(upbeat music) >> Welcome back to San Francisco, "theCUBE" is live, our day two coverage of VMware Explore 2022 continues. Lisa Martin with Dave Nicholson. Dave and I are pleased to welcome Jason Collier, principal member of technical staff at AMD to the program. Jason, it's great to have you. >> Thank you, it's great to be here. >> So what's going on at AMD? I hear you have some juicy stuff to talk about. >> Oh, we've got a ton of juicy stuff to talk about. Clearly the Project Monterey announcement was big for us, so we've got that to talk about. Another thing that I really wanted to talk about was a tool that we created and we call it, it's the VMware Architecture Migration Tool, call it VAMT for short. It's a tool that we created and we worked together with VMware and some of their professional services crew to actually develop this tool. And it is also an open source based tool. And really the primary purpose is to easily enable you to move from one CPU architecture to another CPU architecture, and do that in a cold migration fashion. >> So we're probably not talking about CPUs from Tandy, Radio Shack systems, likely this would be what we might refer to as other X86 systems. >> Other X86 systems is a good way to refer to it. >> So it's interesting timing for the development and the release of a tool like this, because in this sort of X86 universe, there are players who have been delayed in terms of delivering their next gen stuff. My understanding is AMD has been public with the idea that they're on track for by the end of the year, Genoa, next gen architecture. So can you imagine a situation where someone has an existing set of infrastructure and they're like, hey, you know what I want to get on board, the AMD train, is this something they can use from the VMware environment? >> Absolutely, and when you think about- >> Tell us exactly what that would look like, walk us through 100 servers, VMware, 1000 VMs, just to make the math easy. What do you do? How does it work? >> So one, there's several things that the tool can do, we actually went through, the design process was quite extensive on this. And we went through all of the planning phases that you need to go through to do these VM migrations. Now this has to be a cold migration, it's not a live migration. You can't do that between the CPU architectures. But what we do is you create a list of all of the virtual machines that you want to migrate. So we take this CSV file, we import this CSV file, and we ask for things like, okay, what's the name? Where do you want to migrate it to? So from one cluster to another, what do you want to migrate it to? What are the networks that you want to move it to? And then the storage platform. So we can move storage, it could either be shared storage, or we could move say from VSAN to VSAN, however you want to set it up. So it will do those storage migrations as well. And then what happens is it's actually going to go through, it's going to shut down the VM, it's going to take a snapshot, it is going to then basically move the compute and/or storage resources over. And once it does that, it's going to power 'em back up. And it's going to check, we've got some validation tools, where it's going to make sure VM Tools comes back up where everything is copacetic, it didn't blue screen or anything like that. And once it comes back up, then everything's good, it moves onto the next one. Now a couple of things that we've got feature wise, we built into it. You can parallelize these tasks. So you can say, how many of these machines do you want to do at any given time? So it could be, say 10 machines, 50 machines, 100 machines at a time, that you want to go through and do this move. Now, if it did blue screen, it will actually roll it back to that snapshot on the origin cluster. So that there is some protection on that. A couple other things that are actually in there are things like audit tracking. So we do full audit logging on this stuff, we take a snapshot, there's basically kind of an audit trail of what happens. There's also full logging, SYS logging, and then also we'll do email reporting. So you can say, run this and then shoot me a report when this is over. Now, one other cool thing is you can also actually define a change window. So I don't want to do this in the middle of the afternoon on a Tuesday. So I want to do this later at night, over the weekend, you can actually just queue this up, set it, schedule it, it'll run. You can also define how long you want that change window to be. And what it'll do, it'll do as many as it can, then it'll effectively stop, finish up, clean up the tasks and then send you a report on what all was successfully moved. >> Okay, I'm going to go down the rabbit hole a little bit on this, 'cause I think it's important. And if I say something incorrect, you correct me. >> No problem. >> In terms of my technical understanding. >> I got you. >> So you've got a VM, essentially a virtual machine typically will consist of an entire operating system within that virtual machine. So there's a construct that containerizes, if you will, the operating system, what is the difference, where is the difference in the instruction set? Where does it lie? Is it in the OS' interaction with the CPU or is it between the construct that is the sort of wrapper around the VM that is the difference? >> It's really primarily the OS, right? And we've not really had too many issues doing this and most of the time, what is going to happen, that OS is going to boot up, it's going to recognize the architecture that it's on, it's going to see the underlying architecture, and boot up. All the major operating systems that we test worked fine. I mean, typically they're going to work on all the X86 platforms. But there might be instruction sets that are kind of enabled in one architecture that may not be in another architecture. >> And you're looking for that during this process. >> Well usually the OS itself is going to kind of detect that. So if it pops up, the one thing that is kind of a caution that you need to look for. If you've got an application that's explicitly using an instruction set that's on one CPU vendor and not the other CPU vendor. That's the one thing where you're probably going to see some application differences. That said, it'll probably be compatible, but you may not get that instruction set advantage in it. >> But this tool remediates against that. >> Yeah, and what we do, we're actually using VM Tools itself to go through and validate a lot of those components. So we'll look and make sure VM Tools is enabled in the first place, on the source system. And then when it gets to the destination system, we also look at VM Tools to see what is and what is not enabled. >> Okay, I'm going to put you on the spot here. What's the zinger, where doesn't it work? You already said cold, we understand, you can schedule for cold migrations, that's not a zinger. What's the zinger, where doesn't it work? >> It doesn't work like, live migrations just don't work. >> No live, okay, okay, no live. What about something else? What's the oh, you've got that version, you've got that version of X86 architecture, it-won't work, anything? >> A majority of those cases work, where it would fail, where it's going to kick back and say, hey, VM Tools is not installed. So where you would see this is if you're running a virtual appliance from some vendor, like insert vendor here that say, got a firewall, or got something like that, and they don't have VM Tools enabled. It's going to fail it out of the gate, and say, hey, VM Tools is not on this, you might want to manually do it. >> But you can figure out how to fix that? >> You can figure out how to do that. You can also, and there's a flag in there, so in kind of the options that you give it, you say, ignore VM Tools, don't care, move it anyway. So if you've got less, some VMs that are in there, but they're not a priority VM, then it's going to migrate just fine. >> Got It. >> Can you elaborate a little bit on the joint development work that AMD and VMware are doing together and the value in it for customers? >> Yeah, so it's one of those things we worked with VMware to basically produce this open source tool. So we did a lot of the core component and design and we actually engaged VMware Professional Services. And a big shout out to Austin Browder. He helped us a ton in this project specifically. And we basically worked, we created this, kind of co-designed, what it was going to look like. And then jointly worked together on the coding, of pulling this thing together. And then after that, and this is actually posted up on VMware's public repos now in GitHub. So you can go to GitHub, you can go to the VMware samples code, and you can download this thing that we've created. And it's really built to help ease migrations from one architecture to another. So if you're looking for a big data center move and you got a bunch of VMs to move. I mean, even if it's same architecture to same architecture, it's definitely going to ease the pain of going through and doing a migration of, it's one thing when you're doing 10 machines, but when you're doing 10,000 virtual machines, that's a different story. It gets to be quite operationally inefficient. >> I lose track after three. >> Yeah. >> So I'm good for three, not four. >> I was going to ask you what your target market segment is here. Expand on that a little bit and talk to me about who you're working with and those organizations. >> So really this is targeted toward organizations that have large deployments in enterprise, but also I think this is a big play with channel partners as well. So folks out there in the channel that are doing these migrations and they do a lot of these, when you're thinking about the small and mid-size organizations, it's a great fit for that. Especially if they're kind of doing that upgrade, the lift and shift upgrade, from here's where you've been five to seven years on an architecture and you want to move to a new architecture. This is really going to help. And this is not a point and click GUI kind of thing. It's command line driven, it's using PowerShell, we're using PowerCLI to do the majority of this work. And for channel partners, this is an excellent opportunity to put the value and the value add and VAR, And there's a lot of opportunity for, I think, channel partners to really go and take this. And once again, being open source. We expect this to be extensible, we want the community to contribute and put back into this to basically help grow it and make it a more useful tool for doing these cold migrations between CPU architectures. >> Have you seen any in the last couple of years of dynamics, obviously across the world, any industries in particular that are really leading edge for what you guys are doing? >> Yeah, that's really, really interesting. I mean, we've seen it, it's honestly been a very horizontal problem, pretty much across all vertical markets. I mean, we've seen it in financial services, we've seen it in, honestly, pretty much across the board. Manufacturing, financial services, healthcare, we have seen kind of a strong interest in that. And then also we we've actually taken this and presented this to some of our channel partners as well. And there's been a lot of interest in it. I think we presented it to about 30 different channel partners, a couple of weeks back about this. And I got contact from 30 different channel partners that said they're interested in basically helping us work on it. >> Tagging on to Lisa's question, do you have visibility into the AMD thought process around the timing of your next gen release versus others that are competitors in the marketplace? How you might leverage that in terms of programs where partners are going out and saying, hey, perfect time, you need a refresh, perfect time to look at AMD, if you haven't looked at them recently. Do you have any insight into that in what's going on? I know you're focused on this area. But what are your thoughts on, well, what's the buzz? What's the buzz inside AMD on that? >> Well, when you look overall, if you look at the Gartner Hype Cycle, when VMware was being broadly adopted, when VMware was being broadly adopted, I'm going to be blunt, and I'm going to be honest right here, AMD didn't have a horse in the race. And the majority of those VMware deployments we see are not running on AMD. Now that said, there's an extreme interest in the fact that we've got these very cored in systems that are now coming up on, now you're at that five to seven year refresh window of pulling in new hardware. And we have extremely attractive hardware when it comes to running virtualized workloads. The test cluster that I'm running at home, I've got that five to seven year old gear, and I've got some of the, even just the Milan systems that we've got. And I've got three nodes of another architecture going onto AMD. And when I got these three nodes completely maxed to the number of VMs that I can run on 'em, I'm at a quarter of the capacity of what I'm putting on the new stuff. So what you get is, I mean, we worked the numbers, and it's definitely, it's like a 30% decrease in the amount of resources that you need. >> That's a compelling number. >> It's a compelling number. >> 5%, 10%, nobody's going to do anything for that. You talk 30%. >> 30%. It's meaningful, it's meaningful. Now you you're out of Austin, right? >> Yes. >> So first thing I thought of when you talk about running clusters in your home is the cost of electricity, but you're okay. >> I'm okay. >> You don't live here, you don't live here, you don't need to worry about that. >> I'm okay. >> Do you have a favorite customer example that you think really articulates the value of AMD when you're in customer conversations and they go, why AMD and you hit back with this? >> Yeah. Actually it's funny because I had a conversation like that last night, kind of random person I met later on in the evening. We were going through this discussion and they were facing exactly this problem. They had that five to seven year infrastructure. It's funny, because the guy was a gamer too, and he's like, man, I've always been a big AMD fan, I love the CPUs all the way since back in basically the Opterons and Athlons right. He's like, I've always loved the AMD systems, loved the graphics cards. And now with what we're doing with Ryzen and all that stuff. He's always been a big AMD fan. He's like, and I'm going through doing my infrastructure refresh. And I told him, I'm just like, well, hey, talk to your VAR and have 'em plug some AMD SKUs in there from the Dells, HPs and Lenovos. And then we've got this tool to basically help make that migration easier on you. And so once we had that discussion and it was great, then he swung by the booth today and I was able to just go over, hey, this is the tool, this is how you use it, here's all the info. Call me if you need any help. >> Yeah, when we were talking earlier, we learned that you were at Scale. So what are you liking about AMD? How does that relate? >> The funny thing is this is actually the first time in my career that I've actually had a job where I didn't work for myself. I've been doing venture backed startups the last 25 years and we've raised couple hundred million dollars worth of investment over the years. And so one, I figured, here I am going to AMD, a larger corporation. I'm just like, am I going to be able to make it a year? And I have been here longer than a year and I absolutely love it. The culture at AMD is amazing. We still have that really, I mean, almost it's like that underdog mentality within the organization. And the team that I'm working with is a phenomenal team. And it's actually, our EVP and our Corp VP, were actually my executive sponsors, we were at a prior company. They were one of my executive sponsors when I was at Scale. And so my now VP boss calls me up and says, hey, I'm putting a band together, are you interested? And I was kind of enjoying a semi-retirement lifestyle. And then I'm just like, man, because it's you, yes, I am interested. And the group that we're in, the work that we're doing, the way that we're really focusing on forward looking things that are affecting the data center, what's going to be the data center like three to five years from now. It's exciting, and I am having a blast, I'm having the time of my life. I absolutely love it. >> Well, that relationship and the trust that you will have with each other, that bleeds into the customer conversations, the partner conversations, the employee conversations, it's all inextricably linked. >> Yes it is. >> And we want to know, you said three to five years out, like what? Like what? Just general futurist stuff, where do you think this is going. >> Well, it's interesting. >> So moon collides with the earth in 2025, we already know that. >> So we dialed this back to the Pensando acquisition. When you look at the Pensando acquisition and you look at basically where data centers are today, but then you look at where basically the big hyperscalers are. You look at an AWS, you look at their architecture, you specifically wrap Nitro around that, that's a very different architecture than what's being run in the data center. And when you look at what Pensando does, that's a lot of starting to bring what these real clouds out there, what these big hyperscalers are running into the grasps of the data center. And so I think you're going to see a fundamental shift. The next 10 years are going to be exciting because the way you look at a data center now, when you think of what CPUs do, what shared storage, how the networking is all set up, it ain't going to look the same. >> Okay, so the competing vision with that, to play devil's advocate, would be DPUs are kind of expensive. Why don't we just use NICs, give 'em some more bandwidth, and use the cheapest stuff. That's the competing vision. >> That could be. >> Or the alternative vision, and I imagine everything else we've experienced in our careers, they will run in parallel paths, fit for function. >> Well, parallel paths always exist, right? Otherwise, 'cause you know how many times you've heard mainframe's dead, tape's dead, spinning disk is dead. None of 'em dead, right? The reality is you get to a point within an industry where it basically goes from instead of a growth curve like that, it goes to a growth curve of like that, it's pretty flat. So from a revenue growth perspective, I don't think you're going to see the revenue growth there. I think you're going to see the revenue growth in DPUs. And when you actually take, they may be expensive now, but you look at what Monterey's doing and you look at the way that those DPUs are getting integrated in at the OEM level. It's going to be a part of it. You're going to order your VxRail and VSAN style boxes, they're going to come with them. It's going to be an integrated component. Because when you start to offload things off the CPU, you've driven your overall utilization up. When you don't have to process NSX on basically the X86, you've just freed up cores and a considerable amount of them. And you've also moved that to where there's a more intelligent place for that pack to be processed right, out here on this edge. 'Cause you know what, that might not need to go into the host bus at all. So you have just alleviated any transfers over a PCI bus, over the PCI lanes, into DRAM, all of these components, when you're like, but all to come with, oh, that bit needs to be on this other machine. So now it's coming in and it's making that decision there. And then you take and integrate that into things like the Aruba Smart Switch, that's running the Pensando technology. So now you got top of rack that is already making those intelligent routing decisions on where packets really need to go. >> Jason, thank you so much for joining us. I know you guys could keep talking. >> No, I was going to say, you're going to have to come back. You're going to have to come back. >> We've just started to peel the layers of the onion, but we really appreciate you coming by the show, talking about what AMD and VMware are doing, what you're enabling customers to achieve. Sounds like there's a lot of tailwind behind you. That's awesome. >> Yeah. >> Great stuff, thank you. >> It's a great time to be at AMD, I can tell you that. >> Oh, that's good to hear, we like it. Well, thank you again for joining us, we appreciate it. For our guest and Dave Nicholson, I'm Lisa Martin. You're watching "theCUBE Live" from San Francisco, VMware Explore 2022. We'll be back with our next guest in just a minute. (upbeat music)

