. >> Yeah, absolutely. Some of our goals right now, for example, is we're really looking to hire a full-time developer, we want a full-time intelligence analyst, so we're always looking to raise donations, so you could donate on our website. >> John: Which is? >> Which is globalemancipation.ngo. Globalemancipation.ngo. We're also always looking for people who are willing to help donate their time and their skills and whatnot. We have a couple of fundraising goals right now. We're always looking for that. We receive a lot of product donations from companies all over the world, mostly from the tech sector. We're really blessed in that we aren't spending a lot of money on that, but we do need to hire a couple of people so that's our next big goal. >> I should have asked you this off the top. Among your titles, executive director and founder, what was the founder part? What motivated you to get involved in this, because it's, I mean, there are a lot of opportunities to do non-profit work, but this one found you, or you found it. >> That's right. It's a happy circumstance. I've always done anti-human trafficking, since my college days, actually. I started volunteering, or I started to intern at the Protection Project at Johns Hopkins University, which was a legislative-based program, so it was really fantastic, traveling the world, helping countries draft legislation on trafficking, but I really wanted to get closer and begin to measure my impact, so that's when I started thinking about data anyways, to be able to put our thumb, is what we're doing. Working. How are we going to be able to measure success and what does that look like? Then I started volunteering for a rescue operations organization; the sort of knock down the doors, go rescue people group, and so, I really liked having the closer impact and being able to feel like hey, I can do something about this problem that I know is terrible and that's why it spread. A lot of the people I worked with, including my husband, come from the cyberthreat intelligence world, so I feel like those ideas and values have been steeped in me, slowly and surely, over the last decade, so that just ages myself a little bit maybe, but yes, so those ideas have been percolating over time, so it just kind of happened that way. >> Well, you want to feel young, hang around with us. (laughing) I should speak for myself, John, I'm sorry. >> No, no, you're right on, believe me. I was nodding my head right there with you. >> Can you comment on the media coverage? Is it adequate in your view? Does there need to be more? >> On trafficking itself? You know, it's really good that it's starting to come into the forefront a lot more. I'm hearing about it. Five years ago, most of the time, if I told people that there are still people in slavery, it didn't end with the Civil War, they would stand at me slackjawed. There have been a few big media pushes. There's been some films, like Taken, Liam Neeson's film, so that's always the image I use, and that's just one type of trafficking, but I'm hearing more and more. Ashton Kutcher runs a foundation called Thorn that's really fantastic and they do a similar mission to what I do. He has been able to raise the spotlight a lot. Currently there's a debate on the floor of the Senate right now, too, talking about section 230 of the CDA, which is sort of centered around the Backpage debate anyway. Where do we draw the line between the freedom of speech on the internet, with ESPs in particular, but being able to still catch bad guys exactly. The Backpage sort of founder idea. It's really hot and present in the news right now. I would love to see the media start to ask questions, drill down into the data, to be able to ask and answer those real questions, so we're hoping that Global Emancipation Network will do that for the media and for policy makers around the world. >> Well it is extraordinary work being done by an extraordinary person. It's a privilege to have you on with us, here on theCUBE. We thank you, not only for the time, but for the work you're doing, and good luck with that. >> Thank you very much for having me on. I really appreciate it. >> You bet. That's the Global Emancipation Network. Globalemancipation.ngo right? Fundraising, always helpful. Back with more here on theCUBE in Washington D.C., right after this. (electronic beats)

>> Announcer: Live from Las Vegas, it's theCUBE. Covering Veritas Vision 2017. Brought to you by Veritas. (peppy digital music) >> Veritas Vision 2017 everybody. We're here at The Aria Hotel. This is day two of theCUBE's coverage of Vtas, #VtasVision, and this is theCUBE, the leader in live tech coverage. My name is Dave Vellante, and I'm here with Stuart Miniman who is my cohost for the week. Stu, we heard Richard Branson this morning. The world-renowned entrepreneur Sir Richard Branson came up from the British Virgin Islands where he lives. He lives in the Caribbean. And evidently he was holed out during the hurricane in his wine cellar, but he was able to make it up here for the keynote. We saw on Twitter, so, great keynote, we'll talk about that a little bit. We saw on Twitter that he actually stopped by the Hitachi event, Hitachi NEXT for women in tech, a little mini event that they had over there. So, pretty cool guy. Some of the takeaways: he talked a lot about- well, first of all, welcome to day two. >> Thanks, Dave. Yeah, and people are pretty excited that sometimes they bring in those marquee guests, someone that's going to get everybody to say, "Okay, wait, it's day two. "I want to get up early, get in the groove." Some really interesting topics, I mean talking about, thinking about the community at large, one of the things I loved he talked about. I've got all of these, I've got hotels, I've got different things. We draw a circle around it. Think about the community, think about the schools that are there, think about if there's people that don't have homes. All these things to, giving back to the community, he says we can all do our piece there, and talking about sustainable business. >> As far as, I mean we do a lot of these, as you know, and as far as the keynote speakers go, I thought he was one of the better ones. Certainly one of the bigger names. Some of the ones that we've seen in the past that I think are comparable, Bill Clinton at Dell World 2012 was pretty happening. >> There's a reason that Bill Clinton is known as the orator that he is. >> Yeah, so he was