>> Hi, my name is Andy Clemenko. I'm a Senior Solutions Engineer at StackRox. Thanks for joining us today for my talk on labels, labels, labels. Obviously, you can reach me at all the socials. Before we get started, I like to point you to my GitHub repo, you can go to andyc.info/dc20, and it'll take you to my GitHub page where I've got all of this documentation, socials. Before we get started, I like to point you to my GitHub repo, you can go to andyc.info/dc20, (upbeat music) >> Hi, my name is Andy Clemenko. I'm a Senior Solutions Engineer at StackRox. Thanks for joining us today for my talk on labels, labels, labels. Obviously, you can reach me at all the socials. Before we get started, I like to point you to my GitHub repo, you can go to andyc.info/dc20, and it'll take you to my GitHub page where I've got all of this documentation, I've got the Keynote file there. YAMLs, I've got Dockerfiles, Compose files, all that good stuff. If you want to follow along, great, if not go back and review later, kind of fun. So let me tell you a little bit about myself. I am a former DOD contractor. This is my seventh DockerCon. I've spoken, I had the pleasure to speak at a few of them, one even in Europe. I was even a Docker employee for quite a number of years, providing solutions to the federal government and customers around containers and all things Docker. So I've been doing this a little while. One of the things that I always found interesting was the lack of understanding around labels. So why labels, right? Well, as a former DOD contractor, I had built out a large registry. And the question I constantly got was, where did this image come from? How did you get it? What's in it? Where did it come from? How did it get here? And one of the things we did to kind of alleviate some of those questions was we established a baseline set of labels. Labels really are designed to provide as much metadata around the image as possible. I ask everyone in attendance, when was the last time you pulled an image and had 100% confidence, you knew what was inside it, where it was built, how it was built, when it was built, you probably didn't, right? The last thing we obviously want is a container fire, like our image on the screen. And one kind of interesting way we can kind of prevent that is through the use of labels. We can use labels to address security, address some of the simplicity on how to run these images. So think of it, kind of like self documenting, Think of it also as an audit trail, image provenance, things like that. These are some interesting concepts that we can definitely mandate as we move forward. What is a label, right? Specifically what is the Schema? It's just a key-value. All right? It's any key and pretty much any value. What if we could dump in all kinds of information? What if we could encode things and store it in there? And I've got a fun little demo to show you about that. Let's start off with some of the simple keys, right? Author, date, description, version. Some of the basic information around the image. That would be pretty useful, right? What about specific labels for CI? What about a, where's the version control? Where's the source, right? Whether it's Git, whether it's GitLab, whether it's GitHub, whether it's Gitosis, right? Even SPN, who cares? Where are the source files that built, where's the Docker file that built this image? What's the commit number? That might be interesting in terms of tracking the resulting image to a person or to a commit, hopefully then to a person. How is it built? What if you wanted to play with it and do a git clone of the repo and then build the Docker file on your own? Having a label specifically dedicated on how to build this image might be interesting for development work. Where it was built, and obviously what build number, right? These kind of all, not only talk about continuous integration, CI but also start to talk about security. Specifically what server built it. The version control number, the version number, the commit number, again, how it was built. What's the specific build number? What was that job number in, say, Jenkins or GitLab? What if we could take it a step further? What if we could actually apply policy enforcement in the build pipeline, looking specifically for some of these specific labels? I've got a good example of, in my demo of a policy enforcement. So let's look at some sample labels. Now originally, this idea came out of label-schema.org. And then it was a modified to opencontainers, org.opencontainers.image. There is a link in my GitHub page that links to the full reference. But these are some of the labels that I like to use, just as kind of like a standardization. So obviously, Author's, an email address, so now the image is attributable to a person, that's always kind of good for security and reliability. Where's the source? Where's the version control that has the source, the Docker file and all the assets? How it was built, build number, build server the commit, we talked about, when it was created, a simple description. A fun one I like adding in is the healthZendpoint. Now obviously, the health check directive should be in the Docker file. But if you've got other systems that want to ping your applications, why not declare it and make it queryable? Image version, obviously, that's simple declarative And then a title. And then I've got the two fun ones. Remember, I talked about what if we could encode some fun things? Hypothetically, what if we could encode the Compose file of how to build the stack in the first image itself? And conversely the Kubernetes? Well, actually, you can and I have a demo to show you how to kind of take advantage of that. So how do we create labels? And really creating labels as a function of build time okay? You can't really add labels to an image after the fact. The way you do add labels is either through the Docker file, which I'm a big fan of, because it's declarative. It's in version control. It's kind of irrefutable, especially if you're tracking that commit number in a label. You can extend it from being a static kind of declaration to more a dynamic with build arguments. And I can show you, I'll show you in a little while how you can use a build argument at build time to pass in that variable. And then obviously, if you did it by hand, you could do a docker build--label key equals value. I'm not a big fan of the third one, I love the first one and obviously the second one. Being dynamic we can take advantage of some of the variables coming out of version control. Or I should say, some of the variables coming out of our CI system. And that way, it self documents effectively at build time, which is kind of cool. How do we view labels? Well, there's two major ways to view labels. The first one is obviously a docker pull and docker inspect. You can pull the image locally, you can inspect it, you can obviously, it's going to output as JSON. So you going to use something like JQ to crack it open and look at the individual labels. Another one which I found recently was Skopeo from Red Hat. This allows you to actually query the registry server. So you don't even have to pull the image initially. This can be really useful if you're on a really small development workstation, and you're trying to talk to a Kubernetes cluster and wanting to deploy apps kind of in a very simple manner. Okay? And this was that use case, right? Using Kubernetes, the Kubernetes demo. One of the interesting things about this is that you can base64 encode almost anything, push it in as text into a label and then base64 decode it, and then use it. So in this case, in my demo, I'll show you how we can actually use a kubectl apply piped from the base64 decode from the label itself from skopeo talking to the registry. And what's interesting about this kind of technique is you don't need to store Helm charts. You don't need to learn another language for your declarative automation, right? You don't need all this extra levels of abstraction inherently, if you use it as a label with a kubectl apply, It's just built in. It's kind of like the kiss approach to a certain extent. It does require some encoding when you actually build the image, but to me, it doesn't seem that hard. Okay, let's take a look at a demo. And what I'm going to do for my demo, before we actually get started is here's my repo. Here's a, let me actually go to the actual full repo. So here's the repo, right? And I've got my Jenkins pipeline 'cause I'm using Jenkins for this demo. And in my demo flask, I've got the Docker file. I've got my compose and my Kubernetes YAML. So let's take a look at the Docker file, right? So it's a simple Alpine image. The org statements are the build time arguments that are passed in. Label, so again, I'm using the org.opencontainers.image.blank, for most of them. There's a typo there. Let's see if you can find it, I'll show you it later. My source, build date, build number, commit. Build number and get commit are derived from the Jenkins itself, which is nice. I can just take advantage of existing URLs. I don't have to create anything crazy. And again, I've got my actual Docker build command. Now this is just a label on how to build it. And then here's my simple Python, APK upgrade, remove the package manager, kind of some security stuff, health check getting Python through, okay? Let's take a look at the Jenkins pipeline real quick. So here is my Jenkins pipeline and I have four major stages, four stages, I have built. And here in build, what I do is I actually do the Git clone. And then I do my docker build. From there, I actually tell the Jenkins StackRox plugin. So that's what I'm using for my security scanning. So go ahead and scan, basically, I'm staging it to scan the image. I'm pushing it to Hub, okay? Where I can see the, basically I'm pushing the image up to Hub so such that my StackRox security scanner can go ahead and scan the image. I'm kicking off the scan itself. And then if everything's successful, I'm pushing it to prod. Now what I'm doing is I'm just using the same image with two tags, pre-prod and prod. This is not exactly ideal, in your environment, you probably want to use separate registries and non-prod and a production registry, but for demonstration purposes, I think this is okay. So let's go over to my Jenkins and I've got a deliberate failure. And I'll show you why there's a reason for that. And let's go down. Let's look at my, so I have a StackRox report. Let's look at my report. And it says image required, required image label alert, right? Request that the maintainer, add the required label to the image, so we're missing a label, okay? One of the things we can do is let's flip over, and let's look at Skopeo. Right? I'm going to do this just the easy way. So instead of looking at org.zdocker, opencontainers.image.authors. Okay, see here it says build signature? That was the typo, we didn't actually pass in. So if we go back to our repo, we didn't pass in the the build time argument, we just passed in the word. So let's fix that real quick. That's the Docker file. Let's go ahead and put our dollar sign in their. First day with the fingers you going to love it. And let's go ahead and commit that. Okay? So now that that's committed, we can go back to Jenkins, and we can actually do another build. And there's number 12. And as you can see, I've been playing with this for a little bit today. And while that's running, come on, we can go ahead and look at the Console output. Okay, so there's our image. And again, look at all the build arguments that we're passing into the build statement. So we're passing in the date and the date gets derived on the command line. With the build arguments, there's the base64 encoded of the Compose file. Here's the base64 encoding of the Kubernetes YAML. We do the build. And then let's go down to the bottom layer exists and successful. So here's where we can see no system policy violations profound marking stack regimes security plugin, build step as successful, okay? So we're actually able to do policy enforcement that that image exists, that that label sorry, exists in the image. And again, we can look at the security report and there's no policy violations and no vulnerabilities. So that's pretty good for security, right? We can now enforce and mandate use of certain labels within our images. And let's flip back over to Skopeo, and let's go ahead and look at it. So we're looking at the prod version again. And there's it is in my email address. And that validated that that was valid for that policy. So that's kind of cool. Now, let's take it a step further. What if, let's go ahead and take a look at all of the image, all the labels for a second, let me remove the dash org, make it pretty. Okay? So we have all of our image labels. Again, author's build, commit number, look at the commit number. It was built today build number 12. We saw that right? Delete, build 12. So