(upbeat intro music) >> More than a decade ago, the team at Wikibon coined the term Server SAN. We saw the opportunity to dramatically change the storage infrastructure layer and predicted a major change in technologies that would hit the market. Server SAN had three fundamental attributes. First of all, it was software led. So all the traditionally expensive controller functions like snapshots and clones and de-dupe and replication, compression, encryption, et cetera, they were done in software directly challenging a two to three decade long storage controller paradigm. The second principle was it leveraged and shared storage inside of servers. And the third it enabled any-to-any typology between servers and storage. Now, at the time we defined this coming trend in a relatively narrow sense inside of a data center location, for example, but in the past decade, two additional major trends have emerged. First the software defined data center became the dominant model, thanks to VMware and others. And while this eliminated a lot of overhead, it also exposed another problem. Specifically data centers today allocate probably we estimate around 35% of CPU cores and cycles to managing things like storage and network and security, offloading those functions. This is wasted cores and doing this with traditional general purpose x86 processors is expensive and it's not efficient. This is why we've been reporting so aggressively on ARM's ascendancy into the enterprise. It's not only coming it's here and we're going to talk about that today. The second mega trend is cloud computing. Hyperscale infrastructure has allowed technology companies to put a management and control plane into the cloud and expand beyond our narrow server SAN scope within a single data center and support the management of distributed data at massive scale. And today we're on the cusp of a new era of infrastructure. And one of the startups in this space is Nebulon. Hello everybody, this is Dave Vellante, and welcome to this Cube Conversation where we welcome in two great guests, Craig Nunes, Cube alum, co-founder and COO at Nebulon and Tobias Flitsch who's director of product management at Nebulon. Guys, welcome. Great to see you. >> So good to be here Dave. Feels awesome. >> Soon, face to face. Craig, I'm heading your way. >> I can't wait. >> Craig, you heard my narrative upfront and I'm wondering are those the trends that you guys saw when you, when you started the company, what are the major shifts in the world today that, that caused you and your co-founders to launch Nebulon? >> Yeah, I'll give you sort of the way we think about the world, which I think aligns super right with, with what you're talking about, you know, over the last several years, organizations have had a great deal of experience with public cloud data centers. And I think like any platform or technology that is, you know, gets its use in a variety of ways, you know, a bit of savvy is being developed by organizations on, you know, what do I put where, how do I manage things in the most efficient way possible? And there are, in terms of the types of folks we're focused on in Nebulon's business, we see now kind of three groups of people emerging, and, and we sort of simply coined three terms, the returners, the removers, and the remainers. I'll explain what I mean by each of those, the returners are folks who maybe early on, you know, hit the gas on cloud, moved, you know, everything in, a lot in, and realize that while it's awesome for some things, for other things, it was less optimal. Maybe cost became a factor or visibility into what was going on with their data was a factor, security, service levels, whatever. And they've decided to move some of those workloads back. Returners. There are what I call the removers that are taking workloads from, you know, born in the cloud. On-prem, you know, and this was talked a lot about in Martine's blog that, you know, talked about a lot of the growth companies that built up such a large footprint in the public cloud, that economics were kind of working against them. You can, depending on the knobs you turn, you know, you're probably spending two and a half X, two X, what you might spend if you own your own factory. And you can argue about, you know, where your leverage is in negotiating your pricing with the cloud vendors, but there's a big gap. The last one is, and I think probably the most significant in terms of who we've engaged with is the remainers. And the remainers are, you know, hybrid IT organizations. They've got assets in the cloud and on-prem, they aspire to an operational model that is consistent across everything and, you know, leveraging all the best stuff that they observed in their cloud-based assets. And it's kind of our view that frankly we take from, from this constituency that, when people talk about cloud or cloud first, they're moving to something that is really more an operating model versus a destination or data center choice. And so, we get people on the phone every day, talking about cloud first. And when you kind of dig into what they're after, it's operating model characteristics, not which data center do I put it in, and those, those decisions are separating. And so that, you know, it's really that focus for us is where, we believe we're doing something unique for that group of customers. >> Yeah. Cloud first doesn't doesn't mean cloud only. And of course followers of this program know, we talk a lot about this, this definition of cloud is changing, it's evolving, It's moving to the edge, it's moving to data centers, data centers are moving to the cloud. Cross-cloud, it's that big layer that's expanding. And so I think the definition of cloud, even particularly in customer's minds is evolving. There's no question about it. People, they'll look at what VMware is doing in AWS and say, okay, that's cloud, but they'll also look at things like VMware cloud foundation and say oh yeah, that's cloud too. So to me, the beauty of cloud is in the eye of the customer beholder. So I buy that. Tobias. I wonder if you could talk about how this all translates into product, because you guys start up, you got to sell stuff, you use this term smart infrastructure, what is that? How does this all turn into stuff you can sell? >> Right. Yeah. So let me back up a little bit and talk a little bit about, you know, what we at Nebulon do. So we are a cloud based software company, and we're delivering sort of a new category of smart infrastructure. And if you think about things that you would know from your everyday surroundings, smart infrastructure is really all around us. Think smart home technology like Google Nest as an example. And what this all has in common is that there's a cloud control plane that is managing some IOT end points and smart devices in various locations. And by doing that, customers gain benefits like easy remote management, right? You can manage your thermostat, your temperature from anywhere in the world basically. You don't have to worry about automated software updates anymore, and you can easily automate your home, your infrastructure, through this cloud control plane and translating this idea to the data center, right? This idea is not necessarily new, right? If you look into the networking space with Meraki networks, now Cisco or Mist Systems now Juniper, they've really pioneered efforts in cloud management. So smart network infrastructure, and the key problem that they solved there is, you know, managing these vast amount of access points and switches that are scattered across the data centers across campuses, and, you know, the data center. Now, if you translate that to what Nebulon does, it's really applying this smart infrastructure idea, this methodology to application infrastructure, specifically to compute and storage infrastructure. And that's essentially what we're doing. So with smart infrastructure, basically our offering it at Nebulon, the product, that comes with the benefits of this cloud experience, public cloud operating model, as we've talked about, some of our customers look at the cloud as an operating model rather than a destination, a physical location. And with that, we bring to us this model, this, this experience as operating a model to on-premises application infrastructure, and really what you get with this broad offering from Nebulon and the benefits are really circling it out, you know, four areas, first of all, rapid time to value, right? So application owners think people that are not specialists or experts when it comes to IT infrastructure, but more generalists, they can provision on-premise application infrastructure in less than 10 minutes, right? It can go from, from just bare metal physical racks to the full application stack in less than 10 minutes, so they're up and running a lot quicker. And they can immediately deliver services to their end customers, cloud-like operations, this, this notion of zero touch remote management, which now with the last couple of months with this strange time that we're with COVID is, you know, turnout is becoming more and more relevant really as in remotely administrating and management of infrastructure that scales from just hundreds of nodes to thousands of nodes. It doesn't really matter with behind the scenes software updates, with global AI analytics and insights, and basically overall combined reduce the operational overhead when it comes to on-premises infrastructure by up to 75%, right? The other thing is support for any application, whether it's containerized, virtualized, or even bare metal applications. And the idea here is really consistent leveraging server-based storage that doesn't require any Nebulon-specific software on the server. So you get the full power of your application servers for your applications. Again, as the servers intended. And then the fourth benefit when it comes to smart infrastructure is, is of course doing this all at a lower cost and with better data center density. And that is really comparing it to three-tier architectures where you have your server, your SAN fabric, and then you have an external storage, but also when you compare it with hyper-converged infrastructure software, right, that is consuming resources of the application servers, think CPU, think memory and networking. So basically you get a lot more density with that approach compared to those architectures. >> Okay, I want to dig into some of that differentiation too, but what exactly do I buy from you? Do I buy a software subscription? Is that right? Can you explain that a little bit? >> Right. So basically the way we do this is it's really leveraging two key new innovations, right? So, and you see why I made the bridge to smart home technology, because the approach is civil, right? The one is, you know, the introduction of a cloud control plane that basically manage this on-premise application infrastructure, of course, that is delivered to customers as a service. The second one is, you know, a new infrastructure model that uses IOT endpoint technology, and that is embedded into standard application servers and the storage within this application servers. Let me add a couple of words to that to explain a little bit more, so really at the heart of smart infrastructure, in order to deliver this public cloud experience for any on-prem application is this cloud-based control plane, right? So we've built this, how we recommend our customers to use a public cloud, and that is built, you know, building your software on modern technologies that are vendor-agnostic. So it could essentially run anywhere, whether it is, you know, any public cloud vendor, or if we want to run in our own data centers, when regulatory requirements change, it's massively scalable and responsive, no matter how large the managed infrastructure is. But really the interesting part here, Dave, is that the customer doesn't really have to worry about any of that, it's delivered as a service. So what a customer gets from this cloud control plane is a single API end point, how they get it with a public cloud. They get a web user interface, from which they can manage all of their infrastructure, no matter how many devices, no matter where it is, can be in the data center, can be in an edge location anywhere in the world, they get template-based provisioning much like a marketplace in a public cloud. They get analytics, predictive support services, and super easy automation capabilities. Now the second thing that I mentioned is this server embedded software, the server embedded infrastructure software, and that is running on a PCIE based offload engine. And that is really acting as this managed IOT endpoint within the application server that I managed that I mentioned earlier. And that approach really further converges modern application infrastructure. And it really replaces the software defined storage approach that you'll find in hyper-converged infrastructure software. And that is really by embedding the data services, the storage data service into silicon within the server. Now this offload engine, we call that a services processing unit or SPU in short. And that is really what differentiates us from hyper-converged infrastructure. And it's quite different than a regular accelerator card that you get with some of the hyper-converged infrastructure offerings. And it's different in the sense that the SPU runs basically all of the shared and local data services, and it's not just accelerating individual algorithms, individual functions. And it basically provides all of these services aside the CPU with the boot drive, with data drives. And in essence provides you with this a separate fall domain from the service, so for example, if you reboot your server, the data plan remains intact. You know, it's not impacted for that. >> Okay. So I want to stay on that for just a second, Craig, if I could, I get very clear how you're different from, as Tobias said, the three-tier server SAN fabric, external array, the HCI thing's interesting because in some respects, the HCI has, you know, guys take Nutanix, they talk about cloud and becoming more friendly with developers and API piece, but what's your point of view Craig on how you position relative to say HCI? >> Yeah, absolutely. So everyone gets what three-tier architecture is and was, and HCI software, you know, emerged as an alternative to the three-tier architectures. Everyone I think today understands that data services are, you know, SDS is software hosted in the operating system of each HCI device and consume some amount of CPU, memory, network, whatever. And it's typically constrained to a hypervisor environment, kind of where we're most of that stuff is done. And over time, these platforms have added some monitoring capabilities, predictive analytics, typically provided by the vendor's cloud, right? And as Tobias mentioned, some HCIS vendors have augmented this approach by adding an accelerator to make things like compression and dedupe go faster, right? Think SimpliVity or something like that. The difference that we're talking about here is, the infrastructure software that we deliver as a service is embedded right into server silicon. So it's not sitting in the operating system of choice. And what that means is you get the full power of the server you bought for your workloads. It's not constrained to a hypervisor-only environment, it's OS agnostic. And, you know, it's entirely controlled and administered by the cloud versus with, you know, most HCIS is an on-prem console that manages a cluster or two on-prem. And, you know, think of it from a automation perspective. When you automate something, you've got to set up your playbook kind of cluster by cluster. And depending what versions they're on, APIs are changing, behaviors are changing. So a very different approach at scale. And so again, for us, we're talking about something that gives you a much more efficient infrastructure that is then managed by the cloud and gives you this full kind of operational model you would expect for any kind of cloud-based deployment. >> You know, I got to go back, you guys obviously have some three-part DNA hanging around and you know, of course you remember well, the three-part ASIC, it was kind of famous at the time and it was unique. And I bring that up only because you've mentioned a couple of times the silicon and a lot of people yeah, whatever, but we have been on this, especially, particularly with ARM. And I want to share with the audience, if you follow my breaking analysis, you know this. If you look at the historical curve of Moore's law with x86, it's the doubling of performance every two years, roughly, that comes out to about 40% a year. That's moderated down to about 30% a year now, if you look at the ARM ecosystem and take for instance, apple A15, and the previous series, for example, over the last five years, the performance, when you combine the CPU, GPU, NPU, the accelerators, the DSPs, which by the way, are all customizable. That's growing at 110% a year, and the SOC costs 50 bucks. So my point is that you guys are riding perfect example of doing offloads with a way more efficient architecture. You're now on that curve, that's growing at 100% plus per year. Whereas a lot of the legacy storage is still on that 30% a year curve, and so cheaper, lower power. That's why I love to buy, as you were bringing in the IOT and the smart infrastructure, this is the future of storage and infrastructure. >> Absolutely. And the thing I would emphasize is it's not limited to storage, storage is a big issue, but we're talking about your application infrastructure and you brought up something interesting on the GPU, the SmartNIC of things, and just to kind of level set with everybody there, there's the HCI world, and then there's this SmartNIC DPU world, whatever you want to call it, where it's effectively a network card, it's got that specialized processing onboard and firmware to provide some network security storage services, and think of it as a PCIE card in your server. It connects to an external storage system, so think Nvidia Bluefield 2 connecting to an external NVME storage device. And the interesting thing about that is, you know, storage processing is offloaded from the server. So as we said earlier, good, right, you get the server back to your application, but storage moves out of the server. And it starts to look a little bit like an external storage approach versus a server based approach. And infrastructure management is done by, you know, the server SmartNIC with some monitoring and analytics coming from, you know, your supplier's cloud support service. So complexity creeps back in, if you start to lose that, you know, heavily converged approach. Again, we are taking advantage of storage within the server and, you know, keeping this a real server based approach, but distinguishing ourselves from the HCI approach. Cause there's a real ROI there. And when we talked to folks who are looking at new and different ways, we talk a lot about the cloud and I think we've done a bit of that already, but then at the end of the day, folks are trying to figure out well, okay, but then what do I buy to enable this? And what you buy is your standard server recipe. So think your favorite HPE, Lenovo, Supermicro, whatever, whatever your brand, and it's going to come enabled with this IOT end point within it, so it's really a smart server, if you will, that can then be controlled by our cloud. And so you're effectively buying, you know, from your favorite server vendor, a server option that is this endpoint and a subscription. You don't buy any of this from us, by the way, it's all coming from them. And that's the way we deliver this. >> You know, sorry to get into the plumbing, but this is something we've been on and a facet of it. Is that silicon custom designed or is it pretty much off the shelf, do you guys add any value to it? >> No, there are off the shelf options that can deliver tremendous horsepower