quite good. And then Robert Gates, both at ServiceNow and Nutanics, Condi Rice at Nutanics, both very impressive. Malcolm Gladwell, who's been on theCUBE and Nate Silver, who's also been on theCUBE, again, very impressive. Thomas Friedman we've seen at the IBM shows. The author, the guy who wrote the Jobs book was very very strong, come on, help me. >> Oh, yeah, Walter Isaacson. >> Walter Isaacson was at Tableau, so you've seen some- >> Yeah, I've seen Elon Musk also at the Dell show. >> Oh, I didn't see Elon, okay. >> Yeah, I think that was the year you didn't come. >> So I say Branson, from the ones I've seen, I don't know how he compared to Musk, was probably the best I think I've ever seen. Very inspirational, talking about the disaster. They had really well-thought-out and well-produced videos that he sort of laid in. The first one was sort of a commercial for Richard Branson and who he was and how he's, his passion for changing the world, which is so genuine. And then a lot of stuff on the disaster in the British Virgin Islands, the total devastation. And then he sort of went into his passion for entrepreneurs, and what he sees as an entrepreneur is he sort of defined it as somebody who wants to make the world a better place, innovations, disruptive innovations to make the world a better place. And then had a sort of interesting Q&A session with Lynn Lucas. >> Yeah, and one of the lines he said, people, you don't go out with the idea that, "I'm going to be a businessman." It's, "I want to go out, I want to build something, "I want to create something." I love one of the early anecdotes that he said when he was in school, and he had, what was it, a newsletter or something he was writing against the Vietnam War, and the school said, "Well, you can either stay in school, "or you can keep doing your thing." He said, "Well, that choice is easy, buh-bye." And when he was leaving, they said, "Well, you're either going to be, end up in jail or be a millionaire, we're not sure." And he said, "Well, what do ya know, I ended up doing both." (both laughing) >> So he is quite a character, and just very understated, but he's got this aura that allows him to be understated and still appear as this sort of mega-personality. He talked about, actually some of the interesting things he said about rebuilding after Irma, obviously you got to build stronger homes, and he really sort of pounded the reducing the reliance on fossil fuels, and can't be the same old, same old, basically calling for a Marshall Plan for the Caribbean. One of the things that struck me, and it's a tech audience, generally a more liberal audience, he got some fond applause for that, but he said, "You guys are about data, you don't just ignore data." And one of the data points that he threw out was that the Atlantic Ocean at some points during Irma was 86 degrees, which is quite astounding. So, he's basically saying, "Time to make a commitment "to not retreat from the Paris Agreement." And then he also talked about, from an entrepreneurial standpoint and building a company that taking note of the little things, he said, makes a big difference. And talking about open cultures, letting people work from home, letting people take unpaid sabbaticals, he did say unpaid. And then he touted his new book, Finding My Virginity, which is the sequel to Losing My Virginity. So it was all very good. Some of the things to be successful: you need to learn to learn, you need to listen, sort of an age-old bromide, but somehow it seemed to have more impact coming from Branson. And then, actually then Lucas asked one of the questions that I put forth, was what's his relationship with Musk and Bezos? And he said he actually is very quite friendly with Elon, and of course they are sort of birds of a feather, all three of them, with the rocket ships. And he said, "We don't talk much about that, "we just sort of-" specifically in reference to Bezos. But overall, I thought it was very strong. >> Yeah Dave, what was the line I think he said? "You want to be friends with your competitors "but fight hard against them all day, "go drinking with them at night." >> Right, fight like crazy during the day, right. So, that was sort of the setup, and again, I thought Lynn Lucas did a very good job. He's, I guess in one respect he's an easy interview 'cause he's such a- we interview these dynamic figures, they just sort of talk and they're good. But she kept the conversation going and asked some good questions and wasn't intimidated, which you can be sometimes by those big personalities. So I thought that was all good. And then we turned into- which I was also surprised and appreciative that they put Branson on first. A lot of companies would've held him to the end. >> Stu: Right. >> Said, "Alright, let's get everybody in the room "and we'll force them to listen to our product stuff, "and then we can get the highlight, the headliner." Veritas chose to do it differently. Now, maybe it was a scheduling thing, I don't know. But that was kind of cool. Go right to where the action is. You're not coming here to watch 60 Minutes, you want to see the headline show right away, and that's what they did, so from a content standpoint I was appreciative of that. >> Yeah, absolutely. And then, of course, they brought on David Noy, who we're going to have on in a little while, and went through, really, the updates. So really it's the expansion, Dave, of their software-defined storage, the family of products called InfoScale. Yesterday we talked a bit about the Veritas HyperScale, so that is, they've got the HyperScale for OpenStack, they've got the HyperScale for containers, and then filling out the product line is the Veritas Access, which is really their scale-out NAS solution, including, they did one of the classic unveils of Veritas Software Company. It was a little odd for me to be like, "Here's an appliance "for Veritas Bezel." >> Here's a box! >> Partnership with Seagate. So they said very clearly, "Look, if you really want it simple, "and you want it to come just from us, "and that's what you'd like, great. "Here's an appliance, trusted supplier, "we've put the whole thing together, "but that's not going to be our primary business, "that's not the main way we want to do things. "We want to offer the software, "and you can choose your hardware piece." Once again, knocking on some of those integrated hardware suppliers with the 70 point margin. And then the last