that's kind of cool dynamic labels. Name, healthz, right? But what we're looking for is we're going to look at the org.zdockerketers label. So let's go look at the label real quick. Okay, well that doesn't really help us because it's encoded but let's base64 dash D, let's decode it. And I need to put the dash r in there 'cause it doesn't like, there we go. So there's my Kubernetes YAML. So why can't we simply kubectl apply dash f? Let's just apply it from standard end. So now we've actually used that label. From the image that we've queried with skopeo, from a remote registry to deploy locally to our Kubernetes cluster. So let's go ahead and look everything's up and running, perfect. So what does that look like, right? So luckily, I'm using traefik for Ingress 'cause I love it. And I've got an object in my Kubernetes YAML called flask.doctor.life. That's my Ingress object for traefik. I can go to flask.docker.life. And I can hit refresh. Obviously, I'm not a very good web designer 'cause the background image in the text. We can go ahead and refresh it a couple times we've got Redis storing a hit counter. We can see that our server name is roundrobing. Okay? That's kind of cool. So let's kind of recap a little bit about my demo environment. So my demo environment, I'm using DigitalOcean, Ubuntu 19.10 Vms. I'm using K3s instead of full Kubernetes either full Rancher, full Open Shift or Docker Enterprise. I think K3s has some really interesting advantages on the development side and it's kind of intended for IoT but it works really well and it deploys super easy. I'm using traefik for Ingress. I love traefik. I may or may not be a traefik ambassador. I'm using Jenkins for CI. And I'm using StackRox for image scanning and policy enforcement. One of the things to think about though, especially in terms of labels is none of this demo stack is required. You can be in any cloud, you can be in CentOs, you can be in any Kubernetes. You can even be in swarm, if you wanted to, or Docker compose. Any Ingress, any CI system, Jenkins, circle, GitLab, it doesn't matter. And pretty much any scanning. One of the things that I think is kind of nice about at least StackRox is that we do a lot more than just image scanning, right? With the policy enforcement things like that. I guess that's kind of a shameless plug. But again, any of this stack is completely replaceable, with any comparative product in that category. So I'd like to, again, point you guys to the andyc.infodc20, that's take you right to the GitHub repo. You can reach out to me at any of the socials @clemenko or andy@stackrox.com. And thank you for attending. I hope you learned something fun about labels. And hopefully you guys can standardize labels in your organization and really kind of take your images and the image provenance to a new level. Thanks for watching. (upbeat music) >> Narrator: Live from Las Vegas It's theCUBE. Covering AWS re:Invent 2019. Brought to you by Amazon Web Services and Intel along with it's ecosystem partners. >> Okay, welcome back everyone theCUBE's live coverage of AWS re:Invent 2019. This is theCUBE's 7th year covering Amazon re:Invent. It's their 8th year of the conference. I want to just shout out to Intel for their sponsorship for these two amazing sets. Without their support we wouldn't be able to bring our mission of great content to you. I'm John Furrier. Stu Miniman. We're here with the chief of AWS, the chief executive officer Andy Jassy. Tech athlete in and of himself three hour Keynotes. Welcome to theCUBE again, great to see you. >> Great to be here, thanks for having me guys. >> Congratulations on a great show a lot of great buzz. >> Andy: Thank you. >> A lot of good stuff. Your Keynote was phenomenal. You get right into it, you giddy up right into it as you say, three hours, thirty announcements. You guys do a lot, but what I liked, the new addition, the last year and this year is the band; house band. They're pretty good. >> Andy: They're good right? >> They hit the queen notes, so that keeps it balanced. So we're going to work on getting a band for theCUBE. >> Awesome. >> So if I have to ask you, what's your walk up song, what would it be? >> There's so many choices, it depends on what kind of mood I'm in. But, uh, maybe Times Like These by the Foo Fighters. >> John: Alright. >> These are unusual times right now. >> Foo Fighters playing at the Amazon Intersect Show. >> Yes they are. >> Good plug Andy. >> Headlining. >> Very clever >> Always getting a good plug in there. >> My very favorite band. Well congratulations on the Intersect you got a lot going on. Intersect is a music festival, I'll get to that in a second But, I think the big news for me is two things, obviously we had a one-on-one exclusive interview and you laid out, essentially what looks like was going to be your Keynote, and it was. Transformation- >> Andy: Thank you for the practice. (Laughter) >> John: I'm glad to practice, use me anytime. >> Yeah. >> And I like to appreciate the comments on Jedi on the record, that was great. But I think the transformation story's a very real one, but the NFL news you guys just announced, to me, was so much fun and relevant. You had the Commissioner of NFL on stage with you talking about a strategic partnership. That is as top down, aggressive goal as you could get to have Rodger Goodell fly to a tech conference to sit with you and then bring his team talk about the deal. >> Well, ya know, we've been partners with the NFL for a while with the Next Gen Stats that they use on all their telecasts and one of the things I really like about Roger is that he's very curious and very interested in technology and the first couple times I spoke with him he asked me so many questions about ways the NFL might be able to use the Cloud and digital transformation to transform their various experiences and he's always said if you have a creative idea or something you think that could change the world for us, just call me he said or text me or email me and I'll call you back within 24 hours. And so, we've spent the better part of the last year talking about a lot of really interesting, strategic ways that they can evolve their experience both for fans, as well as their players and the Player Health and Safety Initiative, it's so important in sports and particularly important with the NFL given the nature of the sport and they've always had a focus on it, but what you can do with computer vision and machine learning algorithms and then building a digital athlete which is really like a digital twin of each athlete so you understand, what does it look like when they're healthy and compare that when it looks like they may not be healthy and be able to simulate all kinds of different combinations of player hits and angles and different plays so that you could try to predict injuries and predict the right equipment you need before there's a problem can be really transformational so we're super excited about it. >> Did you guys come up with the idea or was it a collaboration between them? >> It was really a collaboration. I mean they, look, they are very focused on players safety and health and it's a big deal for their- you know, they have two main constituents the players and fans and they care deeply about the players and it's a-it's a hard problem in a sport like Football, I mean, you watch it. >> Yeah, and I got to say it does point out the use cases of what you guys are promoting heavily at the show here of the SageMaker Studio, which was a big part of your Keynote, where they have all this data. >> Andy: Right. >> And they're data hoarders, they hoard data but the manual process of going through the data was a killer problem. This is consistent with a lot of the enterprises that are out there, they have more data than they even know. So this seems to be a big part of the strategy. How do you get the customers to actually wake up to the fact that they got all this data and how do you tie that together? >> I think in almost every company they know they have a lot of data. And there are always pockets of people who want to do something with it. But, when you're going to make these really big leaps forward; these transformations, the things like Volkswagen is doing where they're reinventing their factories and their manufacturing process or the NFL where they're going to radically transform how they do players uh, health and safety. It starts top down and if the senior leader isn't convicted about wanting to take that leap forward and trying something different and organizing the data differently and organizing the team differently and using machine learning and getting help from us and building algorithms and building some muscle inside the company it just doesn't happen because it's not in the normal machinery of what most companies do. And so it always, almost always, starts top down. Sometimes it can be the Commissioner or CEO sometimes it can be the CIO but it has to be senior level conviction or it doesn't get off the ground. >> And the business model impact has to be real. For NFL, they know concussions, hurting their youth pipe-lining, this is a huge issue for them. This is their business model. >> They lose even more players to lower extremity injuries. And so just the notion of trying to be able to predict injuries and, you know, the impact it can have on rules and the impact it can have on the equipment they use, it's a huge game changer when they look at the next 10 to 20 years. >> Alright, love geeking out on the NFL but Andy, you know- >> No more NFL talk? >> Off camera how about we talk? >> Nobody talks about the Giants being 2 and 10. >> Stu: We're both Patriots fans here. >> People bring up the undefeated season. >> So Andy- >> Everybody's a Patriot's fan now. (Laughter) >> It's fascinating to watch uh, you and your three hour uh, Keynote, uh Werner in his you know, architectural discussion, really showed how AWS is really extending its reach, you know, it's not just a place. For a few years people have been talking about you know, Cloud is an operational model its not a destination or a location but, I felt it really was laid out is you talked about Breadth and Depth and Werner really talked about you know, Architectural differentiation. People talk about Cloud, but there are very-there are a lot of differences between the vision for where things are going. Help us understand why, I mean, Amazon's vision is still a bit different from what other people talk about where this whole Cloud expansion, journey, put ever what tag or label you want on it but you know, the control plane and the technology that you're building and where you see that going. >> Well I think that, we've talked about this a couple times we have two macro types of customers. We have those that really want to get at the low level building blocks and stitch them together creatively however they see fit to create whatever's in their-in their heads. And then we have the second segment of customers that say look, I'm willing to give up some of that flexibility in exchange for getting 80% of the way there much faster. In an abstraction that's different from those low level building blocks. And both segments of builders we want to serve and serve well and so we've built very significant offerings in both areas. I think when you look at microservices um, you know, some of it has to do with the fact that we have this very strongly held belief born out of several years of Amazon where you know, the first 7 or 8 years of Amazon's consumer business we basically jumbled together all of the parts of our technology in moving really quickly and when we wanted to move quickly where you had to impact multiple internal development teams it was so long because it was this big ball, this big monolithic piece. And we got religion about that in trying to move faster in the consumer business and having to tease those pieces apart. And it really was a lot of impetus behind conceiving AWS where it was these low level, very flexible building blocks that6 don't try and make all the decisions for customers they get to make them themselves. And some of the microservices that you saw Werner talking about just, you know, for instance, what we-what we did with Nitro or even what we did with Firecracker those are very much about us relentlessly working to continue to uh, tease apart the different components. And even things that look like low level building blocks over time, you build more and more features and all of the sudden you realize they have a lot of things that are combined together that you wished weren't that slow you down and so, Nitro was a completely re imagining of our Hypervisor and Virtualization layer to allow us, both to let customers have better performance but also to let us move faster and have a better security story for our customers. >> I got to ask you the question around transformation because I think that all points, all the data points, you got