on that form factor. And so we take advantage of that to, you know, do what we do in terms of, you know, creating these sort of smart servers with our end point. And so that's where we're at. >> Yeah. Awesome. So guys, what's your sweet spot, you know, why are customers, you know, what are you seeing customers adopting? Maybe some examples you guys can share? >> Yeah, absolutely. So I think Tobias mentioned that because of the architectural approach, there's a lot of flexibility there, you can run virtualized, containerized, bare metal applications. The question is where are folks choosing to get started? And those use cases with our existing customers revolved heavily around virtualization modernization. So they're going back in to their virtualized environment, whether their existing infrastructure is array-based or HCI-based. And they're looking to streamline that, save money, automate more, the usual things. The second area is the distributed edge. You know, the edge is going through tremendous transformation with IOT devices, 5g, and trying to get processing closer to where customers are doing work. And so that distributed edge is a real opportunity because again, it's a more cost-effective, more dense infrastructure. The cloud effectively can manage across all of these sites through a single API. And then the third area is cloud service provider transformation. We do a fair bit of business with, you know, cloud service providers, CTOs, who are looking at trying to build top line growth, trying to create new services and, and drive better bottom line. And so this is really, you know, as much as a revenue opportunity for them as cost saving opportunity. And then the last one is this notion of, you know, bringing the cloud on-prem, we've done a cloud repatriation deal. And I know you've seen a little of that, but maybe not a lot of it. And, you know, I can tell you in our first deals, we've already seen it, so it's out there. Those are the places where people are getting started with us today. >> It's just interesting, you're right. I don't see a ton of it, but if I'm going to repatriate, I don't want to go backwards. I don't want to repatriate to legacy. So it actually does kind of make sense that I repatriate to essentially a component of on-prem cloud that's managed in the cloud, that makes sense to me to buy. But today you're managing from the cloud, you're managing on-prem infrastructure. Maybe you could show us a little leg, share a little roadmap, I mean, where are you guys headed from a product standpoint? >> Right, so I'm not going to go too far on the limb there, but obviously, right. So one of the key benefits of a cloud managed platform is this notion of a single API, right. We talked about the distributed edge where, you know, think of retailer that has, you know, thousands of stores, each store having local infrastructure. And, you know, if you think about the challenges that come with, you know, just administrating those systems, rolling out firmware updates, rolling out updates in general, monitoring those systems, et cetera. So having a single console, a cloud console to administrate all of that infrastructure, obviously, you know, the benefits are easy now. If you think about, if you're thinking about that and spin it further, right? So from the use cases and the types of users that we've see, and Craig talked about them at the very beginning, you can think about this as this is a hybrid world, right. Customers will have data that they'll have in the public cloud. They will have data and applications in their data centers and at the edge, obviously it is our objective to deliver the same experience that they gained from the public cloud on-prem, and eventually, you know, those two things can come closer together. Apart from that, we're constantly improving the data services. And as you mentioned, ARM is, is on a path that is becoming stronger and faster. So obviously we're going to leverage on that and build out our data storage services and become faster. But really the key thing that I'd like to, to mention all the time, and this is related to roadmap, but rather feature delivery, right? So the majority of what we do is in the cloud, our business logic in the cloud, the capabilities, the things that make infrastructure work are delivered in the cloud. And, you know, it's provided as a service. So compared with your Gmail, you know, your cloud services, one day, you don't have a feature, the next day you have a feature, so we're continuously rolling out new capabilities through our cloud. >> And that's about feature acceleration as opposed to technical debt, which is what you get with legacy features, feature creep. >> Absolutely. The other thing I would say too, is a big focus for us now is to help our customers more easily consume this new concept. And we've already got, you know, SDKs for things like Python and PowerShell and some of those things, but we've got, I think, nearly ready, an Ansible SDK. We're trying to help folks better kind of use case by use case, spin this stuff up within their organization, their infrastructure. Because again, part of our objective, we know that IT professionals have, you know, a lot of inertia when they're, you know, moving stuff around in their own data center. And we're aiming to make this, you know, a much simpler, more agile experience to deploy and grow over time. >> We've got to go, but Craig, quick company stats. Am I correct, you've raised just under 20 million. Where are you on funding? What's your head count today? >> I am going to plead the fifth on all of that. >> Oh, okay. Keep it stealth. Staying a little stealthy, I love it. Really excited for you. I love what you're doing. It's really starting to come into focus. And so congratulations. You know, you got a ways to go, but Tobias and Craig, appreciate you coming on The Cube today. And thank you for watching this Cube Conversation. This is Dave Vellante. We'll see you next time. (upbeat outro music)

>> Hello and welcome to the session featuring CarMax, driving efficiency and innovation with Ansible. I'm your host Christian Emery. I've been at CarMax for over 18 years in several roles ranging from operations to engineering. And in my current role, I'm responsible for CarMax's private cloud and continuous integration, continuous delivery pipelines. Now, my journey with automation started many years ago when I was a Unix and a Linux admin. Day after day, there was always that routine of manual tasks and processes like backups and routine maintenance. Each tasks had a lot of value to the business, but also required consistency, reliability and completion, and demanded quality for system stability. However, it was really boring to carry out the same thing every day. And personally I had a hunger to do more, bring greater value to the business, and need to realize greater satisfaction through my contributions in my career. And this is where automation came into my life. But before we jump into the presentation, I do want to share a little bit about CarMax. For those who may not know, CarMax has been a unique force in the used car industry since 1993. Through innovation and integrity, we've revolutionized the way people buy and sell used cars. We pride ourselves on the experience we provide our customers and our associates to make it possible. And by changing the way we assist our customers, we've also changed the journey of our associates, providing careers in exciting collaborative work environments. In today's presentation, I'm going to cover the early chapters of the CarMax Ansible story. Topics discussed will highlight business need, why we selected Ansible, rapid adoption and our results. And throughout the presentation, I'm also going to share a lot of thoughts and lessons learned to help you with your automation journey. And while listening to the story, I'd like to challenge you to think about your own business needs, technology challenges, and how your team organizes or organization improves approaches automation. Now in our first year, I was challenged to achieve 5,000 hours in efficiency using Ansible. That was a really intimidating number. But we met the challenge and exceeded it. And since then, we've continued to expand our automation through incremental improvements in everyday work to tackling larger operational challenges like regular changes to the environment, routine upgrades and improved infrastructure delivery. Additionally, we expanded automation adoption across multiple teams. We increased our user and contributor base by over five times. And some of that growth was through organic cross team collaboration. However, the greatest growth we had seen was through hackathons, innovation days where we're able to actively collaborate with other teams using Ansible to solve a business problem. And across all those users, we crossed over 15,000 hours of efficiency gain. And I use that term efficiency gained as a measurement to show not only just labor savings, but also tell the story behind other work we accomplished. And keep in mind, this is work that we wouldn't have been able to achieve without automation. And through that user base and hours of efficiency realized, we implemented over 150,000 successful changes. So how do we get there? Earlier I told you about my personal interest in automation and how I've carried that into my current role. And as a leader, I challenge my team to standardize processes and automate as much as possible. We started initially with really repetitive tasks, much like a game of whack-a-mole, but more importantly, through our experimentation, we quickly found we could get better and more consistent results. We soon applied the same approach to our automation for even greater success. But before Ansible, we started to run into issues where team members were taking a more siloed approach to the work. And in an early retrospective, we came to realize that there is a need for a bigger picture mindset. And from that point on, we agreed to standards to increase quality in our code. However, we still occasionally ran into quality issues. Some of these challenges were from homegrown technology, lack of integration and general infrastructure. Now, this is all compounded by the fact that we were using different scripting and programming languages, and not everyone on the team was familiar with Python when compared to say Bash or PowerShell. And while our homegrown solutions made a difference, we thought there could be better ways to meet that demand from the business to do more, better and faster. But like most things in technology, there's always a different tool and approach to get something done. However, some of these other tools required agents on servers making a deployment, a major effort on its own. And additionally, the learning curve was steeper for systems admins and engineers that don't have as much development experience. But this is where Ansible came into the picture. It was easy to use with human readable code. It was an agentless solution allowing us to get started without as much ramp up time. We also liked the fact that it was built on an open standard and a growing user community with an increasing engagement base from partner in vendor integrators. Even better, it had an API we could use to integrate our other platforms as needed. Most recently with the introduction of Ansible collections, we can use community content with greater focus on our automation while worrying less about building new tools. Now, once we select an Ansible as our automation platform, we took a three part approach to implementation and building a foundation for its use. And as I discuss each of these areas, I just like you to consider how to best prepare your teams or organizations for using Ansible. And while planning the transformation, be sure to identify any sort of constraints, roadblocks, and how you plan to measure those results. People, arguably people are the most important part of the equation. You can have all the processes and ways to measure return, but at the end of the day, you need your teams to make that work happen. Start by asking yourself, how well does the team handle change? Are there resource challenges with aligning people and work? Do the people have the right level of knowledge? Do they need training? And how do you start with one team to quickly begin or expand automation? Processes, documentation, standards. Processes are those great ingredients for success in any technology organization. How well are your existing processes documented? Are there any sort of defined standards methods to approaching work? What about your environments? How well does your organization handle executing processes or changes? And lastly, technology. We always need to show results for our investments and technology can help us show that math. Does your organization use metrics and measurements to track progress and results? How do you define or measure success for a project? How should return on investment be measured or quantified? Like I mentioned before, I can't stress it enough, your people, your teams are the most important part of implementing Ansible. They'll be responsible for implementing and developing, maintaining the platform as well as following standards to execute that transformation. And to be successful, they need to have tools, environments, and knowledge. But one of the great things about Ansible is its comparatively easy learning curve. Ansible playbooks are written in a human readable markup language. And I found that most systems admins and engineers are able to pick up Ansible relatively quickly. And for our adoption, some folks were able to pick it up and begin development, while others were a little bit more comfortable and confident with just a little bit of training. Now, Ansible also democratizes technology, freeing up admins and engineers from traditional OS defined silos. Additionally, Ansible playbooks can be consumed by teams without explicit knowledge of the systems or the underlying technology. That's only if a playbook is well written and returns consistent results each time. For us, we first used Ansible to improve our delivery and reduce repeatable manual tasks. Then we turned our attention to shifting left self-service and we're now focused on enabling developers by getting out of the way. These improvements afforded our teams more time to deliver new capabilities to the business. But another benefit to that is teams were able to devote more time to learning and experimenting. When teams first started automating, there's always that impulse or need to go after that biggest win. I would always caution folks to start simple, find small wins to build that experience. These incremental gains are going to feel small, but they quickly add up over time. And as you're going to see, the work should always be done in those smaller increments to return value faster while allowing the ability to quickly make corrections or change course all together. Now, another huge benefit of using that smaller code increment is reuse. These smaller building blocks can and will be used time and time again, reducing future development efforts. And as we quickly learned, one of the best places to start with automation are documented processes. Each step in a process is already documented, it's a huge opportunity to convert it to code and step through those manual processes. And at CarMax, one of the first places we started out was our server checklist process. The process was really thorough, had over a hundred steps to validate systems, make sure they have the right configuration security and specs for each build. And while that process really gave us good consistent results, it was time consuming. It was also prone to human error. But once we automated each of those steps in validations, we were able to turn our focus to the next bottleneck in the process to speed up delivery. And this is why it's always important to strive for quality through consistent predictable results. Automation is just another tool to help make that vision a reality. And when working with teams, it's also important to understand development best practices, keep it simple, and always use version control with code. Better yet, if you're from an ops background, I'd say partner with your development teams to help with this part of the journey. And lastly, when it comes to integrations between platforms and systems, use a modular design, be flexible because technology changes, and over time, so are your integrations. And when it comes to Ansible or just automation in general, there's always that need for efficiency, consistency, reliability, and flexible integrations. And to make this become a reality, you really need to take both a low tech and a high tech approach. If you recall earlier, I mentioned starting with documented processes. That low tech road involves using process mapping value stream analysis tools where you lay out processes end to end to determine the amount of time it takes to execute a process. These processes can be mapped out using whiteboard, sticky notes or by software tools. And from there, more importantly, you can visualize the process bottlenecks and the areas of improvement should be pretty visible. So for CarMax, what we did was we mapped out our infrastructure delivery. We found it was a huge opportunity. But it was also an area we were more comfortable automating given our deep knowledge of the process. So years ago, when we started the process, our time to deliver virtual environments was about two days. Fast forward to now, we can consistently deliver the same infrastructure in just minutes. And in turn, we reuse portions of that process and code for OS refreshes, virtual machine rehydration, system recovery and hypervisor upgrades, just to name a few. And by freeing up team members to do more knowledge work and spend less time on operations, we're able to pivot more resources on the team to align with the business on strategic initiatives. Team members also had more time to do training, research and development for new capabilities, and other areas for future innovation. Now, Ansible gave us a tool where we need to think more like a DevOps organization. And admittedly, a lot of what I've talked about so far has been very operation centric, but systems engineers were all of a sudden writing a testing code, building tools, delivering infrastructure via code, pipelines and API integrations. And as a result, we instantly had to build and strengthen the collaborative relationship between traditional development and operations teams, we had to break down those silos. But the developers appreciate it because they can focus on developing code and not necessarily worry about environments being ready in time or configured correctly. Conversely, operations teams can be focused more on improvements, new capabilities, and spending less time on firefighting. But regardless of the outcomes, you need data to tell that story. And these data elements can start with the hard numbers from reduced cycle times when we were mapping out processes, you can use delivery and SLA metrics. Those were some easy go to numbers. But also consider how you tell that efficiency story. And remember, ROI isn't always about money or the time savings. So as an example, metrics we used included the number of teams using the platform, active contributors, workflows, processes run, and efficiency gain calculations. And as we evolve our journey, the metrics may change along with that story that we need to tell. So to recap, at CarMax, we put people first and you should too. Think about the resources and knowledge your teams are going to need to be successful. And like I said earlier, remember to start small, reuse code as much as possible. This is going to help teams realize faster return on their efforts and start that snowball effect where gains quickly compound over time. Have a vision and decide on targeted outcomes for your team or organization. Then build ROI metrics to help tell that story. But a big part of innovation is experimenting and learning from mistakes. So take a chance, try something new. And in closing, I'd like to thank you for your time. I sincerely hope our results and lessons learned will help you on your automation journey wherever it takes you.

