>>Hi, everyone. Welcome to the cubes presentation of the eight of us startup showcase open cloud innovations. This is season two episode one of the ongoing series covering the exciting startups from ABC ecosystems today. Uh, episode one, steam is the open source community and open cloud innovations. I'm Sean for your host got two great guests, Webb brown CEO of coop costs and as Thielen, head of business development, coop quest, gentlemen, thanks for coming on the cube for the showcase 80, but startups. >>Thanks for having a Sean. Great to be back, uh, really excited for the discussion we have here. >>I keep alumni from many, many coupons go. You guys are in a hot area right now, monitoring and reducing the Kubernetes spend. Okay. So first of all, we know one thing for sure. Kubernetes is the hottest thing going on because of all the benefits. So take us through you guys. Macro view of this market. Kubernetes is growing, what's going on with the company. What is your company's role? >>Yeah, so we've definitely seen this growth firsthand with our customers in addition to the broader market. Um, you know, and I think we believe that that's really indicative of the value that Kubernetes provides, right? And a lot of that is just faster time to market more scalability, improved agility for developer teams and, you know, there's even more there, but it's a really exciting time for our company and also for the broader cloud native community. Um, so what that means for our company is, you know, we're, we're scaling up quickly to meet our users and support our users, every, you know, metric that our company's grown about four X over the last year, including our team. Um, and the reason that one's the most important is just because, you know, the, the more folks and the larger that our company is, the better that we can support our users and help them monitor and reduce those costs, which ultimately makes Kubernetes easier to use for customers and users out there on the market. >>Okay. So I want to get into why Kubernetes is costing so much. Obviously the growth is there, but before we get there, what is the background? What's the origination story? Where did coop costs come from? Obviously you guys have a great name costs. Qube you guys probably reduced costs and Kubernetes great name, but what's the origination story. How'd you guys get here? What HR you scratching? What problem are you solving? >>So yeah, John, you, you guessed it, uh, you know, oftentimes the, the name is a dead giveaway there where we're cost monitoring cost management solutions for Kubernetes and cloud native. Um, and backstory here is our founding team was at Google before starting the company. Um, we were working on infrastructure monitoring, um, both on internal infrastructure, as well as Google cloud. Um, we had a handful of our teammates join the Kubernetes effort, you know, early days. And, uh, we saw a lot of teams, you know, struggling with the problems we're solving. We were solving internally at Google and we're we're solving today. Um, and to speak to those problems a little bit, uh, you know, you, you, you touched on how just scale alone is making this come to the forefront, right. You know, there's now many billions of dollars being spent on CU, um, that is bringing this issue, uh, to make it a business critical questions that is being asked in lots of organizations. Um, you know, that combined with, you know, the dynamic nature and complexity of Kubernetes, um, makes it really hard to manage, um, you know, costs, uh, when you scale across a very large organization. Um, so teams turned to coop costs today, you know, thousands of them do, uh, to get monitoring in place, you know, including alerts, recurring reports and like dynamic management insights or automation. >>Yeah. I know we talked to CubeCon before Webb and I want to come back to the problem statement because when you have these emerging growth areas that are really relevant and enabling technologies, um, you move to the next point of failure. And so, so you scaling these abstraction layers. Now services are being turned on more and more keeping it as clusters are out there. So I have to ask you, what is the main cost driver problem that's happening in the cube space that you guys are addressing? Is it just sheer volume? Is it different classes of services? Is it like different things are kind of working together, different monitoring tools? Is it not a platform and take us through the, the problem area? What do you guys see this? >>Yeah, the number one problem area is still actually what, uh, the CNCF fin ops survey highlighted earlier this year, um, which is that approximately two thirds of companies still don't have kind of baseline to visibility into spend when they moved to Kubernetes. Um, so, you know, even if you had a really complex, you know, chargeback program in place, when you're building all your applications on BMS, you move to Kubernetes and most teams again, can't answer these really simple questions. Um, so we're able to give them that visibility in real time, so they can start breaking these problems down. Right. They can start to see that, okay, it's these, you know, the deployments are staple sets that are driving our costs or no, it's actually, you know, these workloads that are talking to, you know, S3 buckets and, you know, really driving, you know, egress costs. Um, so it's really about first and foremost, just getting the visibility, getting the eyes and ears. We're able to give that to teams in real time at the largest scale Kubernetes clusters in the world. Um, and again, most teams, when they first start working with us, don't have that visibility, not having that visibility can have a whole bunch of downstream impacts, um, including kind of not getting, you know, costs right. You know, performance, right. Et cetera. >>Well, let's get into that downstream benefit, uh, um, problems and or situations. But the first question I have just throw naysayer comment at you would be like, oh, wait, I have all this cost monitoring stuff already. What's different about Kubernetes. Why what's what's the problem I can are my other tool is going to work for me. How do you answer that one? >>Yeah. So, you know, I think first and foremost containers are very dynamic right there. They're often complex, often transient and consume variable cluster resources. And so as much as this enables teams to contract construct powerful solutions, um, the associated costs and actually tracking those, those different variables can be really difficult. And so that's why we see why a solution like food costs. That's purpose built for developers using Kubernetes is really necessary because some of those older, you know, traditional cloud cost optimization tools are just not as fit for, for this space specifically. >>Yeah. I think that's exactly right, Alex. And I would add to that just the way that software is being architected deployed and managed is fundamentally changing with Kubernetes, right? It is deeply impacting every part of scifi software delivery process. And through that, you know, decisions are getting made and, you know, engineers are ultimately being empowered, um, to make more, you know, costs impacting decisions. Um, and so we've seen, you know, organizations that get real time kind of built for Kubernetes are built for cloud native, um, benefit from that massively throughout their, their culture, um, you know, cost performance, et cetera. >>Uh, well, can you just give a quick example because I think that's a great point. The architectures are shifting, they're changing there's new things coming in, so it's not like you can use an old tool and just retrofit it. That's sometimes that's awkward. What specific things you see changing with Kubernetes that's that environments are leveraging that's good. >>Yeah. Yeah. Um, one would be all these Kubernetes primitives are concepts that didn't exist before. Right. So, um, you know, I'm not, you know, managing just a generic workload, I'm managing a staple set and, or, you know, three replica sets. Right. And so having a language that is very much tailored towards all of these Kubernetes concepts and abstractions, et cetera. Um, but then secondly, it was like, you know, we're seeing this very obvious, you know, push towards microservices where, you know, typically again, you're shipping faster, um, you know, teams are making more distributed or decentralized decisions, uh, where there's not one single point where you can kind of gate check everything. Um, and that's a great thing for innovation, right? We can move much faster. Um, but for some teams, um, you know, not using a tool like coop costs, that means sacrificing having a safety net in place, right. >>Or guard rails in place to really help manage and monitor this. And I would just say, lastly, you know, uh, a solution like coop costs because it's built for Kubernetes sits in your infrastructure, um, it can be deployed with a single helmet stall. You don't have to share any data remotely. Um, but because it's listening to your infrastructure, it can give you data in real time. Right. And so we're moving from this world where you can make real time automated decisions or manual decisions as opposed to waiting for a bill, you know, a day, two days or a week later, um, when it may be already too late, you know, to avoid, >>Or he got the extra costs and you know what, he wants that. And he got to fight for a refund. Oh yeah. I threw a switch or wasn't paying attention or human error or code because a lot of automation is going on. So I could see that as a benefit. I gotta, I gotta ask the question on, um, developer uptake, because develop, you mentioned a good point. There that's another key modern dynamic developers are in, in the moment making decisions on security, on policy, um, things to do in the CIC D pipeline. So if I'm a developer, how do I engage with Qube cost? Do I have to, can I just download something? Is it easy? How's the onboarding process for your customers? >>Yeah. Great, great question. Um, so, you know, first and foremost, I think this gets to the roots of our company and the roots of coop costs, which is, you know, born in open-source, everything we do is built on top of open source. Uh, so the answer is, you know, you can go out and install it in minutes. Like, you know, thousands of other teams have, um, it is, you know, the, the recommended route or preferred route on our side is, you know, a helm installed. Um, again, you don't have to share any data remotely. You can truly not lock down, you know, namespace eat grass, for example, on the coop cost namespace. Um, and yeah, and in minutes you'll have this visibility and can start to see, you know, really interesting metrics that, again, most teams, when we started working with them, either didn't have them in place at all, or they had a really rough