all the references, Goldman Sachs on stage at the Keynote, Cerner, I mean healthcare just is an amazing example because I mean, that's demonstrating real value there there's no excuse. I talked to someone who wouldn't be named last night, in and around the area said, the CIA has a cost bar like this a cost-a budget like this but the demand for mission based apps is going up exponentially, so there's need for the Cloud. And so, you see more and more of that. What is your top down, aggressive goals to fill that solution base because you're also a very transformational thinker; what is your-what is your aggressive top down goals for your organization because you're serving a market with trillions of dollars of spend that's shifting, that's on the table. >> Yeah. >> A lot of competition now sees it too, they're going to go after it. But at the end of the day you have customers that have a demand for things, apps. >> Andy: Yeah. >> And not a lot of budget increase at the same time. This is a huge dynamic. >> Yeah. >> John: What's your goals? >> You know I think that at a high level our top down aggressive goals are that we want every single customer who uses our platform to have an outstanding customer experience. And we want that outstanding customer experience in part is that their operational performance and their security are outstanding, but also that it allows them to build, uh, build projects and initiatives that change their customer experience and allow them to be a sustainable successful business over a long period of time. And then, we also really want to be the technology infrastructure platform under all the applications that people build. And we're realistic, we know that you know, the market segments we address with infrastructure, software, hardware, and data center services globally are trillions of dollars in the long term and it won't only be us, but we have that goal of wanting to serve every application and that requires not just the security operational premise but also a lot of functionality and a lot of capability. We have by far the most amount of capability out there and yet I would tell you, we have 3 to 5 years of items on our roadmap that customers want us to add. And that's just what we know today. >> And Andy, underneath the covers you've been going through some transformation. When we talked a couple of years ago, about how serverless is impacting things I've heard that that's actually, in many ways, glue behind the two pizza teams to work between organizations. Talk about how the internal transformations are happening. How that impacts your discussions with customers that are going through that transformation. >> Well, I mean, there's a lot of- a lot of the technology we build comes from things that we're doing ourselves you know? And that we're learning ourselves. It's kind of how we started thinking about microservices, serverless too, we saw the need, you know, we would have we would build all these functions that when some kind of object came into an object store we would spin up, compute, all those tasks would take like, 3 or 4 hundred milliseconds then we'd spin it back down and yet, we'd have to keep a cluster up in multiple availability zones because we needed that fault tolerance and it was- we just said this is wasteful and, that's part of how we came up with Lambda and you know, when we were thinking about Lambda people understandably said, well if we build Lambda and we build this serverless adventure in computing a lot of people were keeping clusters of instances aren't going to use them anymore it's going to lead to less absolute revenue for us. But we, we have learned this lesson over the last 20 years at Amazon which is, if it's something that's good for customers you're much better off cannibalizing yourself and doing the right thing for customers and being part of shaping something. And I think if you look at the history of technology you always build things and people say well, that's going to cannibalize this and people are going to spend less money, what really ends up happening is they spend less money per unit of compute but it allows them to do so much more that they ultimately, long term, end up being more significant customers. >> I mean, you are like beating the drum all the time. Customers, what they say, we encompass the roadmap, I got that you guys have that playbook down, that's been really successful for you. >> Andy: Yeah. >> Two years ago you told me machine learning was really important to you because your customers told you. What's the next traunch of importance for customers? What's on top of mind now, as you, look at- >> Andy: Yeah. >> This re:Invent kind of coming to a close, Replay's tonight, you had conversations, you're a tech athlete, you're running around, doing speeches, talking to customers. What's that next hill from if it's machine learning today- >> There's so much I mean, (weird background noise) >> It's not a soup question (Laughter) And I think we're still in the very early days of machine learning it's not like most companies have mastered it yet even though they're using it much more then they did in the past. But, you know, I think machine learning for sure I think the Edge for sure, I think that um, we're optimistic about Quantum Computing even though I think it'll be a few years before it's really broadly useful. We're very um, enthusiastic about robotics. I think the amount of functions that are going to be done by these- >> Yeah. >> robotic applications are much more expansive than people realize. It doesn't mean humans won't have jobs, they're just going to work on things that are more value added. We're believers in augmented virtual reality, we're big believers in what's going to happen with Voice. And I'm also uh, I think sometimes people get bored you know, I think you're even bored with machine learning already >> Not yet. >> People get bored with the things you've heard about but, I think just what we've done with the Chips you know, in terms of giving people 40% better price performance in the latest generation of X86 processors. It's pretty unbelievable in the difference in what people are going to be able to do. Or just look at big data I mean, big data, we haven't gotten through big data where people have totally solved it. The amount of data that companies want to store, process, analyze, is exponentially larger than it was a few years ago and it will, I think, exponentially increase again in the next few years. You need different tools and services. >> Well I think we're not bored with machine learning we're excited to get started because we have all this data from the video and you guys got SageMaker. >> Andy: Yeah. >> We call it the stairway to machine learning heaven. >> Andy: Yeah. >> You start with the data, move up, knock- >> You guys are very sophisticated with what you do with technology and machine learning and there's so much I mean, we're just kind of, again, in such early innings. And I think that, it was so- before SageMaker, it was so hard for everyday developers and data scientists to build models but the combination of SageMaker and what's happened with thousands of companies standardizing on it the last two years, plus now SageMaker studio, giant leap forward. >> Well, we hope to use the data to transform our experience with our audience. And we're on Amazon Cloud so we really appreciate that. >> Andy: Yeah. >> And appreciate your support- >> Andy: Yeah, of course. >> John: With Amazon and get that machine learning going a little faster for us, that would be better. >> If you have requests I'm interested, yeah. >> So Andy, you talked about that you've got the customers that are builders and the customers that need simplification. Traditionally when you get into the, you know, the heart of the majority of adoption of something you really need to simplify that environment. But when I think about the successful enterprise of the future, they need to be builders. how'l I normally would've said enterprise want to pay for solutions because they don't have the skill set but, if they're going to succeed in this new economy they need to go through that transformation >> Andy: Yeah. >> That you talk to, so, I mean, are we in just a total new era when we look back will this be different than some of these previous waves? >> It's a really good question Stu, and I don't think there's a simple answer to it. I think that a lot of enterprises in some ways, I think wish that they could just skip the low level building blocks and only operate at that higher level abstraction. That's why people were so excited by things like, SageMaker, or CodeGuru, or Kendra, or Contact Lens, these are all services that allow them to just send us data and then run it on our models and get back the answers. But I think one of the big trends that we see with enterprises is that they are taking more and more of their development in house and they are wanting to operate more and more like startups. I think that they admire what companies like AirBnB and Pintrest and Slack and Robinhood and a whole bunch of those companies, Stripe, have done and so when, you know, I think you go through these phases and eras where there are waves of success at different companies and then others want to follow that success and replicate it. And so, we see more and more enterprises saying we need to take back a lot of that development in house. And as they do that, and as they add more developers those developers in most cases like to deal with the building blocks. And they have a lot of ideas on how they can creatively stich them together. >> Yeah, on that point, I want to just quickly ask you on Amazon versus other Clouds because you made a comment to me in our interview about how hard it is to provide a service to other people. And it's hard to have a service that you're using yourself and turn that around and the most quoted line of my story was, the compression algorithm- there's no compression algorithm for experience. Which to me, is the diseconomies of scale for taking shortcuts. >> Andy: Yeah. And so I think this is a really interesting point, just add some color commentary because I think this is a fundamental difference between AWS and others because you guys have a trajectory over the years of serving, at scale, customers wherever they are, whatever they want to do, now you got microservices. >> Yeah. >> John: It's even more complex. That's hard. >> Yeah. >> John: Talk about that. >> I think there are a few elements to that notion of there's no compression algorithm for experience and I think the first thing to know about AWS which is different is, we just come from a different heritage and a different background. We ran a business for a long time that was our sole business that was a consumer retail business that was very low margin. And so, we had to operate at very large scale given how many people were using us but also, we had to run infrastructure services deep in the stack, compute storage and database, and reliable scalable data centers at very low cost and margins. And so, when you look at our business it actually, today, I mean its, its a higher margin business in our retail business, its a lower margin business in software companies but at real scale, it's a high volume, relatively low margin business. And the way that you have to operate to be successful with those businesses and the things you have to think about and that DNA come from the type of operators we have to be in our consumer retail business. And there's nobody else in our space that does that. So, you know, the way that we think about costs, the way we think about innovation in the data center, um, and I also think the way that we operate services and how long we've been operating services as a company its a very different mindset than operating package software. Then you look at when uh, you think about some of the uh, issues in very large scale Cloud, you can't learn some of those lessons until you get to different elbows of the curve and scale. And so what I was telling you is, its really different to run your own platform for your own users where you get to tell them exactly how its going to be done. But that's not the way the real world works. I mean, we have millions of external customers who use us from every imaginable country and location whenever they want, without any warning, for lots of different use cases, and they have lots of design patterns and we don't get to tell them what to do. And so operating a Cloud like that, at a scale that's several times larger than the next few providers combined is a very different endeavor and a very different operating rigor. >> Well you got to keep raising the bar you guys do a great job, really impressed again. Another tsunami of announcements. In fact, you had to spill the beans earlier with Quantum the day before the event. Tight schedule. I got to ask you about the musical festival because, I