one, one of the bigger announcements of the show, is the Veritas Cloud Storage, which they're calling is object storage with brains. And one thing we want to dig into: those brains, what is that functionality, 'cause object storage from day one always had a little bit more intelligence than the traditional storage. Metadata is usually built in, so where is the artificial intelligence, machine learning, what is that knowledge that's kind of built into it, because I find, Dave, on the consumer side, I'm amazed these days as how much extra metadata and knowledge gets built into things. So, on my phone, I'll start searching for things, and it'll just have things appear. I know you're not fond of the automated assistants, but I've got a couple of them in my house, so I can ask them questions, and they are getting smarter and smarter over time, and they already know everything we're doing anyway. >> You know, I like the automated assistants. We have, well, my kid has an Echo, but what concerns me, Stu, is when I am speaking to those automated assistants about, "Hey, maybe we should take a trip "to this place or that place," and then all of a sudden the next day on my laptop I start to see ads for trips to that place. I start to think about, wow, this is strange. I worry about the privacy of those systems. They're going to, they already know more about me than I know about me. But I want to come back to those three announcements we're going to have David Noy on: HyperScale, Access, and Cloud Object. So some of the things we want to ask that we don't really know is the HyperScale: is it Block, is it File, it's OpenStack specific, but it's general. >> Right, but the two flavors: one's for OpenStack, and of course OpenStack has a number of projects, so I would think you could be able to do Block and File but would definitely love that clarification. And then they have a different one for containers. >> Okay, so I kind of don't understand that, right? 'Cause is it OpenStack containers, or is it Linux containers, or is it- >> Well, containers are always going to be on Linux, and containers can fit with OpenStack, but we've got their Chief Product Officer, and we've got David Noy. >> Dave: So we'll attack some of that. >> So we'll dig into all of those. >> And then, the Access piece, you know, after the apocalypse, there are going to be three things left in this world: cockroaches, mainframes, and Dot Hill RAID arrays. When Seagate was up on stage, Seagate bought this company called Dot Hill, which has been around longer than I have, and so, like you said, that was kind of strange seeing an appliance unveil from the software company. But hey, they need boxes to run on this stuff. It was interesting, too, the engineer Abhijit came out, and they talked about software-defined, and we've been doing software-defined, is what he said, way before the term ever came out. It's true, Veritas was, if not the first, one of the first software-defined storage companies. >> Stu: Oh yeah. >> And the problem back then was there were always scaling issues, there were performance issues, and now, with the advancements in microprocessor, in DRAM, and flash technologies, software-defined has plenty of horsepower underneath it. >> Oh yeah, well, Dave, 15 years ago, the FUD from every storage company was, "You can't trust storage functionality "just on some generic server." Reminds me back, I go back 20 years, it was like, "Oh, you wouldn't run some "mission-critical thing on Windows." It's always, "That's not ready for prime time, "it's not enterprise-grade." And now, of course, everybody's on the software-defined bandwagon. >> Well, and of course when you talk to the hardware companies, and you call them hardware companies, specifically HPE and Dell EMC as examples, and Lenovo, etc. Lenovo not so much, the Chinese sort of embraced hardware. >> And even Hitachi's trying to rebrand themselves; they're very much a hardware company, but they've got software assets. >> So when you worked at EMC, and you know when you sat down and talked to the guys like Brian Gallagher, he would stress, "Oh, all my guys, all my engineers "are software engineers. We're not a hardware company." So there's a nuance there, it's sort of more the delivery and the culture and the ethos, which I think defines the software culture, and of course the gross margins. And then of course the Cloud Object piece; we want to understand what's different from, you know, object storage embeds metadata in the data and obviously is a lower cost sort of option. Think of S3 as the sort of poster child for cloud object storage. So Veritas is an arms dealer that's putting their hat in the ring kind of late, right? There's a lot of object going on out there, but it's not really taking off, other than with the cloud guys. So you got a few object guys around there. Cleversafe got bought out by IBM, Scality's still around doing some stuff with HPE. So really, it hasn't even taken off yet, so maybe the timing's not so bad. >> Absolutely, and love to hear some of the use cases, what their customers are doing. Yeah, Dave, if we have but one critique, saw a lot of partners up on stage but not as many customers. Usually expect a few more customers to be out there. Part of it is they're launching some new products, not talking about very much the products they've had in there. I know in the breakouts there are a lot of customers here, but would have liked to see a few more early customers front and center. >> Well, I think that's the key issue for this company, Stu, is that, we talked about this at the close yesterday, is how do they transition that legacy install base to the new platform. Bill Coleman said, "It's ours to lose." And I think that's right, and so the answer for a company like that in the playbook is clear: go private so you don't have to get exposed to the 90 day shock lock, invest, build out a modern platform. He talked about microservices and modern development platform. And create products that people want, and migrate people over. You're in a position to do that. But you're right, when you talk to the customers here, they're NetBackup customers, that's really what they're doing, and they're here to sort of learn, learn about best practice and see where they're going. NetBackup, I think, 8.1 was announced this week, so people are glomming onto that, but the vast majority of the revenue of this company is from their existing legacy enterprise business. That's a transition that has to take place. Luckily it doesn't have to take place in the public eye from a financial standpoint. So they can have some patient capital and work through it. Alright Stu, lineup today: a lot of product stuff. We got Jason Buffington coming on for getting the analyst perspective. So we'll be here all day. Last word? >> Yeah, and end of the day with Foreigner, it feels like the first time we're here. Veritas feels hot-blooded. We'll keep rolling. >> Alright, luckily we're not seeing double vision. Alright, keep it right there everybody. We'll be back right after this short break. This is theCUBE, we're live from Vertias Vision 2017 in Las Vegas. We'll be right back. (peppy digital music)