estimate based on maybe even a coop cost Scruff on a dashboard that they installed. >>How does cube cost provide the visibility across the environment? How do you guys actually make it work? >>Yeah, so we, you know, sit in your infrastructure. Um, we have integrations with, um, for on-prem like custom pricing sheets, uh, with card providers will integrate with your actual billing data, um, so that we can, uh, listen for events in your infrastructure, say like a nude node coming up, or a new pod being scheduled, et cetera. Um, we take that information, join with your billing data, whether it's on-prem or in one of the big three cloud providers. And then again, we can, in real time tell you the cost of, you know, any dimension of your infrastructure, whether it's one of the backing, you know, virtual assets you're using, or one of the application dimensions like a label or annotation namespace, you know, pod container, you name it >>Awesome. Alex, what's your take on the landscape with, with the customers as they look the cost reductions. I mean, everyone loves cost reductions as a, certainly I love the safety net comment that Webb made, but at the end of the day, Kubernetes is not so much a cost driver. It's more of a, I want the modern apps faster. Right? So, so, so people who are buying Kubernetes usually aren't price sensitive, but they also don't want to get gouged either on mistakes. Where is the customer path here around Kubernetes cost management and reduction and a scale? >>Yeah. So I think one thing that we're looking forward to hearing this upcoming year, just like we did last year is continuing to work with the various tools that customers are already using and, you know, meeting those customers where they are. So some examples of that are, you know, working with like CICT tools out there. Like we have a great integration with armoring Spinnaker to help customers actually take the insights from coop costs and deploy those, um, in a more efficient manner. Um, we're also working with a lot of partners, like, you know, for fauna to help customers visualize our data and, you know, integrate with or rancher, which are management platforms for Kubernetes. And all of that I think is just to make cost come more to the forefront of the conversation when folks are using Kubernetes and provide that, that data to customers and all the various tools that they're using across the ecosystem. Um, so I think we really want to surface this and make costs more of a first-class citizen across, you know, the, the ecosystem and then the community partners. >>What's your strategy of the biz dev side. As you guys look at a growing ecosystem with CubeCon CNCF, you mentioned that earlier, um, the community is growing. It's always been growing fast. You know, the number of people entering in are amazing, but now that we start going, you know, the S curves kicking in, um, integration and interoperability and openness is always a key part of company success. What's Qube costs is vision on how you're going to do biz dev going forward. >>Absolutely. So, you know, our products opensource that is deeply important to our company, we're always going to continue to drive innovation on our open source product. Um, as Webb mentioned, you know, we have thousands of teams that are, that are using our product. And most of that is actually on the free, but something that we want to make sure continues to be available for the community and continue to bring that development for the community. And so I think a part of that is making sure that we're working with folks not just on the commercial side, but also those open source, um, types of products, right? So, you know, for Fanta is open source Spinnaker's are open source. I think a lot of the biz dev strategies just sticking to our roots and make sure that we continue to drive it a strong open source presence and product for, for our community of users, keep that >>And a, an open source and commercial and keep it stable. Well, I got to ask you, obviously, the wave is here. I always joke, uh, going back. I remember when the word Kubernetes was just kicked around pre uh, the OpenStack days many, many years ago. It's the luxury of being a old cube guy that I am 11 years doing the cube, um, all fun. But if we remember talking to him in the early days, is that with Kubernetes was, if, if it worked, the, the phrase was rising, tide floats all boats, I would say right now, the tides rising pretty well right now, you guys are in a good spot with the cube costs. Are there areas that you see coming where cost monitoring, um, is going to expand more? Where do you see the Kubernetes? Um, what's the aperture, if you will, of the, of the cost monitoring space at your end that you think you can address. >>Yeah, John, I think you're exactly right. This, uh, tide has risen and it just keeps riding rising, right? Like, um, you know, the, the sheer number of organizations we use C using Kubernetes at massive scale is just mind blowing at this point. Um, you know, what we see is this really natural pattern for teams to start using a solution like coop costs, uh, start with, again, either limited or no visibility, get that visibility in place, and then really develop an action plan from there. And that could again be, you know, different governance solutions like alerts or, you know, management reports or, you know, engineering team reports, et cetera. Um, but it's really about, you know, phase two of taking that information and really starting to do something with it. Right. Um, we, we are seeing and expect to see more teams turn to an increasing amount of, of automation to do that. Um, but ultimately that is, uh, very much after you get this baseline highly accurate, uh, visibility that you feel very comfortable making, potentially critical, very critical related to reliability, performance decisions within your infrastructure. >>Yeah. I think getting it right key, you mentioned baseline. Let me ask you a quick follow-up on that. How fast can companies get there when you say baseline, there's probably levels of baseline. Obviously all environments are different now. Not all one's the same, but what's just anecdotally you see, as that baseline, how fast we will get there, is there a certain minimum viable configuration or architecture? Just take us through your thoughts on that. >>Yeah. Great question. It definitely depends on organizational complexity and, you know, can depend on applicational application complexity as well. But I would say most importantly is, um, you know, the, the array of cost centers, departments, you know, complexity across the org as opposed to, you know, technological. Um, so I would say for, you know, less complex organizations, we've seen it happen in, you know, hours or, you know, a day less, et cetera. Um, because that's, you know, one or two or a smaller engineering games, they can share that visibility really quickly. And, um, you know, they may be familiar with Kubernetes and they just get it right away. Um, for larger organizations, we've seen it take kind of up 90 days where it's really about infusing this kind of into their DNA. When again, there may not have been a visibility or transparency here before. Um, again, I think the, the, the bulk of the time there is really about kind of the cultural element, um, and kind of awareness building, um, and just buy in throughout the organization. >>Awesome. Well, guys got a great product. Congratulations, final question for both of you, it's early days in Kubernetes, even though the tide is rising, keeps rising, more boats are coming in. Harbor is getting bigger, whatever, whatever metaphor you want to use, it's really going great. You guys are seeing customer adoption. We're seeing cloud native. I was told that my friends at dock or the container side is going crazy as well. Everything's going great in cloud native. What's the vision on the innovation? How do you guys continue to push the envelope on value in open source and in the commercial area? What's the vision? >>Yeah, I think there's, there's many areas here and I know Alex will have more to add here. Um, but you know, one area that I know is relevant to his world is just more, really interesting integrations, right? So he mentioned coop costs, insights, powering decisions, and say Spinnaker, right? I think more and more of this tool chain really coming together and really seeing the benefits of all this interoperability. Right. Um, so that I think combined with, uh, just more and more intelligence and automation being deployed again, that's only after the fact that teams are really comfortable with his decisions and the information and the decisions that are being made. Um, but I think that increasingly we see the community again, being ready to leverage this information and really powerful ways. Um, just because, you know, as teams scale, there's just a lot to manage. And so a team, you know, leveraging automation can, you know, supercharge them and in really impactful ways. >>Awesome, great integration integrations, Alex, expand on that. A whole different kind of set of business development integrations. When you have lots of tool chains, lots of platforms and tools kind of coming together, sharing data, working together, automating together. >>Well. Yeah, we, so I think it's going to be super important to keep a pulse on the new tools. Right. Make sure that we're on the forefront of what customers are using and just continuing to meet them where they are. And a lot of that honestly, is working with AWS too, right? Like they have great services and EKS and managed Prometheus's. Um, so we want to make sure that we continue to work with that team and support their services as that launched as well. >>Great stuff. I got a couple of minutes left. I felt I'll throw one more question in there since I got two great experts here. Um, just, you know, a little bit change of pace, more of an industry question. That's really no wrong answer, but I'd love to get your reaction to, um, the SAS conversation cloud has changed what used to be SAS. SAS was, oh yeah. Software as a service. Now that you have all these kinds of new kinds of you have automation, horizontally, scalable cloud and edge, you now have vertical machine learning. Data-driven insights. A lot of things in the stack are changing. So the question is what's the new SAS look like it's the same as the old SAS? Or is it a new kind of refactoring of what SAS is? What's your take on this? >>Yeah. Um, there's a web, please jump in here wherever. But in, in my view, um, it's a spectrum, right? There's there's customers that are on both ends of this. Some customers just want a fully hosted, fully managed product that wouldn't benefit from the luxury of not having to do any, any sort of infrastructure management or patching or anything like that. And they just want to consume a great