think this is a very cool innovation. It's the inaugural Intersect conference. >> Yes. >> John: Which is not part of Replay, >> Yes. >> John: Which is the concert tonight. Its a whole new thing, big music act, you're a big music buff, your daughter's an artist. Why did you do this? What's the purpose? What's your goal? >> Yeah, it's an experiment. I think that what's happened is that re:Invent has gotten so big, we have 65 thousand people here, that to do the party, which we do every year, its like a 35-40 thousand person concert now. Which means you have to have a location that has multiple stages and, you know, we thought about it last year and when we were watching it and we said, we're kind of throwing, like, a 4 hour music festival right now. There's multiple stages, and its quite expensive to set up that set for a party and we said well, maybe we don't have to spend all that money for 4 hours and then rip it apart because actually the rent to keep those locations for another two days is much smaller than the cost of actually building multiple stages and so we thought we would try it this year. We're very passionate about music as a business and I think we-I think our customers feel like we've thrown a pretty good music party the last few years and we thought we would try it at a larger scale as an experiment. And if you look at the economics- >> At the headliners real quick. >> The Foo Fighters are headlining on Saturday night, Anderson Paak and the Free Nationals, Brandi Carlile, Shawn Mullins, um, Willy Porter, its a good set. Friday night its Beck and Kacey Musgraves so it's a really great set of um, about thirty artists and we're hopeful that if we can build a great experience that people will want to attend that we can do it at scale and it might be something that both pays for itself and maybe, helps pay for re:Invent too overtime and you know, I think that we're also thinking about it as not just a music concert and festival the reason we named it Intersect is that we want an intersection of music genres and people and ethnicities and age groups and art and technology all there together and this will be the first year we try it, its an experiment and we're really excited about it. >> Well I'm gone, congratulations on all your success and I want to thank you we've been 7 years here at re:Invent we've been documenting the history. You got two sets now, one set upstairs. So appreciate you. >> theCUBE is part of re:Invent, you know, you guys really are apart of the event and we really appreciate your coming here and I know people appreciate the content you create as well. >> And we just launched CUBE365 on Amazon Marketplace built on AWS so thanks for letting us- >> Very cool >> John: Build on the platform. appreciate it. >> Thanks for having me guys, I appreciate it. >> Andy Jassy the CEO of AWS here inside theCUBE, it's our 7th year covering and documenting the thunderous innovation that Amazon's doing they're really doing amazing work building out the new technologies here in the Cloud computing world. I'm John Furrier, Stu Miniman, be right back with more after this short break. (Outro music)

>>Live from Denver, Colorado. It's the cube covering comm vault. Go 2019 brought to you by Combolt. >>Hey, you welcome back to the cubes coverage of combo go 19. We're in Colorado this year. Lisa Martin with Stu Miniman and we have a couple of gents joining us alumni of the cube. We're gonna have a really spirited conversation. Please welcome Avinash locks from the CEO of Hedvig, one of our alumni and Don Foxer, the VP of storage solutions from combo and Ann Campbell. Oh gee, Dawn, you can say that, right? Yes, yes. So guys, just a little bit of news coming out with combo and Hedvig in the last month or so, you guys announced combo, announced they were acquiring Hedvig. We last had you on the cube of an Asha DockerCon 18 talking about had veg. And here we are, the announcement comes in September. Acquisitions already closed, lots of buzz, lots of excitement. I'll finish. Let's start with you. Why Convolt >>good question. Uh, first of all, thanks for having me. Uh, C, the way I look at it, I believe the enterprises are gravitating towards complete solutions. If you look at, uh, data management and backup Conwell's clearly the leader in that space, I don't have to say it, I think the analysts have all attested to it. We bring in a very complimentary set of tools that I think coupled together could be a complete solution for a large variety of workloads in the modern data center. And hence it makes it a ideal fit. And also the cultures from an engineering perspective, being Hedwig being a small company in Cornwall is also a small company. But you know, definitely big when compared to what we are at. Um, the cultures were more or less aligned in terms of the engineering culture, so to speak. And that makes it, uh, it made it a very natural choice. Do you know, feel comfortable going into a bigger company. So it worked out really well. >>So Don way we've seen the slides given in the keynote, they talked about the two halves of the brain, the storage management and the data management. Talked to us a little bit about of Hedwig plus con vault and how that goes together. Yeah, for sure. I mean, if you start to look at the, I mean, I guess you look at the marketplace today and you can tell that, uh, kind of the, the lines of delineation of what vendor a versus vendor B versus vendor seat is doing is completely blurred, right? And you'll see that with the attachment of secondary storage, you see that with way backup companies and are driving more towards sort of, you know, uh, the app dev space. And we really start to look at where, what Combolt's doing and, and, and I always say when we talked about the acquisition of Hedvig, it's accelerating the vision that we've had on be able to provide a really super scalable backend for where you can land information that Combalt protects, but the really interesting and cool part, but as you start to realize the tool set that it has within it, it also keeps us very relevant for the future, for where it, in the shifts with applications are going. >>Then it gives us a chance to really give that complete solution from giving the storage, taking the information as it's being created and storing it in a compliance form, storing it off to the cloud, maybe re-purposing it, reusing it into the future. So that's how this really starts to come together. You have the index in control and management, the understanding of what Combolt provides the data management and you have all the flexibility and control that the Hedvig platform provides and Miriam together just gives you that much more agility for how you can use that information, that data. I want to understand what being part of Convolt will be different for Hedvig. I think back to, I've been talking to you since the company came out of stealth. We're huge proponents of the learnings that the hyperscalers had. You came from Amazon and Facebook. Bringing that to the enterprise is great, but building something that is highly scalable versus frigging something that has repeatability and scalability through thousands of deployment, like convoluted have are two separate issues. So, you know, we'll, we'll being part of Convolt, how will that impact your business and your group? >>I think the latter is what is going to make it really exciting for us. I think we added a point where the product that we are bringing into the market or we are brought into the market, it's pretty mature and most of the customers would deploy it and use it. They've been extremely happy with the way it performs and the way it has performed over time. And I think with the combo, they have a larger footprint in the enterprise, large channel infrastructure already in place makes it a lot more easy to push the product out there into the market. And uh, we will be given and VR given complete autonomy plus you what it would it is at Viva doing. And obviously, you know, when you go into any other organization that has got to be some cross-pollination, which is also something that we will be pursuing. But these two things I think, uh, make it very exciting times for us. >>Didn't you? You mentioned the word acceleration a few minutes ago. I'm just wondering from your perspective being called on as long as you have, do you and maybe customers and partners see the Hedvig acquisition as? Sanjay was saying something that's trending on Twitter today is the hashtag new comm dolls. Yep. So it's actually interesting. At first when the acquisition was announced, there were some partners that were like, Hmm, okay, I need to think about this a little bit. And then as we kind of went through the talk track and sort of explain some of the power with the head of the platform delivers, there were a number of, there was suddenly aha. Like you could just tell the light bulb went off. I get where this is going. And then you see what we're doing from Convolt metallic as well, right? The the SAS offering. And you see how we're continuing to drive all of the innovation in that core product. I don't know if you want to call it a combo to Datto, but I do think we've entered a new era of what we're delivering back to our customers from a solutions perspective. And it's really exciting because you can talk to a customer about backup and give them the best solution in the world, but we can also start to expand and get a whole heck of a lot more strategic and help be thought leaders and some of these new spaces, >>well, some of the commentary that I was reading about the acquisition from analysts say, Hey, this is a potentially, this is going to give Combalt opened the door for a bigger presence in the surge defined space, a big market. Also elevate comm vault from a Tam perspective. Talk to us about those perspectives. As some of the analysts said, when Sanjay came onboard nine months ago, Hey combo, you really got to expand your market share and get a kick out of just cultivating the large enterprises. How do you see that? >>Yeah, sure. I mean that's the easiest place to point to the secondary storage market place, right? So the secondary to storage marketplace, it's double digits in billions of market share. And that can be anything from things like object storage. It could just be scale-out, NAS. It could be, um, it could be, you know, companies like Cohesity and others that have a platform that build out secondary storage is a whole slew of people that play in that space. Uh, it also goes back to like appliances in a whole form of other storage types that are purpose built. So the secondary storage is a fairly broad sort of brush that people paint. You know, something is not running production workloads. But the interesting thing is, and this is kind of something that when the we've talked about we see those lines of private production or primary, secondary, tertiary, that's starting to really blur out. >>Um, so that market share that is in secondary storage, that market share that attaches also to object for where your, where we're going from a even a scale out backup perspective. You know, those are I going to be target areas that we can start to give customers solutions into in a really integrated and complete way. Uh, one of the customer areas that I've heard from Convolt that I'm curious if it might be applicable for your, for your team of an option is the service providers, you know, they've sold and you talk about how many end users actually leverage Convolt technology. It's like almost an order of magnitude more when you go through the service providers and when you talk about scalability and the requirements that that seems to be like it could be a fit for a. >>Yeah, you could even think of someone who is running a private cloud in their own on premise data center as being a service provider for their own internal consumption. Grateful folks working in tunnel. I guess going to an MSB or even do a larger service providers is an extrapolation of the same thing. So it'll obviously make it a very natural fit because you know, everyone understands the cap X game. Operational efficiency is the harder problem to actually crack. And with systems like this you can actually address that very simplistically. And it also allows them to kind of scale with their growth in a very effortless fashion. So it makes an agile mix a lot of natural sense. >>And that's an interesting point cause that aligns well too with the way the Combalt software themselves also attack attaches, right? We do a much better job of running that value back to the larger enterprise or those that are seeing more of that operational efficiency challenge. So it's another reason why this is a great intersection or you know, great, great marriage of the two technologies, um, what want to speak with, I think we talked about Sanjay about he of being at puppet worked a lot with dev ops in that environment. I heard from