>> Announcer: Live, from Boston, Massachusetts, it's the Cube. Covering Red Hat Summit 2017. Brought to you by Red Hat. (futuristic tone) >> Welcome back to the Cube's coverage of the Red Hat Summit here in Boston, Massachusetts. I'm your host, Rebecca Knight, along with my co-host Stu Miniman. We are welcoming right now Sam Ramji. He is the Vice President of Product Management Google Cloud Platforms. Thanks so much for joining us. >> Thank you, Rebecca, really appreciate it. And Stu good to see you again. >> So in your keynote, you talked about how this is the age of the developer. You said this is the best time in history to be a developer. We have more veneration, more cred in the industry. People get us, people respect us. And yet you also talked about how it is also the most challenging time to be a developer. Can you unpack that a little bit for our viewers? >> Yeah, absolutely. So I think there's two parts that make it really difficult. One is just the velocity of all the different pieces, how fast they're moving, right? How do you stay on top of all the different latest technology, right? How do you unpack all of the new buzzwords? How do you say this is a cloud, that's not a cloud? So you're constantly racing to keep up, but you're also maintaining all of your old systems, which is the other part that makes it so complex. Many old systems weren't built for modernization. They were just kind of like hey, this is a really cool thing, and they were built without any sense of the history, or the future that they'd be used in. So imagine the modern enterprise developer who's got a ship software at high rates of speed, support new business initiatives, they've got to deliver innovation, and they have to bridge the very new with the very old. Because if your mobile app doesn't talk to your mainframe, you are not going to move money. It's that simple. There's layers of technology architecture. In fact, you could think of it as technology archeology, as I mentioned in the keynote, right, this we don't want to create a new genre of people called programmer archeologists, who have to go-- >> I'm picturing them just chipping away. >> Sam: I don't think it'll be as exciting as Indiana Jones. >> No. >> Digging through layers of the stack is not really what people want to be doing with their time. >> Sam: Temple of the lost kernel. >> I love it. >> So Sam, it's interesting to kind of see, I was at the Google Cloud event a couple months ago, and here you bring up the term open cloud, which part of me wants to poke a hole in that and be like, come on, everybody has their cloud. Come on, you want to lock everybody in, you've got the best technology, therefore why isn't it just being open because it's great to say open and maybe people will trust you. Help explain that. >> Puppies, freedom, apple pie, motherhood, right. >> Stu: Yeah, yeah. (laughs) >> So there's a couple sides to that. One, we think the cloud is just a spectacular opportunity. We think about 1.2 trillion dollars in current spend will end up in cloud. And the cloud market depending on how you measure it is in the mid 20 billions today. So there's just unbounded upside. So we don't have to be a aspirational monopolist in order to be a successful business. And in fact, if you wind the clock forward, you will see that every market ends up breaking down into a closed system and a closed company, and an open platform. And the open platforms tend to grow more slowly, sort of exponential versus logarithmic, is how we think about it. So it's a pragmatic business strategy. Think about Linux in '97. Think about Linux in 2002. Think about Linux in 2007. Think about Linux in 2012. Think about Linux today. Look at that rate. It's the only thing that you're going to use. So open is very pragmatic that way. It's pragmatic in another direction which is customer choice. Customers are going to come for things that give them more options. Because your job is to future proof your business, to create what in the financial community call optionality. So how do you get that? In 2011, about eight other people and I created a nonprofit called the Open Cloud Initiative. And the Initiative is long since dead, we didn't fund it right, we kind of got these ideas baked, and then moved on. >> Stu: There's another OCI now. >> That's right, it's the Open Container Initiative. But we had three really crisp concepts there. We said number one, an open cloud will be based on open source. There won't be stuff that you can't get, can't replicate, can't build yourself. Second, we said, it'll have open access. There'll be no barriers to entry or exit. There won't be any discrimination on which users can or can't come in, and there won't be any blockers to being able to take your stuff out. 'Cause we felt that without open access, the cloud would be unsafe at any speed, to borrow a quote from Ralph Nader. And then third, built on an open ecosystem. So if you are assuming that you have to be able to be open to tens of thousands of different ideas, tens of thousands of different software applications, which are maybe database infrastructure, things that as a cloud provider, you might want to be a first party provider of. Well those things have to compete, or trade off or enrich each other in a consistent way, in a way that's fair, which is kind of what we mean when we say open ecosystem, but being able to be pulled through is going to give you that rate of change that you need to be exponential rather than logarithmic. So it's based on some fairly durable concepts, but I welcome you to poke holes in it. >> So we did an event with MIT a little while back. We had Marshall Van Alstyne, professor at BU who I know you know. He's an advisor at Cloud Foundry, and