product. Um, on the other hand, there's other customers that have more highly regulated industries or security requirements, and they're going to need things to deploy in their environment. Um, right now QP cost is, is self hosted. But I think in the future, we want to make sure that, you know, we, we have versions of our product available for customers across that entire spectrum. Um, so that, you know, if somebody wants the benefit of just not having to manage anything, they can use a fully self hosted sat or a fully multitenant managed SAS, or, you know, other customers can use a self hosted product. And then there's going to be customers that are in the middle, right, where there's certain components that are okay to be a SAS or hosted elsewhere. But then there's going to be components that are really important to keep in their own environment. So I think, uh, it's really across the board and it's going to depend on customer and customer, but it's important to make sure we have options for all of them. >>Great guys, we have SAS, same as the old SAS. What's the SAS playbook. Now >>I think it is such a deep and interesting question and one that, um, it's going to touch so many aspects of software and on our lives, I predict that we'll continue to see this, um, you know, tension or real trade-off across on the one hand convenience. And now on the other hand, security, privacy and control. Um, and I think, you know, like Alex mentioned, you know, different organizations are going to make different decisions here based on kind of their relative trade-offs. Um, I think it's going to be of epic proportions. I think, you know, we'll look back on this period and just say that, you know, this was one of the foundational questions of how to get this right. We ultimately view it as like, again, we want to offer choice, um, and make, uh, make every choice be great, but let our users, uh, pick the right one, given their profile on those, on those streets. >>I think, I think it's a great comment choice. And also you got now dimensions of implementations, right? Multitenant, custom regulated, secure. I want have all these controls. Um, it's great. No one, no one SaaS rules the world, so to speak. So it's again, great, great dynamic. But ultimately, if you want to leverage the data, is it horizontally addressable? MultiTech and again, this is a whole nother ball game we're watching this closely and you guys are in the middle of it with cube costs, as you guys are creating that baseline for customers. Uh, congratulations. Uh, great to see you where thanks for coming on. Appreciate it. Thank you so much for having us again. Okay. Great. Conservation aiders startup showcase open cloud innovators here. Open source is driving a lot of value as it goes. Commercial, going to the next generation. This is season two episode, one of the AWS startup series with the cube. Thanks for watching.

>>Good morning from Los Angeles, Lisa Martin here at Qube con cloud native con north America, 2021. This is the cubes third day, a wall-to-wall coverage. So great to be back at an event in person I'm excited to be joined by Vince Wang, senior director of products at 49. We're going to talk security and Kubernetes then welcome to the program. >>Thank you for having me. >>So I always love talking to 40 minutes. Cybersecurity is something that is such an impersonal interest of mine. The fording that talks about the importance of integrating security and compliance and the dev sec ops workflow across the container life cycle. Why is this important and how do you help companies achieve it? >>Well, as companies are making digital innovations, they're trying to move faster and as to move faster, or many companies are shifting towards a cloud native approach, uh, rapid integrations, rapid development, and rapid deployment, uh, but sometimes speed, you know, there's a benefit to that, but there's also the downside of that, where, you know, you can lose track of issues and you can, uh, introduce a human error in a problem. So as part of the, as part of the, the, the means to deliver fast while maintaining his six year approach, where both the company and the organizations delivering it and their end customers, it's important to integrate security throughout the entire life cycle. From the moment you start planning and development, and people's in process to when you're developing it and then deploying and running in production, um, the entire process needs to be secured, monitored, and, um, and vetted regularly with good quality, um, processes, deep visibility, and an integrated approach to the problem. Um, and I think the other thing to also consider is in this day and age with the current situation with COVID, there's a lot of, uh, development of employment in terms of what I call NASA dental Baltic cloud, where you're deploying applications in random places, in places that are unplanned because you need speed and that, uh, diversity of infrastructure and diversity of, uh, of clouds and development and things to consider then, uh, produces a lot of, uh, you know, uh, opportunities for security and, and challenges to come about. >>And we've seen so much change from a security perspective, um, the threat landscape over the last 18 months. So it's absolutely critical that the integration happens shifting left. Talk to us about now let's switch topics. Application teams are adopting CIC D uh, CICB workflows. Why does security need to be at the center of that adoption? >>Well, it goes back to my earlier point where when you're moving fast, your organizations are doing, um, you're building, deploying, running continuously and monitoring, and then improving, right? So the idea is you're, you're creating smaller, incremental changes, throwing it to the cloud, running it, adjusting it. So then you're, you're rapidly integrating and you're rapidly developing and delivery. And again, it comes down to that, that rapid nature, uh, things can happen. There's, there's more, uh, more points of touching and there's more points of interactions. And, you know, and again, when you're moving that fast, it's really easy to, um, miss things along the way. So as you have security as a core fundamental element of that DNA, as you're building it, uh, that that's in parallel with everything you're doing, you just make sure that, um, when you do deliver something that is the most secure application possible, you're not exposing your customers or your organizations to unforeseen risks that just kind of sits there. >>Uh, and I think part of that is if you think about cloud infrastructure, misconfiguration is still number one, uh, biggest problem with, uh, with security on the, in the cloud space, there's, uh, tasks and vulnerabilities those, we all know, and there's there's means to control that, but the configurations, when you're storing the data, the registries, all these different considerations that go into a cloud environment, those are the things that organizations need visibility on. And, um, the ability to, to adopt their processes, to be proactive in those things and know what they, uh, do. They just need to know what, what then, where are they're operating in, um, to kind of make these informed decisions. >>That visibility is key. When you're talking with customers in any industry, what are the top three, let's say recommendations to say, here's how you can reduce your exposure to security vulnerabilities in the CIS CD pipeline. What are some of the things that you recommend there to reduce the risk? >>There's a couple, oh, obviously security as a fundamental practice. We've been talking about that. So that's number one, key number. The second thing that I would say would be, uh, when you're adopting solutions, you need to consider the fact that there is a very much of a heterogeneous environment in today's, uh, ecosystem, lots of different clouds, lots of different tools. So integration is key. The ability to, um, have choices of deployment, uh, in terms of where you wanted to play. You don't want to deploy based upon the technology limitations. You want to deploy and operate your business to meet your business needs and having the right of integrations and toolings to, uh, have that flexibility. Now, option is key. And I think the third thing is once you have security, the choices, then you can treat, you create a situation where there's a lot of, uh, you know, process overhead and operational overhead, and you need a platform, a singular cybersecurity platform to kind of bring it all in that can work across multiple technologies and environments, and still be able to control at the visibility and consolidate, uh, policies and nationally consistent across all closet points. >>So we're to the DevOps folks, what are some of the key considerations that they need to take into >>Account to ensure that their container strategy isn't compromising security? Well, I think it comes down to having to think outside of just dev ops, right? You have to, we talk about CIC D you have to think beyond just the build process beyond just where things live. You have to think continuous life cycles and using a cyber security platform that brings it together, such as we have the Fortinet security fabric that does that tying a lot of different integration solutions. We work well within their core, but theirs have the ability to integrate well into various environments that provide that consistent policies. And I think that's the other thing is it's not just about integration. It's about creating that consistency across class. And the reality is also for, I think today's dev ops, many organizations are in transition it's, you know, as, as much as we all think and want to kind of get to that cloud native point in time, the reality is there's a lot of legacy things. >>And so dev ops set ups, the DevSecOps, all these different kind of operational functions need to consider the fact that everything is in transition. There are legacy applications, they are new cloud native top first type of application delivery is using containers of various technologies. And there needs to be a, again, that singular tool, the ability to tie this all together as a single pane of glass, to be able to then navigate emerge between legacy deployments and applications with the new way of doing things and the future of doing things with cloud native, uh, and it comes down again to, to something like the Fortinet security fabric, where we're tying things together, having solutions that can deploy on any cloud, securing any application on any cloud while bringing together that consistency, that visibility and the single point management, um, and to kind of lower that operational overhead and introduce security as part of the entire life cycle. >>Do you have a Vincent example of a customer that 49 has worked with that has done this, that you think really shows the value of what you're