Convolt COO that five of the 45 developers that are here doing whiteboard session come from Hedvig. So speak a little bit of that, that customer base, the developer community microservices, you know, that kind of modern >>I think we have a, a demo session. I don't know what time, but we're going to give you a comprehensive overview of how, uh, you know, kind of Kubernetes orchestrated containers works with Hedwig. I think if people are here, they're hearing me, they should definitely check it out. And, uh, if you look at some of our larger customers, they deploy us in environments where they want to have practically zero touch provisioning capability, right? Which means that you got your infrastructure ought to be completely programmable, which bitches, what the DevOps movement is all about. And uh, the comprehensive set of APIs that be exposed for control and data plane, it actually makes that pipe dream a reality. >>Let's talk a little bit about the integrations. I mentioned a minute ago. The announcement was in September, the acquisitions close and you guys have already really started to buckle down into the integration between the technologies. Can you talk to us about that? And then I'd love to get your perspective on existing had big customers, you know, what door does this open for them? >>So for an existing customers, they are very happy because they now are convinced that we have a larger footprint and we have a lot more people to help to help support them as they grow and they don't have this field anymore as to how perhaps a small startup would be able to support them. So that fear factor goes away. So they're all very relieved on that front. Second, from an integration perspective, uh, there's a lot of things that we are working on from a technological perspective that is getting deep into the roadmap. I dunno if he can talk much about it at this point, but a non-technology we're all well integrated in, we are all Commonwealth employees now Gunwale badges come while emails so well integrated at this point. >>I guess maybe from a high level perspective, what we probably can say is probably number one, we want to make sure the experiences across both products are merged. So it truly views as you know, one one true company vault and providing that experience. And that's everything from installation to support to how we communicate and manage the, the ongoing relationship with the customer. So that's one there's always work to do there. Right? And the next core piece is just how we can make the two technologies basically make, you know, the had big platform, a part of the combo data platform and make sure those two integrations are as tight as possible. And that will be a longterm path, right? Because as that becomes more integrated, there's going to be new ideas, new innovations, and she's gonna come up with a whole lot of new things that we could potentially do that will meet the needs for the customer. And I think the third piece that ties back into the dev ops conversation is we've got two really solid API stacks. So bringing those together is going to be important in the future as well. So that it really is a crisp and clean sort of programmable infrastructure for customers from how the storage is delivered all the way to how it's managed and potentially even deleted out the back end. >>Well, with how quickly we're seeing Convolt move in the last nine months, I mean this year there's so much innovation from leadership changes in sales and marketing, new GTM routes, et cetera. What can, what can combo customers expect in terms of, I know you can't divulge too much on the roadmap, but you know with faster, shorter cycles of development. >>So I'll go first. I mean I think as you look at the sort of sort of where maybe the easiest way to answers is we're staying in front of where the market is heading and we're making sure we're providing solutions that can get customers to solve those challenges when they hit them. We don't want them to have to hit those challenges, have to then struggle, fight, figure out what it is they can do while everyone in their market moves past them. We want to be there with a solution that answers some of those challenges that day. They hit them more preemptive, preemptive, absolutely more preempted to react. That's a perfect way to put it. Thank you. So that's part of what they can expect from us and we do a lot of research and working with our customers and understanding where their future needs are, where they're going. We spend a lot of time with industry experts and analysts too for what they're seeing across the globe. Obviously we can only go so far and travel and talk to so many people. So we leveraged the collective of the industry to also kind of have a pretty good gauge and I'll say we've got leaders like Amash and Sanjay that are also awesome at just kind of having a really good pulse on where the industry is going and what we should be doing as a company. >>I'm just getting pickled in so too early for me to answer how that roadmap may Michio or how customers may perceive. But I think, uh, what should be very encouraging is that we bring so many, so much more capabilities. The enterprise has always been in this mindset of procuring things with a single throat to choke and this makes it very easy for them. >>What's the question of done for you is some of the things that Sue and I have talked about with guests today is from a partner perspective, there's been a lot of positive feedback in Navarro community we talked with and think we're talking with Rick de Blasio tomorrow. Want to understand, you know, some of the new partner programs, how are his Convolt traditional channel, your VARs, but also all the way up to your. Their reaction to all of the changes and the acceleration that Cohmad is driving is particularly with respect to head veg. >>For the most part it's been incredibly positive and even though the technology partner side, it's, it's fairly positive and also it forces us to have a much closer conversation on. All right, let's continue to talk about how we're successful together in the marketplace because we understand that our customers will need more than one vendor, more than two vendors to be successful as they kind of tackle the challenges that are in front of them. So you know, we're not going to stop our innovation and partnering and technology ecosystem development because that's so important to allow the customer to have the choice. We know that we're only one of many players and so we want to give them the choice to use whatever they need. We just want to help them control and manage the data >>and help them maybe simplify their operations. And especially as you know, we don't, we don't go to any event without talking about multi-cloud. It's the world that most businesses are living in. And, and I'll say, if you're not you Willy, how can what combat is doing now, not just with Hedvig but also just with some of the structural changes and directions that you guys have made it help customers embrace multicloud actually be able to protect, recover that data and >>you know, shift, sift insights from it. Yeah, sure. Please. All right, so multi-cloud, so first it starts off in tying in the ecosystems of the different cloud players offer, right? You need to be able to sort the support their platforms. You need to be able to continue to abstract out the information, the data itself that may be tied to an application or tied to a platform and give that level of portability. It's actually something that Hedvig does a fantastic job on as well and when you start to have that level of portability, well then it becomes a heck of a lot easier to either use other platforms within that cloud or a separate cloud or something you might homegrown build yourself as. That's part of the big value prop. We're doing all of these things not to have the best infrastructure but to make it easier for customers to use that data. So that means integrating and being strong partners to cloud players. It means continuing to be a really technological leader in how you can support all the platforms and services they offer and really allow the data to rise to the top as far as the value perspective goes and that's really where we continue to drive our innovation, at least on the on the data management side. >>That's a good Commonwealth perspective. The Hedvig perspective comes from a different angle. We always look at data portability, be it multicloud or even be at hybrid via met a lot of customers who went down the hybrid pot and then had to pull back. And when you pull back, you don't want to be in a situation where you're rewriting your entire application because your data is persisted in a very different way. But providing that data portability with an abstraction that sits between the application and the underlying physical infrastructure, I think is going to be a very important solution to take. You know, view often in this mix and hence together it becomes a comprehensive solution. >>Well guys, we thank you so much for stopping by joining soon and be on the program telling us a little bit more about this exciting new venture that you guys are going in together and we look forward to hearing more about it as it unfolds and maybe getting some customers on the cube next year. Absolutely. All right. Thank you. Thanks for Sumeta, man. I am Lisa Martin. You're watching the cube from convo. Go 19.

(upbeat pop music) >> Announcer: Live from Las Vegas, it's theCUBE covering HPE Discover 2017, brought to you by Hewlett Packard Enterprise. (synthesizer music ticking) >> Okay, welcome back everyone. We're here live in Las Vegas for SiliconANGLE Media. It's theCUBE. This is our coverage of HPE Discover 2017, our seventh year covering HP Discover, now HPE Discover. I'm John Furrier with my Cohost, Dave Vellante. Our next two guests, Paul Miller, Vice President, Software Defined and Cloud Group Marketing at HPE, welcome back to theCUBE, CUBE alumni, Danny Yeo, System Administrator at BYU, Brigham Young University, guys, welcome to theCUBE. Welcome back. >> Thank you. >> Welcome to theCUBE. >> Hey, guys. >> So, tell us-- >> Glad to be here. >> So, tell us, what's your experience in Vegas, so far? What's the take, here, from your perspective on what's happening at the show, your takeaway? >> A lot of exciting technology, with HPE, some things that I wasn't aware what they were doing and I'm very impressed, really impressed. >> John: Like what, what are the things that-- >> One of the things, I was just telling Paul, is their memory driven computing with genomic research. I'm with the College of Life Sciences, specifically, at Brigham Young University and we have people doing research in that area, mapping the human genome, for example. We've got people doing DNA analysis and so forth, so that, that was really fascinating. >> About computing, the Meg Whitman keynote, really, >> Yes. >> redefining compute, it's the vision, >> Yeah. >> and the messaging, hybrid cloud, obviously the center of the action. How does that fit into the portfolio with hyperconverged still on fire? I mean, IT is just getting more automated in a way, but it's more scalable infrastructure. >> Yeah, so we see, you know, our mission in our organization is to drive software defined everything, right, and hyperconverge is all about software defining and making virtualization environments easy and the SimpliVity and the SimpliVity architecture, which is built on rich data services, will enable us to take software defined storage to the next level to make it super, super scalable and extensible and give customers that resilience that they need, the inline dedupe, compression, all those great technologies. You'll see us, you know, push really hard in the hyperconverge space. As you say, it's on fire and I can tell you the sales are on fire. The sessions, here, are on fire, standing-room-only for every SimpliVity session, hands-on labs booked beyond capacity with people loving and learning the technology, but we're not stopping there. We're going to take that same technology and embed it in our Synergy offering. So, just think about the ability to compose and recompose highly scalable software defined storage for enterprise applications and enterprise scale and then you'll also see it be a key part of our technology on the new stack, so, a lot of cool things. The sessions are really hot and on fire, as you say. >> So, Paul, if we go back to the 2009 timeframe, it was converged infrastructure, >> Yeah. >> HP, at the time, kind of coined the term and then it, but essentially, it was some compute, some storage and some networking kind of screwed together >> Yeah. >> and, you know, pre-tested and pre-engineered. >> Yeah. >> That's all good, but it's really evolved dramatically and when you think hyperconverged, you think software defined, software defined everything. >> Yeah. >> It's kind of what Synergy was all