he talked about those platforms and it was interesting, you know, with the phone system you had Apple who got lots of the money, smaller market share as opposed to Android, which of course comes out of Google, has all of the adoption but less revenue. So, not sure it's this, yeah. >> Interestingly, we've run those curves, and you kind of see that same logarithmic versus exponential shift happening in Android. So we've seen, I don't have the latest numbers on the top of my head, but that is generating billions of dollars of third party revenue now. So share does shift over time in favor of openness and faster innovation. >> So let's bring it back to Red Hat here, because if I talk to all the big public cloud guys, Microsoft has embraced open source. >> And they're not just guys, actually, there's lots of women. >> Rebecca: Yes, thank you. >> Stu: I apologize. >> Sorry, I'm in a little bit of a jam here, where I'm trying to tell people the collective noun for technologists is not guys. >> Stu: Okay. >> It could be people, it could be folks, internally we use squirrels from time to time, just to invite people in. >> So, when I talk to the cloud squirrels, Microsoft has embraced open source. Amazon has an interesting relationship. >> I was there when that happened. >> You and I both know the people that they've brought in who have very good credibility in the open source community that are helping out Amazon there. Is it Kubernetes that makes you open because I look at what Red Hat's doing, we say okay, if I want to be able to live across many clouds or in my own data centers, Kubernetes is a layer to do that. It comes back to some of the things like Cloud Foundry. Is that what makes it open because I have choice, or is there more to it that you want to cover from an open cloud standpoint, from a Google standpoint? >> Open and choice effectively is a spectrum of effort. If it's incredibly difficult, it's the same as not having a choice. If it's incredibly easy, then you're saying actually, you really are free to come and go. So Kubernetes is kind of the brightest star in the solar system of open cloud. There's a lot of other technologies, new things that are coming out, like istio and pluri. I don't want to lose you in word soup. Linker D, container D, a lot of other things, because this is a whole new field, a whole fabric that has to come to bear, that just like the internet, can layer on top of your existing data centers or your existing clouds, that you can have other applications or other capabilities layered on top of it. So this permission-less innovation idea is getting reborn in the cloud era, not on top of TCP/IP, we take that for granted, but on top of Kubernetes and all of the linked projects. So yeah, that's a big part of it. >> I want to continue on with that idea of permission-less innovation and talk about the culture of open source, particularly because of what you were saying in the keynote about how it's not about the code, it's about the community. And you were using words like empathy and trust, and things that we don't necessarily think of as synonymous with engineers. >> Sam: Isn't it? >> So, can you just talk a little bit about how you've seen the culture change, particularly since your days at Microsoft, and now being at Google, in terms of how people are working together? >> Absolutely, so the first thing is why did it change? It became an economic imperative. Let's look at software industry competition back in the 90s. In general, the biggest got the mostest. If you could assemble the largest number of very intelligent engineers, and put them all on the same project, you would overwhelm your competition. So we saw that play out again and again. Then this new form of collaboration came around, not just birthed by Linux, but also Apache and a number of other things, where it's like oh, we don't have to work for the same company in order to collaborate. And all of a sudden we started seeing those masses grow as big as the number of engineers who went a single company. Ten thousand people, ten thousand engineers, share the copyright to the Linux kernel. At no point have they worked at the same company. At no point could a company have afforded to get all of them together. So this economic imperative that marks what I think of as the first half of the thirty years of open source that we've been in. The second half has been more us all waking up, and realizing open source has got to be inclusive. A diverse world needs diverse solutions built by diverse people. How do we increase our empathy? How do we increase our understanding so that we can collaborate? Because if we think each other is a jerk, if we get turned off of building our great ideas into software because some community member has said something that's just fundamentally not cool, or deeply hurtful, we are human beings and we do take our toys away, and say I'm not going to be there. >> That's the crux of it too. >> It's absolutely a cutthroat industry, but I think one of the things I'm seeing, I've been in Silicon Valley for 22 years, less three years for a stint at Microsoft, I've actually started to see the community become more self-reflective and like, if we can have cutthroat competition in corporations, we don't have to make that personal. 'Cause every likelihood of open source projects is you're employed as a professional engineer at a company, and that employment agreement might change. Especially in containers, right? Great container developers you'll see they move from one company to another, whether it's a giant company like Google, or whether it's a big startup like Docker, or any range of companies. Or Red Hat. So, this sort of general sense that there is a community is starting to help us make better open source, and you can't be effective in a community if you don't have empathy and you don't start focusing on understanding code of conduct community norms. >> Sam, I'm curious how you look at this spectrum of with this complexity out there, how much will your average customer, and you can segment it anywhere you want, but they say, okay I'm going to engage with this, do open source, get involved, and what spectrum of customers are going to be like, well, let me just run it on Google because you've got a great platform, I'm not going to have Google engineers and you guys have lots of smart people that can do that in any of the platform. How do you see that spectrum of customer, is