able to enable them to achieve? >>We do. We do. We have lots of customers, so can name any one specific customer for various reasons, you know, it's security after all. Um, but the, the most common use cases when customers look at it, that when you, we talked to a CIO, CSO CTO is I think that's a one enter they ask us is, well, how do we, how do we manage in this day and age making these cloud migrations? Everyone? I think the biggest challenge is everyone is in a different point in time in their cloud journey. Um, there's if you talk to a handful of customers or a rueful customers, you're not going to find one single organization that's going to be at the same point in time that matches them yet another person, another organization, in terms of how they're going about their cloud strategies, where they're deploying it at what stage of evolution there are in their organizational transformations. >>Um, and so what they're looking for is that, that that's the ability to deploy and security any application on any topic throughout their entire application life cycle. Um, and so, so the most common things that, that our customers are looking for, um, and, you know, they're doing is they're looking to secure things on the network and then interconnected to the cloud with, uh, to deliver that superior, uh, application experience. So they were deploying something like the security fabric. Uh, again, you know, Fordanet has a cybersecurity approach to that point and securing the native environments. They're looking at dev ops, they're deploying tooling to provide, uh, you know, security posture management, plus a few posture management to look at the things that are doing that, the registries, their environment, the dev environment, to then securing their cloud, uh, networks, uh, like what we do with our FortiGate solutions, where we're deploying things from the dev ops. >>I feel secure in the cloud environment with our FortiGate environments across all the various multitudes of cloud providers, uh, like, uh, AWS Azure, Google cloud, and that time that together with, with some secure, um, interconnections with SD LAN, and then tying that into the liver and productions, um, on the web application side. So it's a very much a continuous life cycle, and we're looking at various things. And again, the other example we have is because of the different places in different, uh, in terms of Tod journeys, that the number one key is the ability to then have that flexibility deployment to integrate well into existing infrastructure and build a roadmap out for, uh, cloud as they evolve. Because when you talk to customers today, um, they're not gonna know where they're going to be tomorrow. They know they need to get there. Uh, they're not sure how they're going to get there. And so what they're doing now is they're getting to cloud as quickly as they can. And then they're looking for flexibility to then kind of adjust and they need a partner like Fordanet to kind of bring that partnership and advisorship to, uh, to those organizations as they make their, their, their strategies clearer and, uh, adjust to new business demands. >>Yeah. That partnership is key there. So afforded it advocates, the importance of taking a platform approach to the application life cycle. Talk to me about what that means, and then give me like the top three considerations that customers need to be considering for this approach. >>Sure. Number one is how flexible is that deployment in terms of, do you, do customers have the option to secure and deploy any application, any cloud, do they have the flexibility of, um, integrating security into their existing toolings and then, uh, changing that out as they need, and then having a partner and a customer solution that kind of grows with that? I think that's the number one. Number two is how well are these, uh, integrations or these flexible options tied together? Um, like what we do with the security fabric, where everything kind of starts with, uh, the idea of a central management console that's, you know, uh, and consistent policies and security, um, from the get-go. And I think the third is, is looking at making sure that the, the, the security integrations, the secure intelligence is done in real time, uh, with a quality source of information, uh, and, and points of, uh, of responsiveness, um, what we do with four guard labs. >>For example, we have swell of large, um, machine learning infrastructure where have supported by all the various customer inputs and great intelligence organizations, but real time intelligence and percussion as part of that deployment life cycle. Again, this kind of really brings it all together, where organizations looking for application security and, and trying to develop in a CSED fashion. And you have the ability to then have security from the get, go hide ident to the existing toolings for flexibility, visibility, and then benefits from security all along the way with real time, you know, uh, you know, leading edge security, that then kind of brings that, that sense of confidence and reassurance as they're developing, they don't need to worry about security. Security should just be part of that. And they just need to worry about solving the customer problems and, uh, and, you know, delivering business outcomes and results. >>That's it, right? It's all about those business outcomes, but delivering that competence is key. Vince, thank you for joining me on the program today, talking through what 49 is doing, how you're helping customers to integrate security and compliance into the dev dev sec ops workflow. We appreciate your insights. >>Thank you so much for your time. I really appreciate it. My >>Pleasure for vents Wang. I'm Lisa Martin. You're watching the cube live from Los Angeles, uh, cube con and cloud native con 21 stick around at Dave Nicholson will join me next with my next guest.

>>From around the globe. It's the cube covering Fortinet security summit brought to you by Fortinet. >>Okay. Welcome back everyone. To the cubes, coverage of Fortinets championship golf tournament, we're here for the cybersecurity summit. David got a great guest, Ruby cutoff CEO, and co-founder of Tufin great to have you on. Thank you for coming on the cube. We were chatting before. Came on. Camera, big talk. You just gave it. Thanks mom. Thanks >>For having me >>Not a bad place here. Golf tournament, golf and cybersecurity, kind of go together. You know, keep the ball in the middle of the fairway. You know, don't let it get out of bounds, you know, >>And it's a beautiful place. So, uh, very happy to be here and be a premier sponsor of the event. >>Congratulations and a good, good to have you on let's get into the cybersecurity. We were talking before we came on camera around how transformation is really hard. We went to the cloud is really hard refactoring. You're just really hard, but security is really, really hard. That's true. So how do you look at how security is perceived in companies? Is there dynamics that are being amplified by the rapid moved movement to the cloud? You seeing apps being developed really fast changes fast. What's the, what's the barometer of the industry right now? Sure, >>Sure. It's interesting. And this hasn't really changed in the past, but we've seen like exacerbated getting worse and worse. I think a lot of companies security is actually seen as a blocker and frankly security is probably the most hated department in the organization because a lot of times, first of all, the security says no, but also they just take their time. So if you think about organizations, enterprises, they run on top of their enterprise applications. They have applications that their own in-house developers are writing, and those developers are changing their apps all the time. They're driving change in it as well. So you end up having dozens of change requests from developers want to open connectivity. You want to go from point a to point B on the network. They open a ticket. It reaches the network security team that ticket might take several days until it's implemented in production. So the level of service that security provides the application teams today is really not very high. So you can really understand why security is not, um, looked upon favorably by the rest of the organization. >>And some organizations. My perception is, is that, you know, the hardcore security teams that have been around for awhile, they've got standards and they're hardcore, a new app comes in, it's gotta be approved. Something's gotta get done. And it's slower, right? It slows people down the perception. It could be slow. How is it changing? Yes, >>So it changing because when you're moving to the cloud and a lot of organizations are adopting the cloud in many ways, private cloud, public cloud hybrid cloud, you know, they're working in cloud native environments and those environments, you know, the developers are, they own the keys to the kingdom, right? They're managing AWS Azure, Google cloud to managing get hub. You know, they got the place to themselves. So they're pushing changes in their apps without asking it for permission. So they're suddenly exposed to this is how fast it can really be. And while anything that they do in the on-prem or sort of traditional applications is still moving very slowly unless they're using an automated approach to policy. So one of the things that I spoke about today is the need for organizations to adopt a policy centric approach. So they need to define a policy of who can talk to whom and what conduct to what across the entire organizational network, whether it's firewalls routers, which is cloud platforms. >>And then once you have that policy, you can start automated based on the policy. So the concept is somebody opens a ticket, a developer wants to make a change. They want a ticket in service. Now remedy that ticket reaches, uh, some system that's going to check for compliance against the policy. If you're able to immediately tell if that change is compliant or not, then you're able to make that split-second decision, which might take an analyst a couple of days, and then you can design the perfect minimal change to implement on the network. That is really agile, right? That's what developers want to see. And a lot of security departments are really struggling with that today. >>Why, why are they? That seems like a no brainer because policy-based innovation has been around in the network layer for many, many years decades. Right? We'll see, makes things go better, faster. Why would they be against it? Where were they? >>Yeah. So they're not really against it. I think it's just the sheer complexity and size of today's networks is nothing compared to where it was 10 years ago. So you have tens to hundreds of firewalls in large enterprises, thousands of routers and switches, load balancers, private cloud SDN, like NSX and