about, fluid pools of infrastructure, >> Yeah. >> we heard you guys talking about that, last Discover. So, tell us, help us understand SimpliVity and how that fits in that portfolio. >> Okay, so, yeah, so the whole convergence thing was all about static building blocks, right? You built 'em, you deployed 'em, but they were really static. What we're trying to go to is fluid pools of everything. So, think about SimpliVity being a fluid pool of storage other you could compose and recompose for different workloads. And, in our overall portfolio, the biggest advantage we have, like with the Synergy product, is the ability for a customer who has, needs the scalability and resilience of SAN, today, to be able to on the time you're deploying an application, compose it for that workload, but now I want software defined because I may need some, a lower cost basis, be able to, at time of deployment, at time of provisioning, deploy it there. So, we see this being a very complimentary strategy, where, now, we have composability from software defined all the way up to the largest SAN type software architectures. >> All right, Danny, let's get into this, sort of your situation. So, can you help us? Paint a picture of what's going on in your shop. You know, what are the challenges that you're having? What are the drivers that are affecting your IT decisions and take us through, sort of, what you're doing with infrastructure. >> Absolutely, so, before we got into hyperconverge, we were essentially like everybody else who had not been exposed to hyperconverge. We have the traditional service stack. You got compute nodes, you got fabric, you got storage nodes and then you got the fabric for them to communicate. And, you know, when you have problems, you get the finger pointing, right? (hosts laughing) And so, that was really frustrating and then, of course, you got a hypervisor and all that put in place in the mix. It was frustrating and supporting that, the outbacks was (object banging) a little bit challenging because, you know, for example, my systems engineer would have to stay, sometimes after hours, after five and he'd start doing things and, you know, patching, upgrading, you name it and sometimes to way after midnight. That was problem. We were trying to minimize that. The other challenge that I had in my shop was backup. We had a backup window, during the weekend, that we cannot meet. At some point in time, the RTO and RPO weren't sufficient and, so, we had to look at a different strategy. Disaster recovery, that was like something unachievable. It's like out there, somewhere, right? >> You can't even meet your backup windows. >> Right, sure. >> Dave: I mean, forget about disaster recovery, right. >> So, summer of 2014, I went to a VMware user conference, stopped by the SimpliVity booth and they asked me if I knew about the technology; I didn't, so they spent some time explaining that to me and after that, they asked me if I just had a little bit more time so that they can do a demo for me, a demonstration. During the demonstration, the engineer basically did a failover from California to either Boston or New York. It was in seconds, 22 seconds if I remember correctly. And then, he says, "Well, that simulated a disaster. "And so, you failover and if the disaster is "now all over and averted, you want to failback, right, "to your primary location, " and he did that, again, in seconds. I was blown away. I was sold. It reminded me of when in 2005, I saw VMotion from VMware. >> Yeah right. >> Yeah (laughs). >> John: Right, everybody went, "Wow." >> Game, game changer isn't it? >> Game change, yes! >> Yeah. >> Right. >> And so, I thought to myself, I need, you know, it was like that movie, I got to get me one of these (laughing). And so, I asked them to come over and visit us on campus, do a deeper dive of the technology and so that way we can ask questions back and forth. They did and then we decided to do a Proof of Concept, so we did that late 2014 and after the Proof of Concept, we were convinced that was the technology to acquire. >> So, you had to make sure it was real? >> Yes, now-- >> You did the proofs, Proof of Concept? >> I have, sorry, go ahead. >> No, please, continue. >> So, I had the unique situation where after I have acquired SimpliVity and was running it in production, a competitor, I'll just put it that way, came in and asked us if we would consider doing a POC with their product. And, we're like, "You know, well, I've already bought this," and they said, "Not a problem, we would like you "to try our product and if our product is superior, "we want to swap out those SimpliVity boxes." So, I thought, well, what do I have to lose? (Paul laughing) So, I had the opportunity to run both hyperconverged technologies, side-by-side. >> Okay. >> As we were thinking how best to really test which one works, which one's superior or if they're essentially the same thing, we had an engineer suggest, "Why don't we simulate a drive failure, "start pulling out drives?" And so, we did, we started pulling out drives and I had three nodes on, with SimpliVity and on the other I had four nodes and a box. As we pulled out the, after we pulled out the sixth drive, the other technology failed. We couldn't recover data, basically. We would have to send it to a data recovery center. SimpliVity was just, you know, it was business as usual. It was going, no sweat. >> Dave: Because you had it replicated? Is that right, or-- >> Not yet. We haven't had it replicated, >> Oh, okay. >> but it was an evaluation. >> Dave: Just all synchronous, that's what happened. >> So, it's their technology, right, it's the RAIN and RAIN architecture. >> Yeah. >> and, that's the thing, the RAIN architecture that protected us, so we were able to pull the sixth drive. It was still continue, it threw up a lot of flags, >> Yeah. >> alerts and we knew that-- >> Redundance with the nodes, redundancy at the node level >> Yes. >> as opposed to just the drive level? >> But, that little experiment basically proved to us that we bought the right thing. It validated our acquisition. >> John: So you did the bake-off. That's awesome, right? >> Yes. >> John: So, what did you say to the other guys when they came back and said, yo, your stuff's not working? >> Well, we asked them first. We asked them first, "Help. "Your box is not responding, help." They threw up their hands in the air. >> It's your fault. (hosts laughing) >> Yeah, here's the answer. >> John: You got finger pointing? >> Here's the answer, >> Come on. >> you'll love this, right, the answer is, "You know, you can't just pull out the drives. "You've got to time 'em. "You know, you can't just, willy nilly, you just yank 'em. "You've got to time them." >> Say that to the tornado that's coming down or the earthquake >> Yeah, yeah, sure. >> that's happening or floods, I mean, you? >> Yeah, how do you time those? >> It's a disaster. >> Yeah, how do you time those, yeah? >> So, we decided, look take your product back. We're happy with SimpliVity. We'll keep it. >> This is a huge issue. I mean, Hurricane Sandy, which happened in New York, >> Oh, yeah. >> that was a game changer for a lot of the folks we talk to on theCUBE. You don't know when this is going to come and, literally, this disaster recovery thing is, has to be part of the plan and that's really the key. Now that you have SimpliVity, now that it's part of HPE, what's your world like now with HPE with the SimpliVity? >> It's too soon to tell. (all laughing) No, really, honestly, but after the keynote yesterday, I'm pretty convinced other SimpliVity has, is in good hands. >> John: Yeah. >> And, only time will tell, right? >> So, I want to just sort of summarize the story 'cause we were throwing in all kinds of buzz, RPO, RTO, so, but basically you had a problem with your backup window. That's where this all started. You weren't meeting >> That's where it started. >> your backup window? >> Yes. >> You really didn't even have a disaster recovery, an adequate disaster recovery plan. >> Danny: Not at all. >> So, RPO is a Recovery Point Objective, essentially a measure of how much data you're going to lose, right, >> Yeah. >> and then RTO is Recovery Time Objective, the time it takes you to get your applications back up and running. >> Right. >> And, of course, nobody wants to lose any data, but there's always some exposure. If you want to spend a billion dollars, maybe you can minimize that to near zero, but, and I presume, you didn't spend a billion dollars on this, >> No (laughs). >> but those are the drivers. So, you essentially solved your backup window problem and, at the same time, >> Right. >> you got disaster recovery out-of-the-box, is that correct? >> Yes, so backup is in seconds, right? It's, you know, to do a backup, takes us only a few seconds, like six seconds and so forth. We bought an additional node, put it in a remote site and replicated to it and now we can failover to that node and run only mission-critical apps and when everything's good in the primary location, we can just failback. >> And, that gives you your disaster recovery. Now, and your RPO, is what? I mean, what's the-- >> Seconds. >> Oh, seconds? >> Seconds, yeah, seconds. >> Okay. >> Yes. >> Your RPO is down to seconds? >> Danny: It is that impressive, yeah. >> Okay, so you're at risk of losing seconds of data, which is not the end of the world, necessarily, in your world. And your RTO is minutes? >> About there. >> Yeah. >> Tens of minutes kind of thing? >> No, no. >> No. >> Minutes? >> Just minutes. >> Minutes. >> Minutes, yeah. >> Under 10 minutes? >> Danny: Under 10 minutes, yes. >> Oh, yes. >> Okay. >> Yeah, we're not as huge as some other data center, in the College of Life Sciences, so, so-- >> Dave: Well, you know, and you're not financial services. >> Right. >> So, now, when you, what has been the reaction from your user base? I mean, do they even know? >> They have no clue. >> They don't know. >> It is completely transparent, too. We are now able to do maintenance work during the work day, business hours. We can upgrade. We can patch. They have no clue that this is all going on in the background, which is great because, now, my systems engineer does not have to work after five, hardly ever. >> Dave: So, is this why you bought the company? >> Absolutely, we looked at 'em all, right, and I mean all of 'em and we did similarly. We brought 'em into our labs, we did failover, we did scalability. and that's another huge advantage of the SimpliVity platform built and designed for scalability, compression, because system utilization is very, very, important. And, you know, SimpliVity had a really great marketing tool that we're continuing: it was their guarantees. Guaranteed 90% capacity savings, guaranteed the failover time, a terabyte of VMs in under three minutes, so we're carrying on those guarantees, but what those guarantees actually did was really highlight the architectural advantages that SimpliVity designed in. They took a different approach, right. A lot of people started at, I'm going to simplify the VM management layer. They said, "No, I'm going to make "the most robust virtualization data services platform "in the world," and that's where we really see the core advantage and, again, we looked at 'em all. We put 'em through paces and nobody came close on scalability, availability, disaster tolerance than SimpliVity. >> Paul, what does this mean for your other customers, now, extending out through your portfolio? Obviously, there's different categories, campus and the different use case, but for the other use cases with the composability vision, how does this fit into the hyperconverging, overall? >> Yeah, so we have multiple customers, now, who are running a hyperconverged and composable in their same shop, where they want to have just virtualization and a simple easy deployment, whether it for robo sites or for different work groups. Drop in SimpliVity, up and running in minutes. There are other use cases where they need the high performance of bare-metal or they want to move into containers on bare-metal and that's where Synergy plays out. We have people like you saw, Dreamworks, using Synergy for rendering. >> Right. >> You need bare-metal, you need the power. They can compose and recompose for different movies that they do, different animations. They really love that. We were talking about a genomics research company we're working with. They're using it for bare-metal as well. HudsonAlpha, they're driving bare-metal, but they also have hyperconvergence where the developer community says, I just need to do a few, build a new couple applications. Log in, self service, get your work done on a few VMs and then, when they're done, then they'll move that research into bare-metal, so a lot of different use cases across the board. >> Right, what I love about that, John, is it's horizontal infrastructure >> Yeah. >> that can support multiple workloads and multiple applications, which is kind of infrastructure nirvana for a pro, you know, a practitioner, right, I mean >> Sure. >> having that single platform that you can throw multiple apps and workloads at is, I mean, we've not had that in the industry before, right? >> Paul: No. >> No. >> No. >> So-- >> And building it on one view makes things easy for our customers to manage across the board, so, yeah, we're seeing, I mean, what's interesting about, I think, where we're heading is not only working with, you know, IT leads, but now, developers are starting to become part of our core customers who we're talking to. >> Now, you guys are really, really checking the boxes on making IT easier and as it shifts to the cloud and hybrid, you know, this is the kind of thing; you want out-of-the-box experiences, literally, here and then recovery, this is a good trend. >> Yeah. >> Paul, thanks so much. I know you guys got >> Yeah. a hard stop and you've got to roll to another appointment. Danny, thanks so much for sharing your story. >> Thank you. >> Yeah. >> Love that story, real practitioner, you know, on the ground, on the front lines, doing the bake-off, SimpliVity story, great, great job, thanks so much for sharing. It's theCUBE with more live coverage from HPE Discover after this short break Stay with us. (upbeat pop music)