it by what their business IT needs are, is it the size of the customer, is there a decision tree that you guys have worked out yet to try to help end users with what do they own, what do they outsource? It's in clouds more than outsourcing these days. The deal of outsourcing was your mess for less, and this should be somewhat more transformational and hopefully more business value, right? >> Yeah, Urs Hölzle, who's our SVP of Technical Infrastructure, says, the cloud is not a co-location facility. It is different, it is not your server that you shipped up and you know, ran. It's an integrated set of services that should make it incredibly easy to do computing. And we have tons of very intelligent women and men operating our cloud. We think about things like how do you balance velocity and reliability? We have a discipline called site reliability engineering. We've published a book on it, a community is growing up around that, it's sort of the mainstream version of dev ops. So there are a bunch of components that any company at any size can adopt, as long as you need both velocity and reliability. This has always been the tyranny of the or. If I can move fast I can break things, but even Mark Zuckerberg recently said you know, move fast and break fewer things. Kind of a shift, 'cause you don't want to break a lot of people's experience. How do you do that, while making sure that you have high reliability? It really defies simple classification. We have seen companies from startups to mom and pop shops, all the way to giant enterprises adopting cloud, adopting Google cloud platform. One of the big draws is of course, data analytics. Google is a deeply data intensive business, and we've taken that to eleven basically with machine learning, which is why it was so important to explain tense or flow, offer that as open source, and be able to move AI forward. Any company, at any size that wants to do high speed, high scale data analytics, is coming to GCP. We've seen it basically break down into, what's the business value, how close is it to the decision maker, and how motivated is an engineer to learn something different and give cloud a try. >> Because the engineer has to get better at working with the data, understanding the data, and deriving the right insights from the data. >> You're exactly right. Engineers are people, and people need to learn, and they need to be motivated to change. >> Sam, last question I have for you is, you've been involved in many different projects. We look at from the outside and say, okay, how much should be company driven, how much does a foundation get involved? We've seen certain foundations that have done very well, and others that have struggled. It's very interesting to watch Google. We'd give you good as we've talked on the Cube so far. Kubernetes seems to be going well. Great adoption. Google participates, but not too much, and Red Hat I think would agree with that. So congratulations on that piece. >> Sam: Thank you. >> What's your learnings that you've had as you've been involved in some of these various initiatives, couple foundations. We interviewed you when you were back at the Cloud Foundry, and things like that, so, what have you learned that you might want to say, hey, here's some guidelines. >> Yeah, so I think the first guideline is the core of a foundation is, the core purpose of a foundation is bootstrapping trust. So where trust is missing, then you will need that in order to create better contribution and higher velocity in the project. If there's trust there, if there's a benevolent dictator and everyone says that person's fine or that company's fine, then you won't necessarily need a foundation. You've seen a lot of changes in open source startups, dot coms that are also a dot org, shifting to models where you say well, this thing is actually so big it needs to not be owned by any one company. And therefore, to get the next level of contribution, we need to be able to bring in giant companies, then we create trust at that next level. So foundations are really there for trust. It's really important to be strong enough to get something off the ground, and this is the challenge we had at Cloud Foundry, it was a VMware project and then a Pivotal project, and many people believe this is great open source, but it's not an open community, but the technology had to keep working really well. So we how do we have a majority contributor, and start opening up, in a thoughtful process and bringing people in, until you can say what our target is to have the main contributor be less than 50% of the code commits. 'Cause then the majority is really coming from the community. Other projects that have been around for longer, maybe they started out with no majority. Those organizations, those projects tend to be self-organizing, and what they need is just a foundation to build a place that people can contribute money to, so the community can have events. So there's two very different types of organizations. One's almost like a charity, to say I really care about this popular open source project, and I want to be able to give something back, and others are more like a trade association, which is like, we need to enable very complex coordination between big companies that have a lot at stake, in which case you'll create a different class of foundation. >> Great, well Sam Ramji, thank you so much for being with us here on the Cube. I'm Rebecca Knight, and for your host Stu Miniman, please join us back in a bit. (futuristic tone)