>> From theCUBE Studios in Palo Alto in Boston, bringing you data driven insights from theCUBE and ETR. This is breaking analysis with Dave Vellante. >> In June of this year, Snowflake filed a confidential document suggesting that it would do an IPO. Now of course, everybody knows about it, found out about it and it had a $20 billion valuation. So, many in the community and the investment community and so forth are excited about this IPO. It could be the hottest one of the year, and we're getting a number of questions from investors and practitioners and the entire Wiki bond, ETR and CUBE community. So, welcome everybody. This is Dave Vellante. This is "CUBE Insights" powered by ETR. In this breaking analysis, we're going to unpack five critical questions around Snowflake's IPO or pending IPO. And with me to discuss that is Erik Bradley. He's the Chief Engagement Strategists at ETR and he's also the Managing Director of VENN. Erik, thanks for coming on and great to see you as always. >> Great to see you too. Always enjoy being on the show. Thank you. >> Now for those of you don't know Erik, VENN is a roundtable that he hosts and he brings in CIOs, IT practitioners, CSOs, data experts and they have an open and frank conversation, but it's private to ETR clients. But they know who the individual is, what their role is, what their title is, et cetera and it's a kind of an ask me anything. And I participated in one of them this past week. Outstanding. And we're going to share with you some of that. But let's bring up the agenda slide if we can here. And these are really some of the questions that we're getting from investors and others in the community. There's really five areas that we want to address. The first is what's happening in this enterprise data warehouse marketplace? The second thing is kind of a one area. What about the legacy EDW players like Oracle and Teradata and Netezza? The third question we get a lot is can Snowflake compete with the big cloud players? Amazon, Google, Microsoft. I mean they're right there in the heart, in the thick of things there. And then what about that multi-cloud strategy? Is that viable? How much of a differentiator is that? And then we get a lot of questions on the TAM. Meaning the total available market. How big is that market? Does it justify the valuation for Snowflake? Now, Erik, you've been doing this now. You've run a couple VENNs, you've been following this, you've done some other work that you've done with Eagle Alpha. What's your, just your initial sort of takeaway from all this work that you've been doing. >> Yeah, sure. So my first take on Snowflake was about two and a half years ago. I actually hosted them for one of my VENN interviews and my initial thought was impressed. So impressed. They were talking at the time about their ability to kind of make ease of use of a multi-cloud strategy. At the time although I was impressed, I did not expect the growth and the hyper growth that we have seen now. But, looking at the company in its current iteration, I understand where the hype is coming from. I mean, it's 12 and a half billion private valuation in the last round. The least confidential IPO (laughs) anyone's ever seen (Dave laughs) with a 15 to $20 billion valuation coming out, which is more than Teradata, Margo and Cloudera combined. It's a great question. So obviously the success to this point is warranted, but we need to see what they're going to be able to do next. So I think the agenda you laid out is a great one and I'm looking forward to getting into some of those details. >> So let's start with what's happening in the marketplace and let's pull up a slide that I very much love to use. It's the classic X-Y. On the vertical axis here we show net score. And remember folks, net score is an indicator of spending momentum. ETR every quarter does like a clockwork survey where they're asking people, "Essentially are you spending more or less?" They subtract the less from the more and comes up with a net score. It's more complicated than, but like NPS, it's a very simple and reliable methodology. That's the vertical axis. And the horizontal axis is what's called market share. Market share is the pervasiveness within the data set. So it's calculated by the number of mentions of the vendor divided by the number of mentions within that sector. And what we're showing here is the EDW sector. And we've pulled out a few companies that I want to talk about. So the big three, obviously Microsoft, AWS and Google. And you can see Microsoft has a huge presence far to the right. AWS, very, very strong. A lot of Redshift in there. And then they're pretty high on the vertical axis. And then Google, not as much share, but very solid in that. Close to 60% net score. And then you can see above all of them from a vertical standpoint is Snowflake with a 77.5% net score. You can see them in the upper right there in the green. One of the highest Erik in the entire data set. So, let's start with some sort of initial comments on the big guys and Snowflakes. Your thoughts? >> Sure. Just first of all to comment on the data, what we're showing there is just the data warehousing sector, but Snowflake's actual net score is that high amongst the entire universe that we follow. Their data strength is unprecedented and we have forward-looking spending intention. So this bodes very well for them. Now, what you did say very accurately is there's a difference between their spending intentions on a net revenue level compared to AWS, Microsoft. There no one's saying that this is an apples-to-apples comparison when it comes to actual revenue. So we have to be very cognizant of that. There is domination (laughs) quite frankly from AWS and from Azure. And Snowflake is a necessary component for them not only to help facilitate a multi-cloud, but look what's happening right now in the US Congress, right? We have these tech leaders being grilled on their actual dominance. And one of the main concerns they have is the amount of data that they're collecting. So I think the environment is right to have another player like this. I think Snowflake really has a lot of longevity and our data is supporting that. And the commentary that we hear from our end users, the people that take the survey are supporting that as well. >> Okay, and then let's stay on this X-Y slide for a moment. I want to just pull out a couple of other comments here, because one of the questions we're asking is Whither, the legacy EDW players. So we've got in here, IBM, Oracle, you can see Teradata and then Hortonworks and MapR. We're going to talk a little bit about Hortonworks 'cause it's now Cloudera. We're going to talk a little bit about Hadoop and some of the data lakes. So you can see there they don't have nearly the net score momentum. Oracle obviously has a huge install base and is investing quite frankly in R&D and do an Exadata and it has its own cloud. So, it's got a lock on it's customers and if it keeps investing and adding value, it's not going away. IBM with Netezza, there's really been some questions around their commitment to that base. And I know that a lot of the folks in the VENNs that we've talked to Erik have said, "Well, we're replacing Netezza." Frank Slootman has been very vocal about going after Teradata. And then we're going to talk a little bit about the Hadoop space. But, can you summarize for us your thoughts in your research and the commentary from your community, what's going on with the legacy guys? Are these guys cooked? Can they hang on? What's your take? >> Sure. We focus on this quite a bit actually. So, I'm going to talk about it from the data perspective first, and then we'll go into some of the commentary and the panel. You even joined one yesterday. You know that it was touched upon. But, first on the data side, what we're noticing and capturing is a widening bifurcation between these cloud native and the legacy on-prem. It is undeniable. There is nothing that you can really refute. The data is concrete and it is getting worse. That gap is getting wider and wider and wider. Now, the one thing I will say is, nobody's going to rip out their legacy applications tomorrow. It takes years and years. So when you look at Teradata, right? Their market cap's only 2 billion, 2.3 billion. How much revenue growth do they need to stay where they are? Not much, right? No one's expecting them to grow 20%, which is what you're seeing on the left side of that screen. So when you look at the legacy versus the cloud native, there is very clear direction of what's happening. The one thing I would note from the data perspective is if you switched from net score or adoptions and you went to flat spending, you suddenly see Oracle and Teradata move over to that left a little bit, because again what I'm trying to say is I don't think they're going to catch up. No, but also don't think they're going away tomorrow. That these have large install bases, they have relationships. Now to kind of get into what you were saying about each particular one, IBM, they shut down Netezza. They shut it down and then they brought it back to life. How does that make you feel if you're the head of data architecture or you're DevOps and you're trying to build an application for a large company? I'm not going back to that. There's absolutely no way. Teradata on the other hand is known to be incredibly stable. They are known to just not fail. If you need to kind of re-architect or you do a migration, they work. Teradata also has a lot of compliance built in. So if you're a financials, if you have a regulated business or industry, there's still some data sets that you're not going to move up to the cloud. Whether it's a PII compliance or financial reasons, some of that stuff is still going to live on-prem. So Teradata is still has a very good niche. And from what we're hearing from our panels, then this is a direct quote if you don't mind me looking off screen for one second. But this is a great one. Basically said, "Teradata is the only one from the legacy camp who is putting up a fight and not giving up." Basically from a CIO perspective, the rest of them aren't an option anymore. But Teradata is still fighting and that's great to hear. They have their own data as a service offering and listen, they're a small market cap compared to these other companies we're talking about. But, to summarize, the data is very clear. There is a widening bifurcation between the two camps. I do not think legacy will catch up. I think all net new workloads are moving to data as a service, moving to cloud native, moving to hosted, but there are still going to be some existing legacy on-prem applications that will be supported with these older databases. And of those, Oracle and Teradata are still viable options. >> I totally agree with you and my colleague David Floyd is actually quite high on Teradata Vantage because he really does believe that a key component, we're going to talk about the TAM in a minute, but a key component of the TAM he believes must include the on-premises workloads. And Frank Slootman has been very clear, "We're not doing on-prem, we're not doing this halfway house." And so that's an opportunity for companies like Teradata, certainly Oracle I would put it in that camp is putting up a fight. Vertica is another one. They're very small, but another one that's sort of battling it out from the old NPP world. But that's great. Let's go into some of the specifics. Let's bring up here some of the specific commentary that we've curated here from the roundtables. I'm going to go through these and then ask you to comment. The first one is just, I mean, people are obviously very excited about Snowflake. It's easy to use, the whole thing zero to Snowflake in 90 minutes, but Snowflake is synonymous with cloud-native data warehousing. There are no equals. We heard that a lot from your VENN panelist. >> We certainly did. There was even more euphoria around Snowflake than I expected when we started hosting these series of data warehousing panels. And this particular gentleman that said that happens to be the global head of data architecture for a fortune 100 financials company. And you mentioned earlier that we did a report alongside Eagle Alpha. And we noticed that among fortune 100 companies that are also using the big three public cloud companies, Snowflake is growing market share faster than anyone else. They are positioned in a way where even if you're aligned with Azure, even if you're aligned with AWS, if you're a large company, they are gaining share right now. So that particular gentleman's comments was very interesting. He also made a comment that said, "Snowflake is the person who championed the idea that data warehousing is not dead yet. Use that old monthly Python line and you're not dead yet." And back in the day where the Hadoop came along and the data lakes turned into a data swamp and everyone said, "We don't need warehousing anymore." Well, that turned out to be a head fake, right? Hadoop was an interesting technology, but it's a complex technology. And it ended up not really working the way people want it. I think Snowflake came in at that point at an opportune time and said, "No, data warehousing isn't dead. We just have to separate the compute from the storage layer and look at what I can do. That increases flexibility, security. It gives you that ability to run across multi-cloud." So honestly the commentary has been nothing but positive. We can get into some of the commentary about people thinking that there's competition catching up to what they do, but there is no doubt that right now Snowflake is the name when it comes to data as a service. >> The other thing we heard a lot was ETL is going to get completely disrupted, you sort of embedded ETL. You heard one panelist say, "Well, it's interesting to see that guys like Informatica are talking about how fast they can run inside a Snowflake." But Snowflake is making that easy. That data prep is sort of part of the package. And so that does not bode well for ETL vendors. >> It does not, right? So ETL is a legacy of on-prem databases and even when Hadoop came along, it still needed that extra layer to kind of work with the data. But this is really, really disrupting them. Now the Snowflake's credit, they partner well. All the ETL players are partnered with Snowflake, they're trying to play nice with them, but the writings on the wall as more and more of this application and workloads move to the cloud, you don't need the ETL layer. Now, obviously that's going to affect their talent and Informatica the most. We had a recent comment that said, this was a CIO who basically said, "The most telling thing about the ETL players right now is every time you speak to them, all they talk about is how they work in a Snowflake architecture." That's their only metric that they talk about right now. And he said, "That's very telling." That he basically used it as it's their existential identity to be part of Snowflake. If they're not, they don't exist anymore. So it was interesting to have sort of a philosophical comment brought up in one of my roundtables. But that's how important playing nice and finding a niche within this new data as a service is for ETL, but to be quite honest, they might be going the same way of, "Okay, let's figure out our niche on these still the on-prem workloads that are still there." I think over time we might see them maybe as an M&A possibility, whether it's Snowflake or one of these new up and comers, kind of bring them in and sort of take some of the technology that's useful and layer it in. But as a large market cap, solo existing niche, I just don't know how long ETL is for this world. >> Now, yeah. I mean, you're right that if it wasn't for the marketing, they're not fighting fashion. But >> No. >> really there're some challenges there. Now, there were some contrarians in the panel and they signaled some potential icebergs ahead. And I guarantee you're going to see this in Snowflake's Red Herring when we actually get it. Like we're going to see all the risks. One of the comments, I'll mention the two and then we can talk about it. "Their engineering advantage will fade over time." Essentially we're saying that people are going to copycat and we've seen that. And the other point is, "Hey, we might see some similar things that happened to Hadoop." The public cloud players giving away these offerings at zero cost. Essentially marginal cost of adding another service is near zero. So the cloud players will use their heft to compete. Your thoughts? >> Yeah, first of all one of the reasons I love doing panels, right? Because we had three gentlemen on this panel that all had nothing but wonderful things to say. But you always get one. And this particular person is a CTO of a well known online public travel agency. We'll put it that way. And he said, "I'm going to be the contrarian here. I have seven different technologies from private companies that do the same thing that I'm evaluating." So that's the pressure from behind, right? The technology, they're going to catch up. Right now Snowflake has the best engineering which interestingly enough they took a lot of that engineering from IBM and Teradata if you actually go back and look at it, which was brought up in our panel as well. He said, "However, the engineering will catch up. They always do." Now from the other side they're getting squeezed because the big cloud players just say, "Hey, we can do this too. I can bundle it with all the other services I'm giving you and I can squeeze your pay. Pretty much give it a waive at the cost." So I do think that there is a very valid concern. When you come out with a $20 billion IPO evaluation, you need to warrant that. And when you see competitive pressures from both sides, from private emerging technologies and from the more dominant public cloud players, you're going to get squeezed there a little bit. And if pricing gets squeezed, it's going to be very, very important for Snowflake to continue to innovate. That comment you brought up about possibly being the next Cloudera was certainly the best sound bite that I got. And I'm going to use it as Clickbait in future articles, because I think everyone who starts looking to buy a Snowflake stock and they see that, they're going to need to take a look. But I would take that with a grain of salt. I don't think that's happening anytime soon, but what that particular CTO was referring to was if you don't innovate, the technology itself will become commoditized. And he believes that this technology will become commoditized. So therefore Snowflake has to continue to innovate. They have to find other layers to bring in. Whether that's through their massive war chest of cash they're about to have and M&A, whether that's them buying analytics company, whether that's them buying an ETL layer, finding a way to provide more value as they move forward is going to be very important for them to justify this valuation going forward. >> And I want to comment on that. The Cloudera, Hortonworks, MapRs, Hadoop, et cetera. I mean, there are dramatic differences obviously. I mean, that whole space was so hard, very difficult to stand up. You needed science project guys and lab coats to do it. It was very services intensive. As well companies like Cloudera had to fund all these open source projects and it really squeezed their R&D. I think Snowflake is much more focused and you mentioned some of the background of their engineers, of course Oracle guys as well. However, you will see Amazon's going to trot out a ton of customers using their RA3 managed storage and their flash. I think it's the DC two piece. They have a ton of action in the marketplace because it's just so easy. It's interesting one of the comments, you asked this yesterday, was with regard to separating compute from storage, which of course it's Snowflakes they basically invented it, it was one of their climbs to fame. The comment was what AWS has done to separate compute from storage for Redshift is largely a bolt on. Which I thought that was an interesting comment. I've had some other comments. My friend George Gilbert said, "Hey, despite claims to the contrary, AWS still hasn't separated storage from compute. What they have is really primitive." We got to dig into that some more, but you're seeing some data points that suggest there's copycatting going on. May not be as functional, but at the same time, Erik, like I was saying good enough is maybe good enough in this space. >> Yeah, and especially with the enterprise, right? You see what Microsoft has done. Their technology is not as good as all the niche players, but it's good enough and I already have a Microsoft license. So, (laughs) you know why am I going to move off of it. But I want to get back to the comment you mentioned too about that particular gentleman who made that comment about RedShift, their separation is really more of a bolt on than a true offering. It's interesting because I know who these people are behind the scenes and he has a very strong relationship with AWS. So it was interesting to me that in the panel yesterday he said he switched from Redshift to Snowflake because of that and some other functionality issues. So there is no doubt from the end users that are buying this. And he's again a fortune 100 financial organization. Not the same one we mentioned. That's a different one. But again, a fortune 100 well known financials organization. He switched from AWS to Snowflake. So there is no doubt that right now they have the technological lead. And when you look at our ETR data platform, we have that adoption reasoning slide that you show. When you look at the number one reason that people are adopting Snowflake is their feature set of technological lead. They have that lead now. They have to maintain it. Now, another thing to bring up on this to think about is when you have large data sets like this, and as we're moving forward, you need to have machine learning capabilities layered into it, right? So they need to make sure that they're playing nicely with that. And now you could go open source with the Apache suite, but Google is doing so well with BigQuery and so well with their machine learning aspects. And although they don't speak enterprise well, they don't sell to the enterprise well, that's changing. I think they're somebody to really keep an eye on because their machine learning capabilities that are layered into the BigQuery are impressive. Now, of course, Microsoft Azure has Databricks. They're layering that in, but this is an area where I think you're going to see maybe what's next. You have to have machine learning capabilities out of the box if you're going to do data as a service. Right now Snowflake doesn't really have that. Some of the other ones do. So I had one of my guest panelist basically say to me, because of that, they ended up going with Google BigQuery because he was able to run a machine learning algorithm within hours of getting set up. Within hours. And he said that that kind of capability out of the box is what people are going to have to use going forward. So that's another thing we should dive into a little bit more. >> Let's get into that right now. Let's bring up the next slide which shows net score. Remember this is spending momentum across the major cloud players and plus Snowflake. So you've got Snowflake on the left, Google, AWS and Microsoft. And it's showing three survey timeframes last October, April 20, which is right in the middle of the pandemic. And then the most recent survey which has just taken place this month in July. And you can see Snowflake very, very high scores. Actually improving from the last October survey. Google, lower net scores, but still very strong. Want to come back to that and pick up on your comments. AWS dipping a little bit. I think what's happening here, we saw this yesterday with AWS's results. 