ACI public cloud Kubernetes. It's just a plethora of networking. So we're thinking of it as proliferation of networking is getting worse and worse, especially with IOT and now moving to the cloud. So it is just so complex that if you don't have specialized tools, there's absolutely no way they'll, you'll be able to. >>So your talk must so gone over well, because I do a lot of interviews and I hear developers talking about shift left, right? Which is, you know, basically vernacular for do security in the dev CIC D pipelining. So while you're there rather than having to go fix the bugs later, this seems to be a hot trend. People like it, they want it, they want to check it off, get it done, move on this policy-based automation, help them here. >>It does in some ways, I mean, so you need a policy for the cloud as well, but there's a different challenge that I see altogether in the cloud. And one of the challenges that we're saying is that there's actually a political divide. You have network security folks who are managing, you know, firewalls routers, switches, and maybe the hub to the cloud. And then inside the spokes inside the cloud itself, you have a different team, cloud operators, cloud security folks. And those two teams don't really talk to each other. Some companies have set up centers of excellence, where they're trying to bring all the experts together. But most companies, network security, folks who want to understand what's happening inside the cloud are sort of given the Heisman. They're not invited to meetings. Um, and there's lack of which I think is tragic because it's not going to go over well. So there's huge challenges in security in the cloud. And unless these two departments are going to talk to each other and work together, we're not going to get anywhere near the level of security that we need. >>The cloud team, the cloud guys, if you will, you know, quote guys or gals and the security guys and gals, they're not getting along. What's the, what's the, is it historical? Just legacy structures? Is it more of my department? I own the keys to the kingdom. So go through me kind of the vibe, or is it more of just evolution of the, developer's going to say, I'm going to go around you like shadow it, um, created the cloud. Is there like a shadow security, but trend around this? >>Yeah, there is. And I think it stems from what we covered in the beginning, which is, you know, app developers are now used to and trained to fear security. Every change they want on the on-prem network takes a week, right? They're moving to the cloud. Suddenly they're able to roam freely, do things quickly. If network security folks come by and say, oh, we want to take a look at those changes. What they're hearing, the music is all we're going to slow you down. And the last thing cloud guys want to hear is that we're going to slow you down. So they have they're fearfully. You know, they're, they're rightly afraid of what's going to happen. If they enable a very cumbersome and slow process, we got to work differently. Right? So there's new paradigms with dev DevSecOps where security is built into the CIC pipeline, where it doesn't slow down app developers, but enables compliance and visibility into the cloud environments at the same time. Great stuff. >>Great insight. I want to ask you your, one of your things in your top that I found interesting. And I like to have you explain it in more detail is you think security can be an enabler for digital transformation. Digital transformation can kick the wrong yeah. With transforming. Okay. Everyone knows that, but security, how does security become that enabler? >>So, I mean, today security is a, um, as a blocker to digital transformation. I think anybody that claims, Hey, we're on a path to digital transformation. We're automated, we're digitally transformed. And yet you asked the right people and you find out every change takes a week on the network. You're not digitally transformed, right? So if you adopt a, a framework where you're able to make changes in a compliant secure matter and make changes in minutes, instead of days, suddenly you'll be able to provide a level of service to app developers like they're getting in the cloud, that's digital transformation. So I see the network change process as pretty much the last piece of it that has not been digitally transformed yet. >>And this is where a lot of opportunity is. Exactly. All right. So talk about what you guys are doing to solve that problem, because you know, this is a big discussion. Obviously security is on everyone's mind. They're reactive to proactive that buying every tool they can platforms are coming out. You're starting to see a control plane. You're starting to see things like collective intelligence networks forming, uh, what's the solution to all this, >>Right? So what we've developed is a security policy layer that sits on top of all the infrastructure. So we've got, uh, four products in the two for an orchestration suite where we can connect to all the major firewalls, router, switches, cloud platforms, private cloud SDN. So we see the configuration in all those different platforms. We know what's happening on the ground. We build a typology model. That is one of the industry's best apology models that enables us to query and say, okay, from point a to point B, which firewalls, router switches and cloud platforms will you traverse. And then we integrate it with ticketing system, like a remedy or service now, so that the user experiences a developer opens a ticket for a change that ticket gets into Tufin. We check it against the policy that was defined by the security managers, the security manager defined a policy of who can talk to whom and what conducted what across the physical network and the cloud. >>So we can tell within a split second, is this compliant or not? If it's not compliant, we don't waste an engineer's time. We kick it back to the original user. But if it is compliant, we use that typology model to perform network change design. So we design the perfect minimal change to implement an every firewall router switch cloud platform. And then the last mile is we provision that change automatically. So we're able to make a change in minutes, instead of days would dramatically better security and accuracy. So the ROI on Tufin is not just security, but agility balanced with security at the same time. So you like the rules of the road, >>But the roads are changing all the time. That's how do you keep track of what's going on? You must have to have some sort of visualization technology when you lay out the topology and things start to be compliant, and then you might see opportunity to do innovative buckets. Hey, you know, I love this policy, but I'm, I'm going to work on my policy because sure. Got to up your game on policy and continue to iterate. Is that how do they, how do your customers Daniel? >>So listen, we we're, uh, we're not a tiny company anymore. We've grown. We went public in April of 2019 race and capital. We have over 500 employees, we sold over 2000 customers worldwide. So, um, you know, when customers ask us for advice, we come in and help them with consulting or professional services in terms of deployment. And the other piece is we gotta keep up all the time with what's happening with Fortnite. For example, as, as one of our strategic partners, every time fortnight makes the change we're on the beta program. So we know about a code change. We're able to test them the lab we know about their latest features. We got to keep up with all that. So that takes a lot of engineering efforts. We've hired a lot of engineers and we're hiring more. Uh, so it takes a lot of investment to do this at scale. And we're able to deliver that for our customers. >>I want the relationship with 400. I see you're here at the golf tournament. You're part of the pavilion. You're part of the tournament by the way. Congratulations. Great, great, great event. Thank you. What's the relationship with food and air from a product and a customer technology standpoint, >>We're working closely with Fortnite, where they're a strategic partner of ours. We're integrated into their Fordham manager, APIs. We're a fabric ready solution for them. So obviously working closely. Some of our biggest customers are fortnight's biggest customers will get the opportunity to sponsor this event, which is great tons of customers here and very interesting conversations. So we're very happy with that relationship. >>This is good. Yeah. So that ask you, what have you learned? I think you got great business success. Looking back now to where we are today, the speed of the market, what's your big takeaway in terms of how security changed and it continues to be challenging and these opportunities, what was the big takeaway for you? >>Well, I guess if you were like spanning my career, uh, the big takeaway is, uh, first of all, and just in startup world, patients think things come to those away, but also, um, you know, just, you got to have the basics, right? What we do is foundational. And there were times when people didn't believe in what we do or thought, you know, this is minor. This is not important as people move to the cloud, this won't matter. Oh, it matters. It matters not just in on-prem and it matters in the cloud as well. You gotta have a baseline of a policy and you gotta base everything around that. Um, and so w we've sort of had that mantra from day one and we were right. And we're, we're very happy to be where we are today. Yeah. >>And, you know, as a founder, a co-founder of the company, you know, most of the most successful companies I observed is usually misunderstood for a long time. That's true. Jesse's favorite quote on the cube. He's now the CEO of Amazon said we were misunderstood for a long time. I'm surprised it took people this long to figure out what we were doing. And, and that was good. A good thing. So, you know, just having that north star vision, staying true to the problem when there were probably opportunities that you are like, oh, we, you know, pressure or sure. Yeah. I mean, you stayed the course. What was the, what was the key thing? Grit focused. Yes. >>Looking to startup life. It's sorta like being in sales. We, we got told no, a thousand times before we got told yes. Or maybe a hundred times. So, uh, you gotta, you gotta be, um, you got to persevere. You gotta be really confident in what you're doing and, uh, just stay the course. And we felt pretty strongly about what we're building, that the technology was right. That the need of the market was right. And we just stuck to our guns. >>So focus on the future. What's the next five, five years look like, what's your focus? What's the strategic imperative for you guys. What's your, what's your, what do you mean working on? >>So there's several things that on the business side, we're transitioning to a