>>Live from Las Vegas, Nevada. It's the cube covering EMC world 2015. Brought to you by EMC, Brocade and VCE. >>Okay. Welcome back everyone. We are live here in Las Vegas with the cube at EMC real 2015. I'm John ferry, the founder of Silicon Ang. I'm joined with two special guests. Stella Lowe. Who's the global communications at EMC runs, global communications and Amy Posey, neuro facilitator at peak teams. Welcome to the cube. >>So >>You had a session women of the world. We did it last year, but great cube session last year. Um, so I want to get a couple of quick questions. What's going on with women of the world, what you guys just came from there and you guys were on the panel and then what is a neuro facilitator? And then let's get into it. Let's talk about men and women, how we work together. >>Okay, great. So let's start with women of world. So, um, so last year we talked about the challenges that we face and how we reframe them into opportunities that we had some fantastic panelists, but this year I was really interested in the science behind men and women. So it's clear that we're different and we're all bled for success, but, but we're wired differently. And we kind of knew that already. I know we talked about it before John, but we now have the science behind it. We can look at brain scans and we can see that we, Oh, we have different brain patterns. We think differently, uh, different parts of the brain fire fire up when, in times of motivation and stress and people like Amy here, who've done lots of work into this, have having the stages. It was great to have her on the panel to discuss it. >>I'm going to give you a plug because EMC does all kinds of things with formula one cars, motorcycles, getting the data and understanding the race. But now you're dealing with people. So what is going on? Tell us what's up neuro facilitator and let's >>So a neuro facilitator is maybe the best made up job title in the world that I gave myself. So essentially what I do is I look at information about the brain and I curate the research that's out there. So there's a lot of new technology to actually read and look inside our heads. We all have a brain, but we don't necessarily all know how it works. So there's a lot more research and, and tools to read our brains and take a look inside. So what I do is I take that research and, and work with, um, neuroscientists and neurobiologist at Stanford, Columbia, UCLA, and, and reach out and figure out how do we take that information and make it easier, still attain. And I do it in the scope of leadership at organizations like EMC and other technology companies to figure out how do we work better? What information is out there? You know, soft skills and sort of relationship skills. I've always been sort of squishy, right? So now there's a lot more science and information about our brains that are informing it. The, the data's out there, what I do and what my job is, is to pull the data and figure out how do we make it into practical, useful applications for us at work at home, wherever we are. So that's essentially, I'm doing so you >>Guys discussed and how men and women are different. Actually look at the data. We have to give a lot of qualitative data. I mean, it keeps counselors in business. You know, the grant in the workforce, uh, balance is important, but we have a lot of that data, but what's the numbers. What is your findings? So >>What's interesting is looking at men and women's brains. What's fascinating is that we are more alike than dissimilar in looking at a brain. If you looked at a brain scan, one of a man and woman, you wouldn't be able to tell the difference between the two, but they're now finding and looking at different parts of the brain in different functions. So for instance, men have approximately 6% more gray matter than women. So in terms of the gray matter, that's the thinking brain essentially, and women have more white matter than gray. Matter about 9% more than men. And the white matter is what connects the brain and communicate both front and back and side to side. And so you can make some extrapolation of that information and say, you know, men may focus more on issues, solutions, problems, whereas women sort of think more broadly or wider. >>So, I mean, there are generalities, but a lot of the sciences is fascinating. There's also some interesting science about the hippocampus, which is, um, sort of deep. If this is your brain, it's deep inside the brain and the hippocampus is the memory center. And it's what they're finding is that for women, they tend to store emotional memories more effectively. So happy, sad, fearful those types of emotions get stored more effectively in the hippocampus. Whereas men oftentimes during stress, the hippocampus actually has a challenge in making connections. So that's where, again, some of the, the focus and determination and silo viewed sometimes that men have in situations or problems comes into play. Um, there's one other piece, the anterior cingulate cortex, which is sort of within the brain and that's the brains error detector. And it turns out it's a little bit bigger in women. So women sort of tend to look for, uh, issues CA you know, problems, um, maybe less solution focused, especially under times of stress and, and a lot of this, data's interesting. >>It, it causes you to make some generalities, you know, not everybody is going to operate in that way. Your mileage may theory, but it's, it's good because it helps us inform some of the quirky behavior that we deal with at work and figuring out why, why don't you do that? Why do you do that and installed that women being better or women using more of the brain or less of the brain it's, it's, it's simply about we, we, if all brains away from differently, we both bring different things to the table. And how do you take both of those benefits and bring them forward into a better outcomes? >>Always great to talk about because in the workforce, people are different. And so differences is a term that we use, like, you know, with kids learn differently, some have evolved differently and men and women have had differences. So the data shows that that's clear. Um, I want to share a quote that my wife shared on Facebook. It says mother, um, well, a worried mother does better research than the FBI. So, um, I bring that up, you know, it's instinctual. So a lot of it's also biological and also environmental talk about the dynamics around that, that wiring, because you're wired by your upbringing too, that affects you. And what's the, what's the data show in the biology. >>So it's interesting because the, the key piece is that it's not just the biological brain differences. It's, it's a whole host of factors that leave a footprint on us, in our behavior. So it's our education, it's our, uh, you know, where we, where we grew up, our culture is part of that. It's also gender stereotypes that play a role in how we operate. And I think all of those things leave a footprint on a, an and lead us to different behaviors. And so you can't just say it's the, the, the information that's on our brains. It's a whole host of factors that influence. So my study of looking at how the brains are a little bit different and what the research is coming, it's, it's blended in with research around leadership and things like confidence and motivation in the workplace bias in the workplace. And they're, they're showing very different things. >>So for instance, if you think about confidence, we did an interesting exercise in the event at women of world. And I asked, you know, there's, there's a lot about confidence and confidence is essentially the will or motivation to act. So how many women in the room, uh, would raise the, you know, go up for a job that they were really interested in and fascinated by, but maybe weren't a hundred percent qualified for, like, how many of you have maybe turned down that job or decided not to apply because it wasn't the right time. Like you, you're pretty competent, but not a hundred percent confident in it. And it was funny because the majority, all the women's hands went up in the room. So then I asked him, I flipped the question in the room and I asked the men in the room. I said, okay, if you were only about 50% confident for a job that you were going up for, would you, of course, right. Like, yes, I >>Fabricate some stuff on their resume and you make >>Them look bigger. So, exactly. So what's interesting is testosterone plays a role in confidence and motivation at work. And it turns out men have 10 times the amount of testosterone as women do. So part of that is that aggression, but we both have it, but that, that aggressive factor, that idea to go after something, to be more confident, um, women are behind the curve in that, from the research that I've seen. So it takes more effort to, to, to be able to have the confidence, to go for it and to sort of break down those barriers that exist for women to, to go after those jobs that they want, even if it's not a hundred percent. And so we did a, an exercise in boosting confidence in testosterone called power posing. And Amy Cuddy out of Harvard does a, a whole Ted talk on it, which is fascinating. >>But the idea is that you, you know, you, you put your chest back, you put your hands on your hips and it helps boost your testosterone up to about 20%. And it reduces cortisol, which is a stress hormone. So it's a, it's a quick way. You don't do it in front of people. You do it sort of on the sly or else you kind of, you don't look very nice to others, but you, you boost your confidence doing that. And it's just a small sort of brain hack that you can do to give yourself an upper hand, knowing that knowing the science behind it. So it's a behavior changing type of research that's coming out, which I think is really, >>That's really interesting, but now it translates into leadership and execution in the workforce. So people are different than men and women are different that changes the dynamic around what good is, because if your point about women not asking for that job or having confidence to the field, like I'm not going to go for it, like a man bravado, whatever testosterone that's what mean that that's the benchmark of what drive means. So this came up with Microsoft CEO at the Anita board conferences, which we had a cube there. And, and this is a big issue. So how do HR, how do the managers, how do people recognize the differences and what does the data show, and, and can you share your thoughts on that? >>Yeah, so I think a lot of it comes down to bias and bias is essentially a shortcut that we use in our brains to take less energy. And it's not a bad thing. It's, it's something we all do. And it's conscious and it's unconscious. So bias, I think is a key piece of that. And the research on bias is fascinating. It's very, it's, it's very popular topic these days, because I think being able to do a couple of things, be aware that there are hundreds of biases and they're both conscious and unconscious, uh, acknowledge that it exists, but not legitimize it not make that. Okay. The third piece is to, to counter it and, and being able to counter bias by making sure that people have opportunities. And even though you may have re removed hypothetical barriers explicitly stating that you want people, men, or women to apply for promotions, be this type of leader, not just assume that because there are no barriers that it's okay, but really be explicit in how you give people opportunities and let them know that they're out there. I think that's really key. >>Yeah. That brings up the point