30% growth. Awesome. Slight miss on the revenue side for AWS, but look, I mean massive. And they're so exposed to so many industries. So some of their industries have been pretty hard hit. Microsoft pretty interesting. A little softness there. But one of the things I wanted to pick up on Erik, when you're talking about Google and BigQuery and it's ML out of the box was what we heard from a lot of the VENN participants. There's no question about it that Google technically I would say is one of Snowflake's biggest competitors because it's cloud native. Remember >> Yep. >> AWS did a license one time. License deal with PowerShell and had a sort of refactor the thing to be cloud native. And of course we know what's happening with Microsoft. They basically were on-prem and then they put stuff in the cloud and then all the updates happen in the cloud. And then they pushed to on-prem. But they have that what Frank Slootman calls that halfway house, but BigQuery no question technically is very, very solid. But again, you see Snowflake right now anyway outpacing these guys in terms of momentum. >> Snowflake is out outpacing everyone (laughs) across our entire survey universe. It really is impressive to see. And one of the things that they have going for them is they can connect all three. It's that multi-cloud ability, right? That portability that they bring to you is such an important piece for today's modern CIO as data architects. They don't want vendor lock-in. They are afraid of vendor lock-in. And this ability to make their data portable and to do that with ease and the flexibility that they offer is a huge advantage right now. However, I think you're a hundred percent right. Google has been so focused on the engineering side and never really focusing on the enterprise sales side. That is why they're playing catch up. I think they can catch up. They're bringing in some really important enterprise salespeople with experience. They're starting to learn how to talk to enterprise, how to sell, how to support. And nobody can really doubt their engineering. How many open sources have they given us, right? They invented Kubernetes and the entire container space. No one's really going to compete with them on that side if they learn how to sell it and support it. Yeah, right now they're behind. They're a distant third. Don't get me wrong. From a pure hosted ability, AWS is number one. Microsoft is yours. Sometimes it looks like it's number one, but you have to recognize that a lot of that is because of simply they're hosted 365. It's a SAS app. It's not a true cloud type of infrastructure as a service. But Google is a distant third, but their technology is really, really great. And their ability to catch up is there. And like you said, in the panels we were hearing a lot about their machine learning capability is right out of the box. And that's where this is going. What's the point of having this huge data if you're not going to be supporting it on new application architecture. And all of those applications require machine learning. >> Awesome. So we're. And I totally agree with what you're saying about Google. They just don't have it figured out how to sell the enterprise yet. And a hundred percent AWS has the best cloud. I mean, hands down. But a very, very competitive market as we heard yesterday in front of Congress. Now we're on the point about, can Snowflake compete with the big cloud players? I want to show one more data point. So let's bring up, this is the same chart as we showed before, but it's new adoptions. And this is really telling. >> Yeah. >> You can see Snowflake with 34% in the yellow, new adoptions, down yes from previous surveys, but still significantly higher than the other players. Interesting to see Google showing momentum on new adoptions, AWS down on new adoptions. And again, exposed to a lot of industries that have been hard hit. And Microsoft actually quite low on new adoption. So this is very impressive for Snowflake. And I want to talk about the multi-cloud strategy now Erik. This came up a lot. The VENN participants who are sort of fans of Snowflake said three things: It was really the flexibility, the security which is really interesting to me. And a lot of that had to do with the flexibility. The ability to easily set up roles and not have to waste a lot of time wrangling. And then the third was multi-cloud. And that was really something that came through heavily in the VENN. Didn't it? >> It really did. And again, I think it just comes down to, I don't think you can ever overstate how afraid these guys are of vendor lock-in. They can't have it. They don't want it. And it's best practice to make sure your sensitive information is being kind of spread out a little bit. We all know that people don't trust Bezos. So if you're in certain industries, you're not going to use AWS at all, right? So yeah, this ability to have your data portability through multi-cloud is the number one reason I think people start looking at Snowflake. And to go to your point about the adoptions, it's very telling and it bodes well for them going forward. Most of the things that we're seeing right now are net new workloads. So let's go again back to the legacy side that we were talking about, the Teradatas, IBMs, Oracles. They still have the monolithic applications and the data that needs to support that, right? Like an old ERP type of thing. But anyone who's now building a new application, bringing something new to market, it's all net new workloads. There is no net new workload that is going to go to SAP or IBM. It's not going to happen. The net new workloads are going to the cloud. And that's why when you switch from net score to adoption, you see Snowflake really stand out because this is about new adoption for net new workloads. And that's really where they're driving everything. So I would just say that as this continues, as data as a service continues, I think Snowflake's only going to gain more and more share for all the reasons you stated. Now get back to your comment about security. I was shocked by that. I really was. I did not expect these guys to say, "Oh, no. Snowflake enterprise security not a concern." So two panels ago, a gentleman from a fortune 100 financials said, "Listen, it's very difficult to get us to sign off on something for security. Snowflake is past it, it is enterprise ready, and we are going full steam ahead." Once they got that go ahead, there was no turning back. We gave it to our DevOps guys, we gave it to everyone and said, "Run with it." So, when a company that's big, I believe their fortune rank is 28. (laughs) So when a company that big says, "Yeah, you've got the green light. That we were okay with the internal compliance aspect, we're okay with the security aspect, this gives us multi-cloud portability, this gives us flexibility, ease of use." Honestly there's a really long runway ahead for Snowflake. >> Yeah, so the big question I have around the multi-cloud piece and I totally and I've been on record saying, "Look, if you're going looking for an agnostic multi-cloud, you're probably not going to go with the cloud vendor." (laughs) But I've also said that I think multi-cloud to date anyway has largely been a symptom as opposed to a strategy, but that's changing. But to your point about lock-in and also I think people are maybe looking at doing things across clouds, but I think that certainly it expands Snowflake's TAM and we're going to talk about that because they support multiple clouds and they're going to be the best at that. That's a mandate for them. The question I have is how much of complex joining are you going to be doing across clouds? And is that something that is just going to be too latency intensive? Is that really Snowflake's expertise? You're really trying to build that data layer. You're probably going to maybe use some kind of Postgres database for that. >> Right. >> I don't know. I need to dig into that, but that would be an opportunity from a TAM standpoint. I just don't know how real that is. >> Yeah, unfortunately I'm going to just be honest with this one. I don't think I have great expertise there and I wouldn't want to lead anyone a wrong direction. But from what I've heard from some of my VENN interview subjects, this is happening. So the data portability needs to be agnostic to the cloud. I do think that when you're saying, are there going to be real complex kind of workloads and applications? Yes, the answer is yes. And I think a lot of that has to do with some of the container architecture as well, right? If I can just pull data from one spot, spin it up for as long as I need and then just get rid of that container, that ethereal layer of compute. It doesn't matter where the cloud lies. It really doesn't. I do think that multi-cloud is the way of the future. I know that the container workloads right now in the enterprise are still very small. I've heard people say like, "Yeah, I'm kicking the tires. We got 5%." That's going to grow. And if Snowflake can make themselves an integral part of that, then yes. I think that's one of those things where, I remember the guy said, "Snowflake has to continue to innovate. They have to find a way to grow this TAM." This is an area where they can do so. I think you're right about that, but as far as my expertise, on this one I'm going to be honest with you and say, I don't want to answer incorrectly. So you and I need to dig in a little bit on this one. >> Yeah, as it relates to question four, what's the viability of Snowflake's multi-cloud strategy? I'll say unquestionably supporting multiple clouds, very viable. Whether or not portability across clouds, multi-cloud joins, et cetera, TBD. So we'll keep digging into that. The last thing I want to focus on here is the last question, does Snowflake's TAM justify its $20 billion valuation? And you think about the data pipeline. You go from data acquisition to data prep. I mean, that really is where Snowflake shines. And then of course there's analysis. You've got to bring in EMI or AI and ML tools. That's not Snowflake's strength. And then you're obviously preparing that, serving that up to the business, visualization. So there's potential adjacencies that they could get into that they may or may not decide to. But so we put together this next chart which is kind of the TAM expansion opportunity. And I just want to briefly go through it. We published this stuff so you can go and look at all the fine print, but it's kind of starts with the data lake disruption. You called it data swamp before. The Hadoop no schema on, right? Basically the ROI of Hadoop became reduction of investment as my friend Abby Meadow would say. But so they're kind of disrupting that data lake which really was a failure. And then really going after that enterprise data warehouse which is kind of I have it here as a 10 billion. It's actually bigger than that. It's probably more like a $20 billion market. I'll update this slide. And then really what Snowflake is trying to do is be data as a service. A data layer across data stores, across clouds, really make it easy to ingest and prepare data and then serve the business with insights. And then ultimately this huge TAM around automated decision making, real-time analytics, automated business processes. I mean, that is potentially an enormous market. We got a couple of hundred billion. I mean, just huge. Your thoughts on their TAM? >> I agree. I'm not worried about their TAM and one of the reasons why as I mentioned before, they are coming out with a whole lot of cash. (laughs) This is going to be a red hot IPO. They are going to have a lot of money to spend. And look at their management team. Who is leading the way? A very successful, wise, intelligent, acquisitive type of CEO. I think there is going to be M&A activity, and I believe that M&A activity is going to be 100% for the mindset of growing their TAM. The entire world is moving to data as a service. So let's take as a backdrop. I'm going to go back to the panel we did yesterday. The first question we asked was, there was an understanding or a theory that when the virus pandemic hit, people wouldn't be taking on any sort of net new architecture. They're like, "Okay, I have Teradata, I have IBM. Let's just make sure the lights are on. Let's stick with it." Every single person I've asked, they're just now eight different experts, said to us, "Oh, no. Oh, no, no." There is the virus pandemic, the shift from work from home. Everything we're seeing right now has only accelerated and advanced our data as a service strategy in the cloud. We are building for scale, adopting cloud for data initiatives. So, across the board they have a great backdrop. So that's going to only continue, right? This is very new. We're in the early innings of this. So for their TAM, that's great because that's the core of what they do. Now on top of it you mentioned the type of things about, yeah, right now they don't have great machine learning. That could easily be acquired and built in. Right now they don't have an analytics layer. I for one would love to see these guys talk to Alteryx. Alteryx is red hot. We're seeing great data and great feedback on them. If they could do that business intelligence, that analytics layer on top of it, the entire suite as a service, I mean, come on. (laughs) Their TAM is expanding in my opinion. >> Yeah, your point about their leadership is right on. And I interviewed Frank Slootman right in the heart of the pandemic >> So impressed. >> and he said, "I'm investing in engineering almost sight unseen. More circumspect around sales." But I will caution people. That a lot of people I think see what Slootman did with ServiceNow. And he came into ServiceNow. I have to tell you. It was they didn't have their unit economics right, they didn't have their sales model and marketing model. He cleaned that up. Took it from 120 million to 1.2 billion and really did an amazing job. People are looking for a repeat here. This is a totally different situation. ServiceNow drove a truck through BMCs install base and with IT help desk and then created this brilliant TAM expansion. Let's learn and expand model. This is much different here. And Slootman also told me that he's a situational CEO. He doesn't have a playbook. And so that's what is most impressive and interesting about this. He's now up against the biggest competitors in the world: AWS, Google and Microsoft and dozens of other smaller startups that have raised a lot of money. Look at the company like Yellowbrick. They've raised I don't know $180 million. They've got a great team. Google, IBM, et cetera. So it's going to be really, really fun to watch. I'm super excited, Erik, but I'll tell you the data right now suggest they've got a great tailwind and if they can continue to execute, this is going to be really fun to watch. >> Yeah, certainly. I mean, when you come out and you are as impressive as Snowflake is, you get a target on your back. There's no doubt about it, right? So we said that they basically created the data as a service. That's going to invite competition. There's no doubt about it. And Yellowbrick is one that came up in the panel yesterday about one of our CIOs were doing a proof of concept with them. We had about seven others mentioned as well that are startups that are in this space. However, none of them despite their great valuation and their great funding are going to have the kind of money and the market lead that Slootman is going to have which Snowflake has as this comes out. And what we're seeing in Congress right now with some antitrust scrutiny around the large data that's being collected by AWS as your Google, I'm not going to bet against this guy either. Right now I think he's got a lot of opportunity, there's a lot of additional layers and because he can basically develop this as a suite service, I think there's a lot of great opportunity ahead for this company. >> Yeah, and I guarantee that he understands well that customer acquisition cost and the lifetime value of the customer, the retention rates. Those are all things that he and Mike Scarpelli, his CFO learned at ServiceNow. Not learned, perfected. (Erik laughs) Well Erik, really great conversation, awesome data. It's always a pleasure having you on. Thank you so much, my friend. I really appreciate it. >> I appreciate talking to you too. We'll do it again soon. And stay safe everyone out there. >> All right, and thank you for watching everybody this episode of "CUBE Insights" powered by ETR. This is Dave Vellante, and we'll see you next time. (soft music)