subscription-based model and we're moving into SAS. One of our products is now a SAS based product. So that's very important to us. We also are now undergoing a shift. So we have a new version called Tufin Aurora Tufin Aurora is a transformation. It's our next generation product. Uh, we're rearchitected the entire, uh, underlying infrastructure to be based on microservices so we could be cloud ready. So that's a major focus in terms of engineering, uh, and in terms of customers, you know, we're, we're selling to larger and larger enterprises. And, uh, we think that this policy topic is critical, not just in the on-prem, but in the cloud. So in the next three years, as people move more and more to the cloud, we believe that what we do will be, become even more relevant as organization will straddle on-premise networks and the cloud. So >>Safe to say that you believe that policy based architecture is the key to automation. >>Absolutely. You can't automate what you don't know, and you can't people, like I mentioned this in my talk, people say, oh, I can do this. I can cook up an Ansible script and automate, all right, you'll push a change, but what is the logic? Why did you make that decision? Is it based on something you've got to have a core foundation? And that foundation is the policy >>Really great insight. Great to have you on the cube. You've got great success and working knowledge and you're in the right place. And you're skating to where the puck is and will be, as they say, congratulations on your success. Thank >>You very much. Thanks for having >>Me. Okay. Keep coming here. The Fortinet championship summit day, cybersecurity summit, 40 minutes golf tournament here in Napa valley. I'm John Firmicute. Thanks for watching.

>>From around the globe. It's the cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS, and our community partners. >>Hi everyone. Welcome back to the cubes. Virtual coverage of AWS reinvent 2020 virtual. We are the cube virtual I'm John ferry, your host with my coach, Dave Alante for keynote analysis from Swami's machine learning, all things, data huge. Instead of announcements, the first ever machine learning keynote at a re-invent Dave. Great to see you. Thanks Johnny. And from Boston, I'm here in Palo Alto. We're doing the cube remote cube virtual. Great to see you. >>Yeah, good to be here, John, as always. Wall-to-wall love it. So, so, John, um, how about I give you my, my key highlights from the, uh, from the keynote today, I had, I had four kind of curated takeaways. So the first is that AWS is, is really trying to simplify machine learning and use machine intelligence into all applications. And if you think about it, it's good news for organizations because they're not the become machine learning experts have invent machine learning. They can buy it from Amazon. I think the second is they're trying to simplify the data pipeline. The data pipeline today is characterized by a series of hyper specialized individuals. It engineers, data scientists, quality engineers, analysts, developers. These are folks that are largely live in their own swim lane. Uh, and while they collaborate, uh, there's still a fairly linear and complicated data pipeline, uh, that, that a business person or a data product builder has to go through Amazon making some moves to the front of simplify that they're expanding data access to the line of business. I think that's a key point. Is there, there increasingly as people build data products and data services that can monetize, you know, for their business, either cut costs or generate revenue, they can expand that into line of business where there's there's domain context. And I think the last thing is this theme that we talked about the other day, John of extending Amazon, AWS to the edge that we saw that as well in a number of machine learning tools that, uh, Swami talked about. >>Yeah, it was great by the way, we're live here, uh, in Palo Alto in Boston covering the analysis, tons of content on the cube, check out the cube.net and also check out at reinvent. There's a cube section as there's some links to so on demand videos with all the content we've had. Dave, I got to say one of the things that's apparent to me, and this came out of my one-on-one with Andy Jassy and Andy Jassy talked about in his keynote is he kind of teased out this idea of training versus a more value add machine learning. And you saw that today in today's announcement. To me, the big revelation was that the training aspect of machine learning, um, is what can be automated away. And it's under a lot of controversy around it. Recently, a Google paper came out and the person was essentially kind of, kind of let go for this. >>But the idea of doing these training algorithms, some are saying is causes more harm to the environment than it does good because of all the compute power it takes. So you start to see the positioning of training, which can be automated away and served up with, you know, high powered ships and that's, they consider that undifferentiated heavy lifting. In my opinion, they didn't say that, but that's clearly what I see coming out of this announcement. The other thing that I saw Dave that's notable is you saw them clearly taking a three lane approach to this machine, learning the advanced builders, the advanced coders and the developers, and then database and data analysts, three swim lanes of personas of target audience. Clearly that is in line with SageMaker and the embedded stuff. So two big revelations, more horsepower required to process training and modeling. Okay. And to the expansion of the personas that are going to be using machine learning. So clearly this is a, to me, a big trend wave that we're seeing that validates some of the startups and I'll see their SageMaker and some of their products. >>Well, as I was saying at the top, I think Amazon's really trying, working hard on simplifying the whole process. And you mentioned training and, and a lot of times people are starting from scratch when they have to train models and retrain models. And so what they're doing is they're trying to create reusable components, uh, and allow people to, as you pointed out to automate and streamline some of that heavy lifting, uh, and as well, they talked a lot about, uh, doing, doing AI inferencing at the edge. And you're seeing, you know, they, they, uh, Swami talked about several foundational premises and the first being a foundation of frameworks. And you think about that at the, at the lowest level of their S their ML stack. They've got, you know, GPU's different processors, inferential, all these alternative processes, processors, not just the, the Xav six. And so these are very expensive resources and Swami talked a lot about, uh, and his colleagues talked a lot about, well, a lot of times the alternative processor is sitting there, you know, waiting, waiting, waiting. And so they're really trying to drive efficiency and speed. They talked a lot about compressing the time that it takes to, to run these, these models, uh, from, from sometimes weeks down to days, sometimes days down to hours and minutes. >>Yeah. Let's, let's unpack these four areas. Let's stay on the firm foundation because that's their core competency infrastructure as a service. Clearly they're laying that down. You put the processors, but what's interesting is the TensorFlow 92% of tensor flows on Amazon. The other thing is that pie torch surprisingly is back up there, um, with massive adoption and the numbers on pie torch literally is on fire. I was coming in and joke on Twitter. Um, we, a PI torch is telling because that means that TensorFlow is originally part of Google is getting, is getting a little bit diluted with other frameworks, and then you've got MX net, some other things out there. So the fact that you've got PI torch 91% and then TensorFlow 92% on 80 bucks is a huge validation. That means that the majority of most machine learning development and deep learning is happening on AWS. Um, >>Yeah, cloud-based, by the way, just to clarify, that's the 90% of cloud-based cloud, uh, TensorFlow runs on and 91% of cloud-based PI torch runs on ADM is amazingly massive numbers. >>Yeah. And I think that the, the processor has to show that it's not trivial to do the machine learning, but, you know, that's where the infrared internship came in. That's kind of where they want to go lay down that foundation. And they had Tanium, they had trainee, um, they had, um, infrared chow was the chip. And then, you know, just true, you know, distributed training training on SageMaker. So you got the chip and then you've got Sage makers, the middleware games, almost like a machine learning stack. That's what they're putting out there >>And how bad a Gowdy, which was, which is, which is a patrol also for training, which is an Intel based chip. Uh, so that was kind of interesting. So a lot of new chips and, and specialized just, we've been talking about this for awhile, particularly as you get to the edge and do AI inferencing, you need, uh, you know, a different approach than we're used to with the general purpose microbes. >>So what gets your take on tenant? Number two? So tenant number one, clearly infrastructure, a lot of announcements we'll go through those, review them at the end, but tenant number two, that Swami put out there was creating the shortest path to success for builders or machine learning builders. And I think here you lays out the complexity, Dave butts, mostly around methodology, and, you know, the value activities required to execute. And again, this points to the complexity problem that they have. What's your take on this? >>Yeah. Well you think about, again, I'm talking about the pipeline, you collect data, you just data, you prepare that data, you analyze that data. You, you, you make sure that it's it's high quality and then you start the training and then you're iterating. And so they really trying to automate as much as possible and simplify as much as possible. What I really liked about that segment of foundation, number two, if you will, is the example, the customer example of the speaker from the NFL, you know, talked about, uh, you know, the AWS stats that we see in the commercials, uh, next gen stats. Uh, and, and she talked about the ways in which they've, well, we all know they've, they've rearchitected helmets. Uh, they've been, it's really a very much database. It was interesting to see they had the spectrum of the helmets that were, you know, the safest, most safe to the least safe and how they've migrated everybody in the NFL to those that they, she started a 24%. >>It was interesting how she wanted a 24% reduction in reported concussions. You know, you got to give the benefit of the doubt and assume some of that's through, through the data. But you know, some of that could be like, you know, Julian Edelman popping up off the