>> Narrator: From Austin, Texas, it's theCUBE. Covering DockerCon 2017. Brought to you by Docker and support from it's ecosystem partners. >> Hi, I'm Stu Miniman joining with my co-host Jim Kobielus. We're here with theCUBE at DockerCon 2017. When I talked to John Furrier, he said Stu, at DockerCon, we're going to get Solomon Hykes, the founder. We're going to get Ben Golub, the CEO. And we're also, of course, going to get Marianna Tessle, who is the EVP of Strategic Development. Marianna, thank you for having us back again, we've been having a great event. How is everything with you? >> Thank you first of all, it's great. This is the second day of DockerCon. I think we had a great set of announcement yesterday, and an amazing set of announcement today as well. It's really going great. You know I have been roaming the exhibit hall, and actually a couple of people said this is one of the best shows they have been part of, and this very engaged audience is great to hear. >> From the keynote yesterday, the word that stuck out to me is really scaling. We talk about scaling employment, scaling the ecosystem, and the show itself. I was at that first DockerCon when we were wedged into that hotel room, as Ben joked. We had 100 more people than we told the Fire Marshall. Because it was tight. TheCUBE is usually a little bit smaller footprint than we have at some other shows. But, Austin, first of all, you pick great locations. I mean, San Francisco, Seattle, here. I'm looking forward to... Have we announced yet where next year's is? >> I don't think we've announced it yet. Usually it happens in the afternoon. >> Here in Austin. Talk to us a little about some of those announcements and stuff that you're excited about with growing the ecosystem. >> You know, I'm going to continue the theme you started with scale, and obviously like you said, a lot of things are changing, and scaling. One of the things we have noticed more and more are companies and enterprises have really started to use us more in scale and more in production, more apps, more of that going on. One of the trends we've noticed that actually Ben covered on stage today is that there's not just the leading edge of development and all new apps, web apps, but actually, we are starting to see more of traditional apps coming on board as well. More traditions Ops saying, I want those benefits as well. I do not want to go all the way to the extreme of re-writing my code, and going to microservices. But I can reap a lot of the benefits from Docker rising and putting our tools on top. So we're actually seeing more and more of that. And more and more companies. >> The discussion with Solomon, we talked this morning. He said, Oh, I don't know what Lego set we are. And I said, You know that green, flat piece that you can build everything on top of, so you can have your spaceset, your castle, and all the pieces there. You want to be a platform that can build. One of the announcements you guys had today, it's the modernized traditional applications. Maybe you can walk us through a little bit what that means, you know that mix of microservices verses traditional apps. How you guys see yourself participating in a customer's journey. >> Right. So, when we call this program, by the way it has a nickname, MTA. It's like you said, what we've seen is customers and users that want to have benefit across the board was if they write new code as they have more traditional apps with traditional stacks. What we came up with is a way for you to move from a more traditional to the new and Dockerizing really quickly. One of the things we also announced today, is a go-to market and a program helping customers to do that. We have great partners we announced today and I'm sure we're going to have even more, whether it's Microsoft, Avalon, HPE, and Cisco. What we're going to basically provide is a way for you to very quickly start seeing the benefits. Taking the traditional app, and within days, like five days, you should be able to get it in a modern state and start seeing the benefits from that. It's something that we're going to encourage customers to do very quickly and see the benefits. In fact, we had a customer today, Noran Trust, who's already been doing that, talking about the benefits they've been seeing from this program. >> Marianna, in terms of developer enablement, that's everything to getting Dockerizing, a universal phenomenon for wrapping legacy systems, for refactoring existing code, for building greenfield applications. What will Docker do to continue to improve the experience of Project Moby as an enabler of your ISV ecosystem? Going forward, how do you see the experience of front-end in front of Moby evolving to enable very simplicity and speed of development? >> First of all, I have to say that one of the magic, or secret sauces of Docker is our user experience, and the way we made technologies sometimes that were already available super accessible and super useful for developers and ops and users. So I would say that's definitely something that we have the DNA to do. And a project in Moby, we see ISV's and companies, and it doesn't have to be a company, it could be like users, a company that can come in and collaborate and really create a new component, or a new project from what we're going to put there, and hopefully others as well is a whole set of these Lego building blocks they can assemble. >> Are there any plans of Dockers to provide task-oriented skins or experiences on Moby for different roles, different developer roles associated with particular projects, you know, task, or wrapping a legacy system is a different task, obviously, from developing a greenfield containerized application. So to an extend, will you evolve the tool to enable more task oriented role specific interfaces? >> I would say as far as Moby, and across the company, we do have this realization that it could be that developers started to use Docker first, but actually Ops, and even like we talked about, traditional IT, it's pretty prevalent. So our thought is really to cater to all of these audiences, kind of understand, have a conversation with them and understand what exactly they need and what would make them more productive. An example of what I mentioned with the MTA program, the Modernized Traditional Apps, that one is targeted more towards an Ops audience. Different things we do, we try to understand our audience and engage with them, and see what's going to make them most productive. Both in terms of tool sets and in terms of how we bring it to them. >> Right, right. >> Marianna, we had the opportunity to have some of the partner keynote speakers on theCUBE, John Gossman on from Microsoft yesterday, we had Mark Cavage on from Oracle, here. There's a lot going on. Maybe give our audience a little flavor as to some of the other partner activity going on that we might have missed if we weren't watching close. >> I think we had the same conversation last year, just explaining how important it is for us that we work well with our ecosystem. It's a big part of our plan and strategy, and again confirmation that customers want to use choice, different things, that we're not alone in the world, and we really want to engage with a vast ecosystem. So you saw from Cloud providers to a more on-prem infrastructure to ISV's to networking providers, storage providers. Like a whole