>> Narrator: From theCUBE Studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Hello everyone, welcome to this special CUBE interview. We are here at theCUBE Virtual covering AWS Summit Virtual Online. This is Amazon's Summits that they normally do all around the world. They're doing them now virtually. We are here in the Palo Alto COVID-19 quarantine crew getting all the interviews here with a special guest, Vice President of Machine Learning, we have Swami, CUBE Alumni, who's been involved in not only the machine learning, but all of the major activity around AWS around how machine learning's evolved, and all the services around machine learning workflows from transcribe, recognition, you name it. Swami, you've been at the helm for many years, and we've also chatted about that before. Welcome to the virtual CUBE covering AWS Summit. >> Hey, pleasure to be here, John. >> Great to see you. I know times are tough. Everything okay at Amazon? You guys are certainly cloud scaled, not too unfamiliar of working remotely. You do a lot of travel, but what's it like now for you guys right now? >> We're actually doing well. We have been I mean, this many of, we are working hard to make sure we continue to serve our customers. Even from their site, we have done, yeah, we had taken measures to prepare, and we are confident that we will be able to meet customer demands per capacity during this time. So we're also helping customers to react quickly and nimbly, current challenges, yeah. Various examples from amazing startups working in this area to reorganize themselves to serve customer. We can talk about that common layer. >> Large scale, you guys have done a great job and fun watching and chronicling the journey of AWS, as it now goes to a whole 'nother level with the post pandemic were expecting even more surge in everything from VPNs, workspaces, you name it, and all these workloads are going to be under a lot of pressure to do more and more value. You've been at the heart of one of the key areas, which is the tooling, and the scale around machine learning workflows. And this is where customers are really trying to figure out what are the adequate tools? How do my teams effectively deploy machine learning? Because now, more than ever, the data is going to start flowing in as virtualization, if you will, of life, is happening. We're going to be in a hybrid world with life. We're going to be online most of the time. And I think COVID-19 has proven that this new trajectory of virtualization, virtual work, applications are going to have to flex, and adjust, and scale, and be reinvented. This is a key thing. What's going on with machine learning, what's new? Tell us what are you guys doing right now. >> Yeah, I see now, in AWS, we offer broadest-- (poor audio capture obscures speech) All the way from like expert practitioners, we offer our frameworks and infrastructure layer support for all popular frameworks from like TensorFlow, Apache MXNet, and PyTorch, PowerShell, (poor audio capture obscures speech) custom chips like inference share. And then, for aspiring ML developers, who want to build their own custom machine learning models, we're actually building, we offer SageMaker, which is our end-to-end machine learning service that makes it easy for customers to be able to build, train, tune, and debug machine learning models, and it is one of our fastest growing machine learning services, and many startups and enterprises are starting to standardize their machine learning building on it. And then, the final tier is geared towards actually application developers, who did not want to go into model-building, just want an easy API to build capabilities to transcribe, run voice recognition, and so forth. And I wanted to talk about one of the new capabilities we are about to launch, enterprise search called Kendra, and-- >> So actually, so just from a news standpoint, that's GA now, that's being announced at the Summit. >> Yeah. >> That was a big hit at re:Invent, Kendra. >> Yeah. >> A lot of buzz! It's available. >> Yep, so I'm excited to say that Kendra is our new machine learning powered, highly accurate enterprise search service that has been made generally available. And if you look at what Kendra is, we have actually reimagined the traditional enterprise search service, which has historically been an underserved market segment, so to speak. If you look at it, on the public search, on the web search front, it is a relatively well-served area, whereas the enterprise search has been an area where data in enterprise, there are a huge amount of data silos, that is spread in file systems, SharePoint, or Salesforce, or various other areas. And deploying a traditional search index has always that even simple persons like when there's an ID desk open or when what is the security policy, or so forth. These kind of things have been historically, people have to find within an enterprise, let alone if I'm actually in a material science company or so forth like what 3M was trying to do. Enable collaboration of researchers spread across the world, to search their experiment archives and so forth. It has been super hard for them to be able to things, and this is one of those areas where Kendra has enabled the new, of course, where Kendra is a deep learning powered search service for enterprises, which breaks down data silos, and collects actually data across various things all the way from S3, or file system, or SharePoint, and various other data sources, and uses state-of-art NLP techniques to be able to actually index them, and then, you can query using natural language queries such as like when there's my ID desk-scoping, and the answer, it won't just give you a bunch of random, right? It'll tell you it opens at 8:30 a.m. in the morning. >> Yeah. >> Or what is the credit card cashback returns for my corporate credit card? It won't give you like a long list of links related to it. Instead it'll give you answer to be 2%. So it's that much highly accurate. (poor audio capture obscures speech) >> People who have been in the enterprise search or data business know how hard this is. And it is super, it's been a super hard problem, the old in the old guard models because databases were limiting to schemas and whatnot. Now, you have a data-driven world, and this becomes interesting. I think the big takeaway I took away from Kendra was not only the new kind of discovery navigation that's possible, in terms of low latency, getting relevant content, but it's really the under-the-covers impact, and I think I'd like to get your perspective on this because this has been an active conversation inside the community, in cloud scale, which is data silos have been a problem. People have had built these data silos, and they really talk about breaking them down but it's really again hard, there's legacy problems, and well, applications that are tied to them. How do I break my silos down? Or how do I leverage either silos? So I think you guys really solve a problem here around data silos and scale. >> Yeah. >> So talk about the data silos. And then, I'm going to follow up and get your take on the kind of size of of data, megabytes, petabytes, I mean, talk about data silos, and the scale behind it. >> Perfect, so if you look at actually how to set up something like a Kendra search cluster, even as simple as from your Management Console in the AWS, you'll be able to point Kendra to various data sources, such as Amazon S3, or SharePoint, and Salesforce, and various others. And say, these are kind of data I want to index. And Kendra automatically pulls in this data, index these using its deep learning and NLP models, and then, automatically builds a corpus. Then, I, as in user of the search index, can actually start querying it using natural language, and don't have to worry where it comes from, and Kendra takes care of things like access control, and it uses finely-tuned machine learning algorithms under the hood to understand the context of natural language query and return the most relevant. I'll give a real-world example of some of the field customers who are using Kendra. For instance, if you take a look at 3M, 3M is using Kendra to support search, support its material science R&D by enabling natural language search of their expansive repositories of past research documents that may be relevant to a new product. Imagine what this does to a company like 3M. Instead of researchers who are spread around the world, repeating the same experiments on material research over and over again, now, their engineers and researchers will allow everybody to quickly search through documents. And they can innovate faster instead of trying to literally reinvent the wheel all the time. So it is better acceleration to the market. Even we are in this situation, one of the interesting work that you might be interested in is the Semantic Scholar team at Allen Institute for AI, recently opened up what is a repository of scientific research called COVID-19 Open Research Dataset. These are expert research articles. (poor audio capture obscures speech) And now, the index is using Kendra, and it helps scientists, academics, and technologists to quickly find information in a sea of scientific literature. So you can even ask questions like, "Hey, how different is convalescent plasma "treatment compared to a vaccine?" And various in that question and Kendra automatically understand the context, and gets the summary answer to these questions for the customers, so. And this is one of the things where when we talk about breaking the data silos, it takes care of getting back the data, and putting it in a central location. Understanding the context behind each of these documents, and then, being able to also then, quickly answer the queries of customers using simple query natural language as well. >> So what's the scale? Talk about the scale behind this. What's the scale numbers? What are you guys seeing? I see you guys always do a good job, I've run a great announcement, and then following up with general availability, which means I know you've got some customers using it. What are we talking about in terms of scales? Petabytes, can you give some insight into the kind of data scale you're talking about here? >> So the nice thing about Kendra is it is easily linearly scalable. So I, as a developer, I can keep adding more and more data, and that is it linearly scales to whatever scale our customers want. So and that is one of the underpinnings of Kendra search engine. So this is where even if you see like customers like PricewaterhouseCoopers is using Kendra to power its regulatory application to help customers search through regulatory information quickly and easily. So instead of sifting through hundreds of pages of documents manually to answer certain questions, now, Kendra allows them to answer natural language question. I'll give another example, which is speaks to the scale. One is Baker Tilly, a leading advisory, tax, and assurance firm, is using Kendra to index documents. Compared to a traditional SharePoint-based full-text search, now, they are using Kendra to quickly search product manuals and so forth. And they're able to get answers up to 10x faster. Look at that kind of impact what Kendra has, being able to index vast amount of data, with in a linearly scalable fashion, keep adding in the order of terabytes, and keep going, and being able to search 10x faster than traditional, I mean traditional keyword search based algorithm is actually a big deal for these customers. They're very excited. >> So what is the main problem that you're solving with Kendra? What's the use case? If I'm the customer, what's my problem that you're solving? Is it just response to data, whether it's a call center, or support, or is it an app? I mean, what's the main focus that you guys came out? What was the vector of problem that you're solving here? >> So when we talked to customers before we started building Kendra, one of the things that constantly came back for us was that they wanted the same ease of use and the ability to search the world wide web, and customers like us to search within an enterprise. So it can be in the form of like an internal search to search within like the HR documents or internal wiki pages and so forth, or it can be to search like internal technical documentation or the public documentation to help the contact centers or is it the external search in terms of customer support and so forth, or to enable collaboration by sharing knowledge base and so forth. So each of these is really dissected. Why is this a problem? Why is it not being solved by traditional search techniques? One of the things that became obvious was that unlike the external world where the web pages are linked that easily with very well-defined structure, internal world is very messy within an enterprise. The documents are put in a SharePoint, or in a file system, or in a storage service like S3, or on naturally, tell-stores or Box, or various other things. And what really customers wanted was a system which knows how to actually pull the data from various these data silos, still understand the access control behind this, and enforce them in the search. And then, understand the real data behind it, and not just do simple keyword search, so that we can build remarkable search service that really answers queries in a natural language. And this has been the theme, premise of Kendra, and this is what had started to resonate with our customers. I talked with some of the other examples even in areas like contact centers. For instance, Magellan Health is using Kendra for its contact centers. So they are able to seamlessly tie like member, provider, or client specific information with other inside information about health care to its agents so that they can quickly resolve the call. Or it can be on internally to do things like external search as well. So very satisfied client. >> So you guys took the basic concept of discovery navigation, which is the consumer web, find what you're looking for as fast as possible, but also took advantage of building intelligence around understanding all the nuances and configuration, schemas, access, under the covers and allowing things to be discovered in a new way. So you basically makes data be discoverable, and then, provide an interface. >> Yeah. >> For discovery and navigation. So it's a broad use cat, then. >> Right, yeah that's sounds somewhat right except we did one thing more. We actually understood not just, we didn't just do discovery and also made it easy for people to find the information but they are sifting through like terabytes or hundreds of terabytes of internal documentation. Sometimes, one other things that happens is throwing a bunch of hundreds of links to these documents is not good enough. For instance, if I'm actually trying to find out for instance, what is the ALS marker in an health care setting, and for a particular research project, then, I don't want to actually sift through like thousands of links. Instead, I want to be able to correctly pinpoint which document contains answer to it. So that is the final element, which is to really understand the context behind each and every document using natural language processing techniques so that you not only find discover the information that is relevant but you also get like highly accurate possible precise answers to some of your questions. >> Well, that's great stuff, big fan. I was really liking the announcement of Kendra. Congratulations on the GA of that. We'll make some room on our CUBE Virtual site for your team to put more Kendra information up. I think it's fascinating. I think that's going to be the beginning of how the world changes, where this, this certainly with the voice activation and API-based applications integrating this in. I just see a ton of activity that this is going to have a lot of headroom. So appreciate that. The other thing I want to get to while I have you here is the news around the augmented artificial intelligence has been brought out as well. >> Yeah. >> So the GA of that is out. You guys are GA-ing everything, which is right on track with your cadence of AWS laws, I'd say. What is this about? Give us the headline story. What's the main thing to pay attention to of the GA? What have you learned? What's the learning curve, what's the results? >> So augmented artificial intelligence service, I called it A2I but Amazon A2I service, we made it generally available. And it is a very unique service that makes it easy for developers to augment human intelligence with machine learning predictions. And this is historically, has been a very challenging problem. We look at, so let me take a step back and explain the general idea behind it. You look at any developer building a machine learning application, there are use cases where even actually in 99% accuracy in machine learning is not going to be good enough to directly use that result as the response to back to the customer. Instead, you want to be able to augment that with human intelligence to make sure, hey, if my machine learning model is returning, saying hey, my confidence interval for this prediction is less than 70%, I would like it to be augmented with human intelligence. Then, A2I makes it super easy for customers to be, developers to use actually, a human reviewer workflow that comes in between. So then, I can actually send it either to the public pool using Mechanical Turk, where we have more than 500,000 Turkers, or I can use a private workflow as a vendor workflow. So now, A2I seamlessly integrates with our Textract, Rekognition, or SageMaker custom models. So now, for instance, NHS is integrated A2I with Textract, so that, and they are building these document processing workflows. The areas where the machine learning model confidence load is not as high, they will be able augment that with their human reviewer workflows so that they can actually build in highly accurate document processing workflow as well. So this, we think is a powerful capability. >> So this really kind of gets to what I've been feeling in some of the stuff we worked with you guys on our machine learning piece. It's hard for companies to hire machine learning people. This has been a real challenge. So I like this idea of human augmentation because humans and machines have to have that relationship, and if you build good abstraction layers, and you abstract away the complexity, which is what you guys do, and that's the vision of cloud, then, you're going to need to have that relationship solidified. So at what point do you think we're going to be ready for theCUBE team, or any customer that doesn't have the or can't find a machine learning person? Or may not want to pay the wages that's required? I mean it's hard to find a machine learning engineer, and when does the data science piece come in with visualization, the spectrum of pure computer science, math, machine learning guru to full end user productivity? Machine learning is where you guys are doing a lot of work. Can you just share your opinion on that evolution of where we are on that? Because people want to get to the point where they don't have to hire machine learning folks. >> Yeah. >> And have that kind support too. >> If you look at the history of technology, I actually always believe that many of these highly disruptive technology started as a way that it is available only to experts, and then, they quickly go through the cycles, where it becomes almost common place. I'll give an example with something totally outside the IT space. Let's take photography. I think more than probably 150 years ago, the first professional camera was invented, and built like three to four years still actually take a really good picture. And there were only very few expert photographers in the world. And then, fast forward to time where we are now, now, even my five-year-old daughter takes actually very good portraits, and actually gives it as a gift to her mom for Mother's Day. So now, if you look at Instagram, everyone is a professional photographer. I kind of think the same thing is about to, it will happen in machine learning too. Compared to 2012, where there were very few deep learning experts, who can really build these amazing applications, now, we are starting to see like tens of thousands of actually customers using machine learning in production in AWS, not just proof of concepts but in production. And this number is rapidly growing. I'll give one example. Internally, if you see Amazon, to aid our entire company to transform and make machine learning as a natural part of the business, six years ago, we started a Machine Learning University. And since then, we have been training all our engineers to take machine learning courses in this ML University, and a year ago, we actually made these coursework available through our Training and Certification platform in AWS, and within 48 hours, more than 100,000 people registered. Think about it, that's like a big all-time record. That's why I always like to believe that developers are always eager to learn, they're very hungry to pick up new technology, and I wouldn't be surprised if four or five years from now, machine learning is kind of becomes a normal feature of the app, the same with databases are, and that becomes less special. If that day happens, then, I would see it as my job is done, so. >> Well, you've got a lot more work to do because I know from the conversations I've been having around this COVID-19 pandemic is it's that there's general consensus and validation that the future got pulled forward, and what used to be an inside industry conversation that we used to have around machine learning and some of the visions that you're talking about has been accelerated on the pace of the new cloud scale, but now that people now recognize that virtual and experiencing it firsthand globally, everyone, there are now going to be an acceleration of applications. So we believe there's going to be a Cambrian explosion of new applications that got to reimagine and reinvent some of the plumbing or abstractions in cloud to deliver new experiences, because the expectations have changed. And I think one of the things we're seeing is that machine learning combined with cloud scale will create a whole new trajectory of a Cambrian explosion of applications. So this has kind of been validated. What's your reaction to that? I mean do you see something similar? What are some of the things that you're seeing as we come into this world, this virtualization of our lives, it's every vertical, it's not one vertical anymore that's maybe moving faster. I think everyone sees the