ground. When, you know, we had a concussion, he doesn't want to come out of the game with the new protocol, but no doubt, they're collecting more data on this stuff, and it's not just head injuries. And she talked about ankle injuries, knee injuries. So all this comes from training models and reducing the time it takes to actually go from raw data to insights. >>Yeah. I mean, I think the NFL is a great example. You and I both know how hard it is to get the NFL to come on and do an interview. They're very coy. They don't really put their name on anything much because of the value of the NFL, this a meaningful partnership. You had the, the person onstage virtually really going into some real detail around the depth of the partnership. So to me, it's real, first of all, I love stat cast 11, anything to do with what they do with the stats is phenomenal at this point. So the real world example, Dave, that you starting to see sports as one metaphor, healthcare, and others are going to see those coming in to me, totally a tale sign that Amazon's continued to lead. The thing that got my attention was is that it is an IOT problem, and there's no reason why they shouldn't get to it. I mean, some say that, Oh, concussion, NFL is just covering their butt. They don't have to, this is actually really working. So you got the tech, why not use it? And they are. So that, to me, that's impressive. And I think that's, again, a digital transformation sign that, that, you know, in the NFL is doing it. It's real. Um, because it's just easier. >>I think, look, I think, I think it's easy to criticize the NFL, but the re the reality is, is there anything old days? It was like, Hey, you get your bell rung and get back out there. That's just the way it was a football players, you know, but Ted Johnson was one of the first and, you know, bill Bellacheck was, was, you know, the guy who sent him back out there with a concussion, but, but he was very much outspoken. You've got to give the NFL credit. Uh, it didn't just ignore the problem. Yeah. Maybe it, it took a little while, but you know, these things take some time because, you know, it's generally was generally accepted, you know, back in the day that, okay, Hey, you'd get right back out there, but, but the NFL has made big investments there. And you can say, you got to give him, give him props for that. And especially given that they're collecting all this data. That to me is the most interesting angle here is letting the data inform the actions. >>And next step, after the NFL, they had this data prep data Wrangler news, that they're now integrating snowflakes, Databricks, Mongo DB, into SageMaker, which is a theme there of Redshift S3 and Lake formation into not the other way around. So again, you've been following this pretty closely, uh, specifically the snowflake recent IPO and their success. Um, this is an ecosystem play for Amazon. What does it mean? >>Well, a couple of things, as we, as you well know, John, when you first called me up, I was in Dallas and I flew into New York and an ice storm to get to the one of the early Duke worlds. You know, and back then it was all batch. The big data was this big batch job. And today you want to combine that batch. There's still a lot of need for batch, but when people want real time inferencing and AWS is bringing that together and they're bringing in multiple data sources, you mentioned Databricks and snowflake Mongo. These are three platforms that are doing very well in the market and holding a lot of data in AWS and saying, okay, Hey, we want to be the brain in the middle. You can import data from any of those sources. And I'm sure they're going to add more over time. Uh, and so they talked about 300 pre-configured data transformations, uh, that now come with stage maker of SageMaker studio with essentially, I've talked about this a lot. It's essentially abstracting away the, it complexity, the whole it operations piece. I mean, it's the same old theme that AWS is just pointing. It's its platform and its cloud at non undifferentiated, heavy lifting. And it's moving it up the stack now into the data life cycle and data pipeline, which is one of the biggest blockers to monetizing data. >>Expand on that more. What does that actually mean? I'm an it person translate that into it. Speak. Yeah. >>So today, if you're, if you're a business person and you want, you want the answers, right, and you want say to adjust a new data source, so let's say you want to build a new, new product. Um, let me give an example. Let's say you're like a Spotify, make it up. And, and you do music today, but let's say you want to add, you know, movies, or you want to add podcasts and you want to start monetizing that you want to, you want to identify, who's watching what you want to create new metadata. Well, you need new data sources. So what you do as a business person that wants to create that new data product, let's say for podcasts, you have to knock on the door, get to the front of the data pipeline line and say, okay, Hey, can you please add this data source? >>And then everybody else down the line has to get in line and Hey, this becomes a new data source. And it's this linear process where very specialized individuals have to do their part. And then at the other end, you know, it comes to self-serve capability that somebody can use to either build dashboards or build a data product. In a lot of that middle part is our operational details around deploying infrastructure, deploying, you know, training machine learning models that a lot of Python coding. Yeah. There's SQL queries that have to be done. So a lot of very highly specialized activities, what Amazon is doing, my takeaway is they're really streamlining a lot of those activities, removing what they always call the non undifferentiated, heavy lifting abstracting away that it complexity to me, this is a real positive sign, because it's all about the technology serving the business, as opposed to historically, it's the business begging the technology department to please help me. The technology department obviously evolving from, you know, the, the glass house, if you will, to this new data, data pipeline data, life cycle. >>Yeah. I mean, it's classic agility to take down those. I mean, it's undifferentiated, I guess, but if it actually works, just create a differentiated product. So, but it's just log it's that it's, you can debate that kind of aspect of it, but I hear what you're saying, just get rid of it and make it simpler. Um, the impact of machine learning is Dave is one came out clear on this, uh, SageMaker clarify announcement, which is a bias decision algorithm. They had an expert, uh, nationally CFUs presented essentially how they're dealing with the, the, the bias piece of it. I thought that was very interesting. What'd you think? >>Well, so humans are biased and so humans build models or models are inherently biased. And so I thought it was, you know, this is a huge problem to big problems in artificial intelligence. One is the inherent bias in the models. And the second is the lack of transparency that, you know, they call it the black box problem, like, okay, I know there was an answer there, but how did it get to that answer and how do I trace it back? Uh, and so Amazon is really trying to attack those, uh, with, with, with clarify. I wasn't sure if it was clarity or clarified, I think it's clarity clarify, um, a lot of entirely certain how it works. So we really have to dig more into that, but it's essentially identifying situations where there is bias flagging those, and then, you know, I believe making recommendations as to how it can be stamped. >>Nope. Yeah. And also some other news deep profiling for debugger. So you could make a debugger, which is a deep profile on neural network training, um, which is very cool again on that same theme of profiling. The other thing that I found >>That remind me, John, if I may interrupt there reminded me of like grammar corrections and, you know, when you're typing, it's like, you know, bug code corrections and automated debugging, try this. >>It wasn't like a better debugger come on. We, first of all, it should be bug free code, but, um, you know, there's always biases of the data is critical. Um, the other news I thought was interesting and then Amazon's claiming this is the first SageMaker pipelines for purpose-built CIC D uh, for machine learning, bringing machine learning into a developer construct. And I think this started bringing in this idea of the edge manager where you have, you know, and they call it the about machine, uh, uh, SageMaker store storing your functions of this idea of managing and monitoring machine learning modules effectively is on the edge. And, and through the development process is interesting and really targeting that developer, Dave, >>Yeah, applying CIC D to the machine learning and machine intelligence has always been very challenging because again, there's so many piece parts. And so, you know, I said it the other day, it's like a lot of the innovations that Amazon comes out with are things that have problems that have come up given the pace of innovation that they're putting forth. And, and it's like the customers drinking from a fire hose. We've talked about this at previous reinvents and the, and the customers keep up with the pace of Amazon. So I see this as Amazon trying to reduce friction, you know, across its entire stack. Most, for example, >>Let me lay it out. A slide ahead, build machine learning, gurus developers, and then database and data analysts, clearly database developers and data analysts are on their radar. This is not the first time we've heard that. But we, as the kind of it is the first time we're starting to see products materialized where you have machine learning for databases, data warehouse, and data lakes, and then BI tools. So again, three different segments, the databases, the data warehouse and data lakes, and then the BI tools, three areas of machine learning, innovation, where you're seeing some product news, your, your take on this natural evolution. >>Well, well, it's what I'm saying up front is that the good news for, for, for our customers is you don't have to be a Google or Amazon or Facebook to be a super expert at AI. Uh, companies like Amazon are going to be providing products that you can then apply to your business. And, and it's allowed you to infuse AI across your entire application portfolio. Amazon Redshift ML was another, um, example of them, abstracting complexity. They're taking, they're taking S3 Redshift and SageMaker complexity and abstracting that and presenting it to the data analysts. So that, that, that individual can worry about, you know, again, getting to the insights, it's injecting ML into the database much in the same way, frankly, the big query has done that. And so that's a huge, huge positive. When you talk