understanding and way to be a full platform, we really need to understand how to integrate and how to engage with that ecosystem, and how to help customers have benefits of the entire thing combined. So we've been really looking at who are the different leaders; Sometimes customers take us there, they're like, hey please partner with this company or that company. Understanding mapping of what is needed, and starting from Cloud, infrastructure, network, storage, management, monitoring, security, all the way to ISV's. I would, since you brought up that fact that Mark was here, Mark from Oracle. I do want to talk about that because I think that is maybe even a bit new and unique. Another thing that we announced today, the fact that we have Oracle, Dockerizing their apps and putting them in Docker store and that is big, and again, to us that is obviously big, but again, big for user. It's a very easy way to get software you really need. And not only that, we announced several weeks ago, a certification program. The nice thing about that, if something is certified in store, you can really use that with a lot of trust. You know it's been tested, it's secure. That we made sure that it followed best practices. We made sure that our support engagement with the publisher. Again, geared toward enterprises that really want to have that confidence of downloading something from the store and just using it. Again, Oracle is kind of groundbreaking in putting their software there, and we're very excited about that and we think there is going to be more to come. We really are looking forward to this being an amazing service for our users who want to really start from components that exist and the components that they can trust and be productive very quickly. >> I'm curious, how do you think of the Docker store in relation to things like the Amazon Marketplace, or you know, many of your other partners have their own piece. There really is no kind of enterprise app store today so what do you guys want to own? How do you integrate with partners as you look at that develop over time? >> For us, Docker Store started as an enabler as we saw more and more need from users to to basically, Hey, I want.. Let's say since I talked about Oracle I want to use a database. I don't want to go and Dockerize it again. If somebody already did it and they're already prepared, they already went through it, why wouldn't I just re-use it? So the fact that you can put things in this building block and then move them around, it actually enables the idea that you can re-use the same component between different users. So basically you have here something you can do once, and many people can benefit. So that's the benefit we see. It started with official images long ago. We saw unbelievable traction for it. Users really love it, it makes them productive very quickly. We wanted to expand it to a wider set of ISV's, a wider set of components, a wider set of apps, and make them available. We, right now, see it as more of an enabler and again it's one of those things, listening to our users, listening to our customers, we saw that that's one of the things that will make them productive really quickly. >> One of the things we saw in abundance at DockerCon this year is customers of Visa, MetLife, and so forth, up on stage, talking about how they are using Docker in their business for actual live applications. In terms of partners, are you focusing on particular vertical industries in terms of partnership with ISV's and VAR's, particular geographies? Give us a sense for where you're going in terms of diversification of geographies and industries, and in terms of your focus on partnerships. >> Yeah, and again different parts of the stack require different kinds of partnerships. Like on the South end of the stack on the infrastructure, we're looking for partners that either provide on-prem or Cloud infrastructure, or they can provide a set of plug-ins that integrate with us and a set of tools that can be used with Docker to complete and enhance the overall experience of users using Docker. So that's kind of one set of partnerships that started from hardware vendors, to different plug-ins. On the North side of it as we look at it, we just talked about the fact that we have... >> Jim: Top of the application, the application services end of the staff is the North, right? >> Exactly, and all the way to the content. What you actually put inside and what you run. >> Data, so forth and so on. >> Exactly. We'll form a set of partnerships there and making sure that those components are available in store, those components are Dockerized, that companies can really use that, and obviously Microsoft is a huge partner for us in the OS and as your others as well. >> The storage vendors, like Veritask and so forth, there is a fair amount of data inside the ecosystem that really you're going to continue to develop a partnership. >> Absolutely, Adera, Quadera, you've seen a lot, and we continue partner and seeing what's needed there. Understanding we are trying to predict where customers are today, where they're going to maybe, what they will need a year or two from now, and be ready for that. >> Marianna, that leads me to my final question. We know where you're going to be in Europe, you won't tell us yet the location of the North American show for next year, but as you look at the ecosystem, how do you see that developing? When we sit down with you a year from now, what do you hope to have as the progress? >> As I look at the exhibit hall, I am hoping that we're going to see a bigger exhibit hall with every single DockerCon. And, not just for fun, but really, it kind of indicates the collaboration we have with the ecosystem. I would like us to be known as a trusted and productive partner for our ecosystem. And a trusted and productive partner for our customer. That kind of knows to work together with all these contingencies to have amazing results. Like you said, we seen customers on stage, we seen the press releases of people say it took me months to get VM going, it takes me seconds to get this now going. So you see the kind of productivity and we would like to enhance it even more and get there faster. >> Absolutely, Marianna, always a pleasure to catch up with you. We've got a few more interviews left, two days of live coverage, for Jim Kobielus, and I'm Stu Miniman. Thanks for watching theCUBE. 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