impact. They see where the gaps are in this new reality here. What's your thoughts? >> Yeah, if you see the history from machine learning specifically around deep learning, while the technology is really not new, especially because the early deep learning paper was probably written like almost 30 years ago. And why didn't we see deep learning take us sooner? It is because historically, deep learning technologies have been hungry for computer resources, and hungry for like huge amount of data. And then, the abstractions were not easy enough. As you rightfully pointed out that cloud has come in made it super easy to get like access to huge amount of compute and huge amount of data, and you can literally pay by the hour or by the minute. And with new tools being made available to developers like SageMaker and all the AI services, we are talking about now, there is an explosion of options available that are easy to use for developers that we are starting to see, almost like a huge amount of like innovations starting to pop up. And unlike traditional disruptive technologies, which you usually see crashing in like one or two industry segments, and then, it crosses the chasm, and then goes mainstream, but machine learning, we are starting to see traction almost in like every industry segment, all the way from like in financial sector, where fintech companies like Intuit is using it to forecast its call center volume and then, personalization. In the health care sector, companies like Aidoc are using computer vision to assist radiologists. And then, we are seeing in areas like public sector. NASA has partnered with AWS to use machine learning to do anomaly detection, algorithms to detect solar flares in the space. And yeah, examples are plenty. It is because now, machine learning has become such common place that and almost every industry segment and every CIO is actually already looking at how can they reimagine, and reinvent, and make their customer experience better covered by machine learning. In the same way, Amazon actually asked itself, like eight or 10 years ago, so very exciting. >> Well, you guys continue to do the work, and I agree it's not just machine learning by itself, it's the integration and the perfect storm of elements that have come together at this time. Although pretty disastrous, but I think ultimately, it's going to come out, we're going to come out of this on a whole 'nother trajectory. It's going to be creativity will be emerged. You're going to start seeing really those builders thinking, "Okay hey, I got to get out there. "I can deliver, solve the gaps we are exposed. "Solve the problems, "pre-create new expectations, new experience." I think it's going to be great for software developers. I think it's going to change the computer science field, and it's really bringing the lifestyle aspect of things. Applications have to have a recognition of this convergence, this virtualization of life. >> Yeah. >> The applications are going to have to have that. So and remember virtualization helped Amazon formed the cloud. Maybe, we'll get some new kinds of virtualization, Swami. (laughs) Thanks for coming on, really appreciate it. Always great to see you. Thanks for taking the time. >> Okay, great to see you, John, also. Thank you, thanks again. >> We're with Swami, the Vice President of Machine Learning at AWS. Been on before theCUBE Alumni. Really sharing his insights around what we see around this virtualization, this online event at the Amazon Summit, we're covering with the Virtual CUBE. But as we go forward, more important than ever, the data is going to be important, searching it, finding it, and more importantly, having the humans use it building an application. So theCUBE coverage continues, for AWS Summit Virtual Online, I'm John Furrier, thanks for watching. (enlightening music)

>>Live from Orlando, Florida. It's the cube covering Microsoft ignite brought to you by Cohesity. >>Welcome back everyone to the cubes live coverage of Microsoft ignite. I'm your host, Rebecca Knight, along with my cohost. We are joined by Jeffrey Snuffer. He is a technical fellow, Oh three 65 intelligence substrate at Microsoft. Most famous for being the father of PowerShell and one of the key architects of the window server. Thank you so much for coming on, for returning to the show. Yeah, thanks. It's great to be back. So first of all, define your, you're relatively new to this role, so tell us a little bit about what you're doing and what is the intelligent substrate. >> Yes, so you know, a lot of people get this confused as intelligence substrate. There's all three 65 the Microsoft graph. And when I do, as I say, Hey, the best way to think about this as an analogy to an operating system, operating systems are complex, but at the end of the day, they're really, really simple. >>They only do three things. They manage and protect resources. They provide services for developers, right services, API APIs and common controls. And then they provide a base set of applications and a way to get additional applications. So windows manage, CPU, memory, the services when 32 API eyes and then the applications like the browser, et cetera. So all three 65 can really be viewed as an operating system. Sounds strange. Why? Because most operating systems have been operating systems for devices, an operating system for phone, an operating system for a PC and operating system for a server. This is an operating system for people and organizations. So when we think about those three responsibilities, resources and you know, protecting and managing resources, these are the resources for people in organizations. So it's their identity, their, their emails, their chats, their documents, services for developers. These where there's wind 32 for windows, we have ms graph, that's our public API, but then we have services to be able to create, collaborate and communicate documents and interactions. >>And then the applications are things like teams and outlook, et cetera. And so then, Oh, sorry. Then the substrate, the substrate, sort of at the core of it. That's one of our core services. It is storage and then a set of services to manage that and set of services. So the storage is basically a planetary scale, no sequel data store. So every time you create a chat and email document or whatever, it gets stored in the substrate and then three additional copies are created, one of them at least 250 miles away. That's why our date availability and high availability are one thing. So everything gets stored there and then that allows us to do common services like search against it. Does that make sense, >>Jeffrey? Well, one of the biggest challenge people have is when you learn about something and then it has changed an awful lot. Yeah. I think back to the first time I used Microsoft word, Microsoft Excel, it wasn't connected to the internet exactly. Let alone talking about the era of global scale in AI and all of these things that can do in. So maybe give us a fresh as if I'm a brand new person and I, you know, I don't have the, you know, all of the legacy history with the Microsoft office family. What, what is the new, you know, people O us that you're talking about? >>Yeah. So I like to think of it as a back to the original office 1.0, if you remember the original office 1.0, you'd had word, Excel and PowerPoint. And I like to joke, I say it was integrated with the advanced technology at that day of called cardboard, right? We just took the, the, the floppy disks from each one of those products, put it in a cardboard box and said it's a suite. But then it was a vision to a vision of how things should work together to help the individual. And then after that version one, then we reorganized the organization to have common technology teams. And that's when we started to get common controls, common user experience, et cetera, common file formats. Uh, and then it became a true integrated suite. Same thing happened when we went to the cloud. We had all these products that would have a front end couple to a back end, another front end, couple to a back end, another front end coupled a backend. >>Each one would have one or more SDKs, et cetera. And when we first brought them to the cloud, it was the same sort of thing, integrate it with an offering and a name. But there was a vision there. And then that vision drove the reality. And what we did was we said, Hey, let's figure out how to have a common storage for these things. Common backend, a common way to communicate, a common way to do messaging. And then that took a number of years. But that's what drives this consistency. And so that's why when you go and you say, I would like to search for something, you'll find that term, whether it's in your word documents or it's in your emails or your team chats or anything. It's that commonality that makes it answered question. It >>does. Um, so it's, I think about, you know, the era of collaboration and, you know, there were competitors to Microsoft that came out that were built on the internet and you know, deliver those solutions. So this week we've talked to, we haven't dug deep deep into teams, but everyone we've talked to that's using it, it's like, no, really this is a really great product and almost like, you know, forget about some of the things you might have remembered through some of those iterations and changes and things not working together. You know, teams has been built and is allowing some great collaboration, communication with remote workers, smaller businesses, the likes. So it's tough because especially if you're using one tool and you've gone over to some other tool set, it's like, Oh, I don't, why would I go back to that? But it's a very different, uh, Microsoft productivity suite today than, than we might have used in the past. >>That's exactly correct. And then the, into the, uh, uh, intelligent substrate is this layer of AI on top of the substrate, right? So part of that is search, but then we're also doing natural language processing. So basically imagine you saw a store of file in in a one drive that gets stored in one drive and a workflow gets kicked off and that workflow then goes and analyzes the contents of that file and create search terms, et cetera. So we then have common search and then we've got natural language processing that'll go and find, Hey, what are the key points for that document? How do I summarize that document? So then if you see it somewhere you can say, Oh, show me the file card. And I'll say, here's this document. You don't have to read the whole thing. Here are the three key points about it. >>And so the, this is, so to answer the question, why would a, why would a platform guy be working in office? It turns out that to build this AI infrastructure, it's really sort of a platform play. There's key advances that need to be made in, in AI. But actually when you get involved in AI, what you realize is what we really need is more engineering than more science. We need more science, no doubt about it. But boy, is there a need for engineering? Like I need to figure out how to get three to five to seven orders of magnitude more volume of AI going through the system. So when you talk about these key advances in AI that need to be made in terms of of applying them to O three 65 describe them for us and talk about how they will change the future of work and the way we collaborate with our team members in the way we communicate with our team members and, and in our productivity. >>Yeah. So this is where I get so excited about Microsoft's play, right? Because when I decided at the end of last year that I was gonna make a new change, I had a number of opportunities both inside and outside the company. And so the, the thing that really made me say, this is where I want to go was, well, one, it was most important new technology, AI on our most precious business asset, our customers data. So that was very exciting for really got me over the edge was Microsoft's approach to AI. Microsoft takes a very different approach to AI than our competitors, right? The heart of most AI is trying to figure out you and you to achieve some result. Now our competitors do that to try and get you to click a button to buy an ad or to buy something you don't need or subvert some government that they want subverted, right? >>That's none of our peg objectives. We want to understand you for exactly one reason to make you successful, right? How do we, like in the past, people would throw the rock at Microsoft, say, Oh, you know, when I use Microsoft products, I got to understand the Microsoft org chart. You know, you ship my org chart. What they're really saying is that they have to understand the tools to get their job done. They have to navigate the tools. What we're trying to do is have the tools understand the person to help the person, help that person get their job done. So there's this great show, I think it was called the remains of day today, the movie with Anthony Hopkins, he played a Butler. And in that he did some research and he talked to the Butler of Buckingham palace who'd been there for 50 years and he said the essence of a great Butler is that he makes the room emptier when he enters. >>What's that mean? Well, when the, when someone sits down the magazine that they want, is there, the drink that they want is there. It just, it just all works out. Well, that's not my experience with computers today. I mean, how many times do you, you know, you end up at the end of the day and you're like, your spouse says, what'd you do to you day? You're like, wow, I dunno. I dunno. I'm just exhausted. Well, it shouldn't, doesn't have to be that way. What we want to do is to have the computer understand you, understand your objectives and not have some big splashy AI. It just, Oh, things just work. Oh, I'm coming to this meeting. Oh, the information I need for that meeting is just there. Oh, it prepped me and knew that I had a few minutes. And so it gave me a few minutes where it's a prep and things just flow. And at the end of the, you know, success will be when you end the day with more energy than you start it. Like that's a big tall tale, a big tall effort. But that's where we're going for that. Get stalled. >>Yeah. Well we, we found that the, the word that has summarized this week for us is one that Satya said over and over again and it was trust. So in today's day and age, there's a lot of cynicism and especially looking at big tech companies, you did a presentation talking about AI in social responsibility. You tease out a little bit of it there as to why you believe Microsoft is well intentioned with AI, but maybe share a little bit more about that vision for social responsibility and you know, where we need to go with AI as an industry as a whole. >>Yeah, exactly. So there's kinda two key points. First is I think there's a, a very vast, uh, misunderstanding of the state of AI Kang. It really is best understood as software 2.0 and we've been at software 1.0 for about 75 years and I don't think anybody thinks we're doing a particularly great job at event. I think we've started to make progress starting around the 1990s with the, with the core principles of, of uh, the worldwide web. That's when we started to really make some progress. But we still have lots of world's problems. So we're at software 2.0 we're at the very beginning of the beginning of the beginning. Now here's the point. The innovators set the field, the innovators set the path. And in AI it's important for Microsoft to be one of the key innovators here because of our approach, because we're standing up and saying, wait, there's great promise. >>There's great challenges, right? There are privacy challenges. There's data bias challenges, there's inclusivity challenges. There are things that really need to be addressed by governments, local legislation and global governments. Brad Smith has been particularly vocal on this and the need for a digital, the only way you're going to solve the problem of autonomous killer robots, which is a real thing, is by a digital Geneva convention. We, Microsoft can't solve that. IBM can solve that. Google can't solve that. Governments need to solve that. And so Microsoft is being very proactive in engaging the communities around these problems. For myself, for instance, I've been working with some of the security researchers to say, okay, well, software 2.0 how do you do threat model on machine learning? Nobody knows. Like literally nobody knows. And so we've been working over the course of the last year to produce a taxonomy of attacks. Now this is the initial thing, but it sparks a conversation as we've shown it to various government people and other, uh, competitors. Uh, they're very excited about this, about trying to join this in, to identify the class of attacks. Because once you can understand the class of attacks, then you begin understanding, well, how do I defend against those? But literally it doesn't exist. So, >>so talking about autonomous killer robots, I'm very worried now. So how do you, Jeffrey said you're talking about Microsoft's more measured approach and as you said, you are working with governments and work in reaching out to policy makers and regulators to talk about these things. Maybe unlike some other technology companies that aren't doing that. How do, are you a tech optimist at the end of the day or are you, but does it keep you up at night these, these, Nope. Nope, >>not at all. Not at all. No. I'm a wild Technomic dumbest people like are very pessimistic and I just like, yeah. You know, no. Like, let me give you an example, right? There's this, this thing that says, Oh, an autonomous car turns the corner at a high speed and it has to decide between killing two old man and a and a woman in a baby carriage. Right? And it's wide. This is a Philip philosophic philosophy problem called the trolley problem. Oh, a trolley driver has to pull a switch a, uh, and it was like over a hundred years old in the a hundred plus years that that's upon posited, there's been exactly zero trolley drivers ever put in this position. Just, it's just not an issue. Look, there are real issues. We do have to work these things. I'd say the biggest worry is not these killer robots or the autonomous cars going wild. >>It is complacency. It is overconfidence. It says, Oh, I got something to work. Let's just ship it. Like there's a lot of brittleness in these AI systems, right? Like, Oh, this works and it can be spectacular, but then this is a complete disaster and that's a complete disaster. So how do we get that taxonomy of like, Hey, when do we know when we're done? How do we test these things? How do I have like a, a secure supply chain for the data models as well as the code itself? You know, so. So I think that software one no doubt does not provide us any of the answers to the challenges of software 2.0 but I do believe that software 1.0 and its challenges tell us the areas that we need to apply our, our mindset to. And that's what we're doing. So >>Jeffrey, before we let you go, we do need to get the update on PowerShell. I have to say, ever since I've first talked to you, I feel like more and more when I go to shows, I hear people just talking about how it's helping their career, helping their business and in doing it, I don't know if it's just because you know, it was brought to the front of the mind and it's like, Oh no, I'm used to seeing that car model out there. But can you give us the latest on power shell even though you're no longer in that group? Oh yeah. I continue to meet with them all the time. >>I'm very active in PowerShell. So we took power shell and made a cross platform to run analytics. We've talked about that and I don't know where we were when we talked about that, but basically we sort of did it for our own purposes, right? We need to manage the world's estate and so we want to have a common infrastructure for doing that. And the joke was that the point is like, look, we're not confused. We don't think that the Unix people are going to greet us as liberator's. Like all, thank heavens, you know, I've been dying under this bash and such. Thank God Microsoft came to save us, right? There's no confusion. We'll surprise. We shifted and then the vast majority, the numbers are crazy. How many Linux people are using PowerShell. It's just insane and we don't really understand it. We're out there talking to people, but they just love it. >>So anyway, so PowerShell version seven is coming out. It'll come out officially at the end of the year, beginning of next year, and this really is the tool that then you can use to manage everything. Both windows and Linux. We have parallel for each, so you can do massive scale. But that's the one that really just brings all the pieces together and gains the critical mass. So we're very excited about it. always a scintillating conversation when you come on the show. Thank you so much for coming on. Thank you. I'm Rebecca Knight for Stu Miniman. Stay tuned for more of the cubes live coverage of Microsoft ignite.