to customers, they, they love the fact that when, when ML can be embedded into the, into the database and it simplifies, uh, that, that all that, uh, uh, uh, complexity, they absolutely love it because they can focus on more important things. >>Clearly I'm this tenant, and this is part of the keynote. They were laying out all their announcements, quick excitement and ML insights out of the box, quick, quick site cue available in preview all the announcements. And then they moved on to the next, the fourth tenant day solving real problems end to end, kind of reminds me of the theme we heard at Dell technology worlds last year end to end it. So we are starting to see the, the, the land grab my opinion, Amazon really going after, beyond I, as in pass, they talked about contact content, contact centers, Kendra, uh, lookout for metrics, and that'll maintain men. Then Matt would came on, talk about all the massive disruption on the, in the industries. And he said, literally machine learning will disrupt every industry. They spent a lot of time on that and they went into the computer vision at the edge, which I'm a big fan of. I just loved that product. Clearly, every innovation, I mean, every vertical Dave is up for grabs. That's the key. Dr. Matt would message. >>Yeah. I mean, I totally agree. I mean, I see that machine intelligence as a top layer of, you know, the S the stack. And as I said, it's going to be infused into all areas. It's not some kind of separate thing, you know, like, Coobernetti's, we think it's some separate thing. It's not, it's going to be embedded everywhere. And I really like Amazon's edge strategy. It's this, you, you are the first to sort of write about it and your keynote preview, Andy Jassy said, we see, we see, we want to bring AWS to the edge. And we see data center as just another edge node. And so what they're doing is they're bringing SDKs. They've got a package of sensors. They're bringing appliances. I've said many, many times the developers are going to be, you know, the linchpin to the edge. And so Amazon is bringing its entire, you know, data plane is control plane, it's API APIs to the edge and giving builders or slash developers, the ability to innovate. And I really liked the strategy versus, Hey, here's a box it's, it's got an x86 processor inside on a, throw it over the edge, give it a cool name that has edge in it. And here you go, >>That sounds call it hyper edge. You know, I mean, the thing that's true is the data aspect at the edge. I mean, everything's got a database data warehouse and data lakes are involved in everything. And then, and some sort of BI or tools to get the data and work with the data or the data analyst, data feeds, machine learning, critical piece to all this, Dave, I mean, this is like databases used to be boring, like boring field. Like, you know, if you were a database, I have a degree in a database design, one of my degrees who do science degrees back then no one really cared. If you were a database person. Now it's like, man data, everything. This is a whole new field. This is an opportunity. But also, I mean, are there enough people out there to do all this? >>Well, it's a great point. And I think this is why Amazon is trying to extract some of the abstract. Some of the complexity I sat in on a private session around databases today and listened to a number of customers. And I will say this, you know, some of it I think was NDA. So I can't, I can't say too much, but I will say this Amazon's philosophy of the database. And you address this in your conversation with Andy Jassy across its entire portfolio is to have really, really fine grain access to the deep level API APIs across all their services. And he said, he said this to you. We don't necessarily want to be the abstraction layer per se, because when the market changes, that's harder for us to change. We want to have that fine-grained access. And so you're seeing that with database, whether it's, you know, no sequel, sequel, you know, the, the Aurora the different flavors of Aurora dynamo, DV, uh, red shift, uh, you know, already S on and on and on. There's just a number of data stores. And you're seeing, for instance, Oracle take a completely different approach. Yes, they have my SQL cause they know got that with the sun acquisition. But, but this is they're really about put, is putting as much capability into a single database as possible. Oh, you only need one database only different philosophy. >>Yeah. And then obviously a health Lake. And then that was pretty much the end of the, the announcements big impact to health care. Again, the theme of horizontal data, vertical specialization with data science and software playing out in real time. >>Yeah. Well, so I have asked this question many times in the cube, when is it that machines will be able to make better diagnoses than doctors and you know, that day is coming. If it's not here, uh, you know, I think helped like is really interesting. I've got an interview later on with one of the practitioners in that space. And so, you know, healthcare is something that is an industry that's ripe for disruption. It really hasn't been disruption disrupted. It's a very high, high risk obviously industry. Uh, but look at healthcare as we all know, it's too expensive. It's too slow. It's too cumbersome. It's too long sometimes to get to a diagnosis or be seen, Amazon's trying to attack with its partners, all of those problems. >>Well, Dave, let's, let's summarize our take on Amazon keynote with machine learning, I'll say pretty historic in the sense that there was so much content in first keynote last year with Andy Jassy, he spent like 75 minutes. He told me on machine learning, they had to kind of create their own category Swami, who we interviewed many times on the cube was awesome. But a lot of still a lot more stuff, more, 215 announcements this year, machine learning more capabilities than ever before. Um, moving faster, solving real problems, targeting the builders, um, fraud platform set of things is the Amazon cadence. What's your analysis of the keynote? >>Well, so I think a couple of things, one is, you know, we've said for a while now that the new innovation cocktail is cloud plus data, plus AI, it's really data machine intelligence or AI applied to that data. And the scale at cloud Amazon Naylor obviously has nailed the cloud infrastructure. It's got the data. That's why database is so important and it's gotta be a leader in machine intelligence. And you're seeing this in the, in the spending data, you know, with our partner ETR, you see that, uh, that AI and ML in terms of spending momentum is, is at the highest or, or at the highest, along with automation, uh, and containers. And so in. Why is that? It's because everybody is trying to infuse AI into their application portfolios. They're trying to automate as much as possible. They're trying to get insights that, that the systems can take action on. >>And, and, and actually it's really augmented intelligence in a big way, but, but really driving insights, speeding that time to insight and Amazon, they have to be a leader there that it's Amazon it's, it's, it's Google, it's the Facebook's, it's obviously Microsoft, you know, IBM's Tron trying to get in there. They were kind of first with, with Watson, but with they're far behind, I think, uh, the, the hyper hyper scale guys. Uh, but, but I guess like the key point is you're going to be buying this. Most companies are going to be buying this, not building it. And that's good news for organizations. >>Yeah. I mean, you get 80% there with the product. Why not go that way? The alternative is try to find some machine learning people to build it. They're hard to find. Um, so the seeing the scale of kind of replicating machine learning expertise with SageMaker, then ultimately into databases and tools, and then ultimately built into applications. I think, you know, this is the thing that I think they, my opinion is that Amazon continues to move up the stack, uh, with their capabilities. And I think machine learning is interesting because it's a whole new set of it's kind of its own little monster building block. That's just not one thing it's going to be super important. I think it's going to have an impact on the startup scene and innovation is going, gonna have an impact on incumbent companies that are currently leaders that are under threat from new entrance entering the business. >>So I think it's going to be a very entrepreneurial opportunity. And I think it's going to be interesting to see is how machine learning plays that role. Is it a defining feature that's core to the intellectual property, or is it enabling new intellectual property? So to me, I just don't see how that's going to fall yet. I would bet that today intellectual property will be built on top of Amazon's machine learning, where the new algorithms and the new things will be built separately. If you compete head to head with that scale, you could be on the wrong side of history. Again, this is a bet that the startups and the venture capitals will have to make is who's going to end up being on the right wave here. Because if you make the wrong design choice, you can have a very complex environment with IOT or whatever your app serving. If you can narrow it down and get a wedge in the marketplace, if you're a company, um, I think that's going to be an advantage. This could be great just to see how the impact of the ecosystem this will be. >>Well, I think something you said just now it gives a clue. You talked about, you know, the, the difficulty of finding the skills. And I think that's a big part of what Amazon and others who were innovating in machine learning are trying to do is the gap between those that are qualified to actually do this stuff. The data scientists, the quality engineers, the data engineers, et cetera. And so companies, you know, the last 10 years went out and tried to hire these people. They couldn't find them, they tried to train them. So it's taking too long. And now that I think they're looking toward machine intelligence to really solve that problem, because that scales, as we, as we know, outsourcing to services companies and just, you know, hardcore heavy lifting, does it doesn't scale that well, >>Well, you know what, give me some machine learning, give it to me faster. I want to take the 80% there and allow us to build certainly on the media cloud and the cube virtual that we're doing. Again, every vertical is going to impact a Dave. Great to see you, uh, great stuff. So far week two. So, you know, we're cube live, we're live covering the keynotes tomorrow. We'll be covering the keynotes for the public sector day. That should be chock-full action. That environment is going to impact the most by COVID a lot of innovation, a lot of coverage. I'm John Ferrari. And with Dave Alante, thanks for watching.