around work life balance, because, you know, I have a family of four, four kids it's stressful just in and of itself to have four kids, but then I go to the workforce and the same with women too. So there's also a home dynamic with leadership and biases and roles. Um, what's your take on any data on the how of that shifting persona realities, if you will, um, shapes the data. >>It's interesting because it's, it's something that we even talked about in the session that it's a struggle and, and, um, Bev career from Intel was talking about that. There's a period of time that actually is really tough to keep women in the workforce. And it's that time where you're growing your family, you're growing your career. And oftentimes things sort of struggle. And I, I read something recently around women in STEM careers, over a 10-year period, 42% of women drop out of the workforce in comparison to 17% of men. And so I think there's a lot, a ways to go in terms of being able to set up environments where working life is integrated, because it's, it's not even balanced anymore. It's integration. And how do you set up structures so that people can do that through how they work through how they connect with others. And, and to me, that's a big piece is how do you keep people in the workforce and still contributing in that critical point in time? And, you know, Intel hasn't figured it out. It's a tough challenge, >>Stamina. We're a big fans of women in tech, obviously because we love tech athletes. We'd love to promote people who are rock stars and technology, whether it's developers to leaders. And I also have a daughter, two daughters. And so two questions. One is women in tech, anything you could share that the data can talk to, to either inspire or give some insight and to, for the young women out there that might not have that cultural baggage, that my generation, at least our worse than older than me have from the previous bias. So motivating young daughters out there, and then how you deal with the career advice for existing women. >>So the motivating young women to get into tech, um, Bev shared a really absolutely fascinating statistic that between the ages of 12 and 18, it's incredibly important to have a male support model for young girls to get into STEM careers, that it was absolutely critical for their success. And it's funny because the question came up like, why can't that be a woman too? And what's interesting. And what we find is oftentimes we give men the short shrift when they try and support women, and we don't want to do that. We want to support men supporting women because when that happens, we all win. Um, and so I think that's a big piece of it is starting young and starting with male support as well as female support. So many women who, who cite men as, as he had mental was in that gray, you know, or in their daily life. And it's pretty important that they can feel that they can do that. >>And this goes back down the wiring data that you have the data on how we're were wired. It's okay, guys, to understand that it's not an apples to apples. So to speak, men are from Mars. Women are from beans, whatever that phrase is, but that's really what the data is. >>And being explicit to men to say, we want you to support women instead of having men take a back seat feeling like maybe this isn't my battle to fight. It's, it's really important to then encourage men to speak up to in those, those situations to, to think about sort of women in tech. One of, uh, a really interesting piece of research that I've seen is about team intelligence and what happens on teams and Anita Willy from Carnegie Mellon produced this really fascinating piece of research on the three things that a team needs to be more intelligent. It's not just getting the smartest people in the room with the highest IQ. That's a part of it. You want table stakes, you want to start with smart people, but she found that having women, more women on a team actually improved the team's overall intelligence, the collective intelligence and success of a team. So more women was the first one. The second was there's this ability and women tend to be better at it, but the ability to read someone's thoughts and emotions just by looking at their eyes. So it's called breeding in the mind's eye. So just taking a look and being able to sense behavior, um, and, and what someone's thinking and feeling, and then being able to adjust to that and pivot on that, not just focusing on the task at hand, but the cohesion of a team with that skill made a difference. >>It's like if it's a total team sport, now that's what you're saying in terms of how use sport analogy, but women now you see women's sports is booming. This brings up my, my, your, uh, awesome research that you just did for the folks out there. Stella was leading this information generation study and the diversity of use cases now with tech, which is why we love tech so much. It's not just the geeky programmer, traditional nail role. You mentioned team, you've got UX design. You have, um, real time agile. So you have more of a, whether it's a rowing analogy or whatever sport or music, collaboration, collaboration is key. And there's so many new disciplines. I mean, I'll share data that I have on the cube looking at all the six years and then even women and men, the pattern that's coming up is women love the visualization. It's weird. I don't know if that's just so it's in the data, but like data scientists that render into reporting and visualization, not like just making slides like in the data. Yeah. So, but they're not writing, maybe not Python code. So what do you guys see similar patterns in terms of, uh, information generation, it's sexy to have an iWatch. It's >>Cool. So like a cry from Intel on the panel, she gave a great statistic that actually, uh, it's more it's women that are more likely to make a decision on consumer tech than men. And yet a lot of the focus is about trying to build tech for men, uh, on the, you know, if consumer tech companies want to get this right, they need to start thinking about what are women looking for, uh, because, uh, they're the ones that are out there making these decisions, the majority of those decisions. >>Yeah. I mean, it's an old thing back in the day when I was in co, um, right out of college and doing my first startup was the wife test. Yeah. Everything goes by the wife because you want to have collaborative decision-making and that's kind of been seen as a negative bias or reinforcement bias, but I think what guys mean is like, they want to get their partner involved. Yeah. So how do, how do we change the biases? And you know, where I've talked to a guy who said, the word geek is reinforcing a bias or nerd where like, I use that term all the time, um, with science, is there, I mean, we had the, the lawsuit with Kleiner Perkins around the gender discrimination. She wasn't included. I mean, what's your take on all of this? I mean, how does someone practically take the data and put it into practice? >>I think the big thing is, you know, like I said, acknowledging that it exists, right? It's out there. We've been, I feel like our brains haven't necessarily adapted to the modern workplace and the challenges that we've dealt with because the modern workplace is something that was invented in the 1960s and our brains have evolved over a long time. So being able to handle some of the challenges that we have, especially on how men and women operate differently at the workplace, I think is key, but calling it out and making it okay to acknowledge it, but then counter where it needs to be countered where it's not right. And being explicit and having the conversations I think is the big piece. And that's what struck me with the Kleiner Perkins deal was let's have the conversation it's out there. A lot of times people are reticent to, to have the conversation because it's awkward and I need to be PC. And I'm worried about things. It's the elephant in the room, right. But it actually is. Dialogue is far better than leaving it. >>People are afraid. I mean, guys are afraid. Women are afraid. So it's a negative cycle. If it's not an out in the open, that's what I'm saying. >>And the idea is it's, what can we do collectively better to, to be more positive, to, to frame it more positively, because I think that makes a bigger difference in terms, in terms of talking about, Oh, we're different. How are we the same? How can we work together? What is the, the connection point that you bring, you bring, we all bring different skills and talents to the table. I think it's really taking a look at that and talking about it and calling it out and say, I'm not great at this. You're great at this. Let's, let's work together on what we can do, uh, more effectively, >>Okay. Team sports is great. And the diversity of workforce and tech is an issue. That's awesome. So I'd ask you to kind of a different question for both of you guys. What's the biggest surprise in the data and it could be what reinforced the belief or insight into something new share, uh, a surprise. Um, it could be pleasant or creepy or share it. >>Price to me is intuition. So we always talk about women having intuitions. I've had men say, you know, well, my wife is so intuitive. She kind of, she kinda gets that and I've had that in the workplace as well. And I think the biggest surprise for me was that we can now see, we've now proved the intuition. Intuition is a thing that women have, and it's about this kind of web thinking and connecting the dots. Yeah. So we sort of store these memories deep, deep inside. And then when we see something similar, we then make that connection. We call it intuition, but it's actually something it's a kind of a, you know, super recall if you like, and, and, and replaying that situation. But that I think was the biggest surprise to me, Amy. So I would think that the thing that, that always astonishes me is the workplace environment and how we set up environments sometimes to shoot ourselves in the foot. >>So, so often we'll set up, uh, a competitive environment, whatever it is, let's let's and it's internal competition. Well, it turns out that the way that the brain chemicals work in women is that competition actually froze us into, to stress or threat cycle much more easily than it does to men, but men need it to be able to get to optimal arousal. There's a lot of interesting research from Amy Ernest in, at Yale and, and that piece of how you can manipulate your environment to be more successful together to me is absolutely key. And being able to pull out elements of competition, but also elements of collaboration, you kind of knew it, but the science validates it and you go, this is why we need to make sure there's a balance between the two. So everyone's successful. So to me, that's the aha. I could listen to Amy all day and how we apply it to the workplace. That's the next big step. Yeah. >>Yeah. You guys are awesome. And thanks so much for sharing and I wish we could go long. We're getting the hook here on time, but is there any links and locations websites we can, people can go to to get more information on the studies, the science. So I, a lot of my day curating >>And looking for more research. So peak teams.com/blog is where I do a lot of my writing and suggestions. Um, it's peak teams, P E K T E M s.com. And so I run our blog and kind of put my musings every once in a while up there so that people can see what I'm working on. Um, but they can reach out at any time. And I'm on Twitter at, at peak teams geek. Speaking of geeks, I embraced the geek mentality, right? >>Well, we have, I think geeks comment personally, but, um, final point, I'll give you the last word, Amy, if you could have a magic wand to take the science and change the preferred vision of the future with respect to men and women, you know, working cohesively together, understanding that we're different decoupled in science. Now, what would you want to see for the environment work force, life balance? What would be the magic wand that you would change? >>I think being able to make women more confident by helping reduce bias with everybody. So being more keyed in to those biases that we have in those automatic things we do to shortcut and to be more aware of them and work on them together and not see them as bad, but see them as human. So I think that's my big takeaway is remove, remove more bias. >>Fantastic. Stella Lowe, and Amy Posey here inside the cube. Thanks so much. Congratulations on your great work. Great panel. We'll continue. Of course, we have a special channel on SiliconANGLE's dot TV for women in tech. Go to SiliconANGLE dot DV. We've got a lot of cube alumni. We had another one here today with Amy. Thank you for joining us. This is the cube. We'll be right back day three, bringing it to a close here inside the cube live in Las Vegas. I'm John Forney. We'll be right back after this short break.