>> Announcer: Live from Orlando Florida, it's theCUBE, covering Microsoft Ignite. Brought to you by Cohesity. >> Good morning everyone. You are watching theCUBE's live coverage of Microsoft Ignite 2019 here in Orlando, Florida. I'm your host Rebecca Knight, co-hosting alongside of Stu Miniman. We are joined by Donovan Brown. He is the Principal Cloud Advocate Manager of Methods and Practices Organizations at Microsoft. (laughing) A mouthful of a title. >> Yes. >> Rebecca: We are thrilled to welcome you on. >> Thank you so much. >> You are the man in the black shirt. >> I have been dubbed the man in the black shirt. >> So tell us what that's all about. You're absolutely famous. Whenever we were saying Donovan Brown's going to be here. "The man in the black shirt?" >> Yes. >> So what's that about? >> So it was interesting. The first time I ever got to keynote in an event was in New York in 2015 for Scott Guthrie, the guy who only wears a red shirt. And I remember, I was literally, and this is no exaggeration, wearing this exact black shirt, right, because I bring it with me and I can tell because the tag in the back is worn more than the other black shirts I have just like this one. And I bring this one out for big events because I was in a keynote yesterday and I knew I was going to be on your show today. And I wore it and it looked good on camera. I felt really good. I'm an ex-athlete. We're very superstitious. I'm like I have to wear that shirt in every keynote that I do from now on because if you look further back, you'll see me in blue shirts and all other colored shirts. But from that day forward, it's going to be hard pressed for you to find me on camera on stage without this black shirt on or a black shirt of some type. And there's a really cool story about the black shirt that was. This is what\ I knew it was a thing. So I pack about six or seven black shirts in every luggage. I'm flying overseas to Germany to go Kampf to do a keynote for, I think it was Azure Saturday. Flights were really messed up. they had to check my bag which makes me very uncomfortable because they lose stuff. I'm not too worried about it, it'll be okay. Check my bag, get to Europe. They've been advertising that the black shirt is coming for months and they lose my luggage. And I am now, heart's pounding out of my chest. (laughing) We go to the airport. I'm shopping in the airport because I don't even have luggage. I cannot find a black shirt and I am just thinking this is devastating. How am I going to go to a conference who's been promoting "the black shirt's coming" not wearing a black shirt? And my luggage does not show up. I show up at the event I'm thinking okay, maybe I'll get lucky and the actual conference shirt will be black and then we're all good. I walk in and all I see are white shirts. I'm like this could not be worse. And then now the speakers show up. They're wearing blue shirts, I'm like this cannot be happening. So I'm depressed, I'm walking to the back and everyone's starts saying, "Donovan's here, Donovan's here." And I'm looking to find my polo, my blue polo I'm going to put on. They're like no, no, no, no Donovan. They printed one black shirt just for me. I was like oh my goodness, this is so awesome. So I put the black shirt on, then I put a jacket on over it and I go out and I tell the story of how hard it was to get here, that they lost my luggage, I'm not myself without a black shirt. But this team had my back. And when I unzipped my shirt, the whole place just starts clapping 'cause I'm wearing >> Oh, I love it. >> a black shirt. >> Exactly. So now to be seen without a black shirt is weird. Jessica Dean works for me. We were in Singapore together and it was an off day. So I just wore a normal shirt. She had to take a double take, "Oh no, is that Donovan, my manager "'cause he's not wearing a black shirt?" I don't wear them all the time but if I'm on camera, on stage you're going to see me in a black shirt. >> Rebecca: All right, I like it. >> Well, Donovan, great story. Your team, Methods and Practices makes up a broad spectrum of activities and was relatively recently rebranded. >> Yeah. >> We've talked to some of your team members on theCUBE before, so tell our audience a little bit about the bridges Microsoft's building to help the people. >> Great. No, so that's been great. Originally, I built a team called The League. Right, there's a really small group of just DevOps focused diehards. And we still exist. A matter of fact, we're doing a meet and greet tonight at 4:30 where you can come and meet all five of the original League members. Eventually, I got tasked with a much bigger team. I tell the story. I was in Norway, I went to sleep, I had four direct reports. I literally woke up and I had 20 people reporting to me and I'm like what just happened? And the team's spanned out a lot more than just DevOps. So having it branded as the DevOps Guy doesn't really yield very well for people who aren't diehard DevOps people. And what we feared was, "Donovan there's people who are afraid of DevOps "who now report to you." You can't be that DevOps guy anymore. You have to broaden what you do so that you can actually focus on the IT pros in the world, the modern operations people, the lift and shift with Jeremy, with what Jeramiah's doing for me right, with the lift and shift of workloads . And you still have to own DevOps. So what I did is I pulled back, reduced my direct reports to four and now I have teams underneath me. Abel Wang now runs DevOps. He's going to be the new DevOps guy for me. Jeramiah runs our lift and shift. Rick Klaus or you know the Hat, he runs all my IT Pro and then Emily who's just an amazing speaker for us, runs all of my modern operations. So we span those four big areas right. Modern operations which is sort of like the ops side of DevOps, IT pros which are the low level infrastructure, diehard Windows server admins and then we have DevOps run by Abel which is still, the majority of The League is over there. And then we have obviously the IT pros, modern ops, DevOps and then the left and shift with Jeramiah. >> I'd like to speak a little bit as to why you've got these different groups? How do you share information across the teams but you know really meet customers where they are and help them along 'cause my background's infrastructure. >> Donovan: Sure. >> And that DevOps, was like that religion pounding at you, that absolutely, I mean, I've got a closet full of hoodies but I'm not a developer. Understand? >> Understood. (laughs) It's interesting because when you look at where our customers are today, getting into the cloud is not something you do overnight. It takes lots of steps. You might start with a lift and shift, right? You might start with just adding some Azure in a hybrid scenario to your on-prem scenario. So my IT pros are looking after that group of people that they're still on prem majority, they're trying to dip those toes into the cloud. They want to start using things like file shares or backups or something that they can have, disaster recovery offsite while they're still running the majority of what they're doing on-prem. So there's always an Azure pool to all four of the teams that I actually run. But I need them to take care of where our customers are today and it's not just force them to be where we want them tomorrow and they're not ready to go there. So it's kind of interesting that my team's kind of have every one of those stages of migration from I'm on-prem, do I need to lift and shift do I need to do modern operations, do I need to be doing full-blown DevOps pull all up? So, I think it's a nice group of people that kind of fit the spectrum of where our customers are going to be taking that journey from where they are to enter the cloud. So I love it. >> One of the things you said was getting to the cloud doesn't happen overnight. >> No, it does not. >> Well, you can say that again because there is still a lot of skepticism and reluctance and nervousness. How do you, we talked so much about this digital transformation and technology is not the hard part. It's the people that pose the biggest challenges to actually making it happen. >> Donovan: Right. >> So we're talking about meeting customers where they are in terms of the tools they need. But where do you meet them in terms of where they are just in their approach and their mindset, in terms of their cloud readiness? >> You listen. Believe it or not, you can't just go and tell people something. You need to listen to them, find out what hurts and then start with that one thing is what I tell people. Focus on what hurts the most first. Don't do a big bang change of any type. I think that's a recipe for disaster. There's too many variables that could go wrong. But when I sit down with a customer is like tell me where you are, tell me what hurts, like what are you afraid of? Is it a compliancies? Let me go get you in contact with someone who can tell you about all the comp. We have over 90 certifications on Azure. Let me. whatever your fear is, I bet you I can get you in touch with someone that's going to help you get past that fear. But I don't say just lift, shift, move it all like stop wasting, like no. Let's focus on that one thing. And what you're going to do is you're going to start to build confidence and trust with that customer. And they know that I'm not there just trying to rip and replace you and get out high levels of ACR. I'm trying to succeed with you, right, empower every person in every organization on the planet to achieve more. You do that by teaching them first, by helping them first. You can sell them last, right? You shouldn't have to sell them at all once they trust that what we we're trying to do together is partner with you. I look at every customer more as a partner than a customer, like how can I come with you and we do better things together than either one of us could have done apart. >> You're a cloud psychologist? Almost, right because I always put myself in their position. If I was a customer, what would I want that vendor to do for me? How would they make me feel comfortable and that's the way that I lead. Right, I don't want you going in there selling anything right. We're here to educate them and if we're doing our job on the product side, the answer is going to be obvious that you need to be coming with us to Azure. >> All right. So Donovan, you mentioned you used to be an athlete? >> Donovan: Yes. >> According to your bio, you're still a bit of an athlete. >> Donovan: A little bit, a little bit. >> So there's the professional air hockey thing which has a tie to something going on with the field. Give us a little bit of background. I've got an air hockey table in my basement. Any tips for those of us that aren't, you know? You were ranked 11th in the world. >> At one point, yeah, though I went to the World Championships. It was interesting because that World Championships I wasn't prepared. My wife plays as well. We were like we're just going to go, we're going to support the tournament. We had no expectations whatsoever. Next thing you know, I'm in the round playing for the top 10 in the world. And that's when it got too serious for me and I lost, because I started taking it too serious. I put too much pressure on myself. But professionally, air hockey's like professional foosball or pool. It's grown men taking this sport way too seriously. It's the way I'd describe it. It is not what you see at Chuck E. Cheese. And what was interesting is Damien Brady who works for me found that there is an AI operated air hockey table here on this floor. And my wife was like, oh my gosh, we have to find this machine. Someone tape Donovan playing it. Six seconds later, my first shot I scored it. And I just looked at the poor people who built it and I'm like yeah, I'm a professional air hockey player. This thing is so not ready for professional time but they took down all my information and said we'd love to consult with you. I said I'd love to consult with you too because this could be a lot of fun. Maybe also a great way for professionals to practice, right, because you don't always have someone who's willing to play hours and hours which it takes to get at the professional level. But to have an AI system that I could even teach up my attack, forcing me to play outside of my comfort zone, to try something other than a left wall under or right well over but have to do more cuts because it knows to search for that. I can see a lot of great applications for the professionalized player with this type of AI. It would actually get a lot better. Literally, someone behind me started laughing. "That didn't take long" because it in six seconds I had scored on it already. I'm like okay, I was hoping it was going to be harder than this. >> I'm thinking back to our Dave Cahill interview of AI for everyone, and this is AI for professional air hockey players. >> It is and in one of my demos, Kendra Havens showed AI inside of your IDE. And I remember I tell the story that I remember I started writing software back in the 90s. I remember driving to a software store. You remember we used to have to drive and you'd buy a box and the box would be really heavy because the manuals are in there, and not to mention a stack of floppy discs that you're going to spend hours putting in your computer. And I bought visual C++ 1.52 was my first compiler. I remember going home so excited. And it had like syntax highlighting and that was like this cool new thing and you had all these great breakpoints and line numbers. And now Kendra's on stage typing this repetitives task and then the editor stops her and says, "It looks like you need to do this a little bit more. "You want me to do this for you?" And I'm like what just happened? This is not syntax highlighting. This is literally watching what you do, identifying a repetitive task, seeing the pattern in your code and suggesting that I can finish writing this code for you. It's unbelievable. >> You bring up a great point. Back when I used to write, it was programming. >> Yes. >> And we said programming was you learn the structure, you learn the logic and you write all the lines of what's going to be there. Coding on the other hand usually is taking something that is there, pulling in the pieces, making the modification. >> Right. >> It sounds like we're talking about even the next generation where the intelligence is going to take over. >> It's built right inside of your IDE which is amazing. You were talking about artificial intelligence, not only for the air hockey. But I love the fact that in Azure, we have so many cognitive services and you just like pick these off the shelf. When I wanted to learn artificial intelligence when I was in the university, you had to go for another language called Lisp. That scared half of us away from artificial intelligence because you have to learn another language just to go do this cool thing that back then was very difficult to do and you could barely get it to play chess, let alone play air hockey. But today, cognitive services search, decision-making, chat bots, they're so easy. Anyone, even a non developer, can start adding the power of AI into their products thanks to the stuff that we're doing in Azure. And this is just lighting up all these new possibilities for us, air hockey, drones that are able to put out fires. I've just seen amazing stuff where they're able to use AI and they add it with as little as two lines of code. And all of a sudden, your app is so much more powerful than it was before. >> Donovan, one of the things that really struck me over the last couple years, looking at Microsoft, is it used to be, you'd think about the Microsoft stack. When I think about developers it's like, oh wait are you a .NET person? Well, you're going to be there. The keynote this morning, one of your team members was on stage with Scott Hanselman and was you know choose your language, choose your tools and you're going to have all of them out there. So talk to us a little bit about that transition inside Microsoft. >> Sure. One of the mantras that I've been saying for a while is "any language, any platform". No one believes me . So I had to start proving it. I'm like so I got on stage one year. It was interesting and this is a really rough year because I flew with three laptops. One had Mac OS on it, one of them had Linux on it and one of them had Windows. And what I did is I created a voting app and what I would do is I'd get on stage and say okay everyone that's in this session, go to this URL and start voting. They got to pick what computer I use, they got to pick what language I programmed in and they got to pick where in Azure-eyed I deployed it to. Was it to an app service was it to Docker? I'm like I'm going to prove to you I can do any language in any platform. So I honestly did not know what demo I was going to do. 20 minutes later, after showing them some slides, I would go back to the app and say what did you pick? And I would move that computer in front of me and right there on stage completely create a complete CI/CD pipeline for the language that that audience chose to whatever resources that they wanted on whatever platform that they wanted me. Was like, have I proven this to you enough or not? And I did that demo for an entire year. Any language that you want me to program in and any platform you want me to target, I'm going to do that right now and I don't even know what it's going to be. You're going to choose it for me. I can't remember the last time I did a .NET demo on stage. I did Python this week when I was on stage with Jason Zander. I saw a lot of Python and Go and other demos this year. We love .NET. Don't get us wrong but everyone knows we can .NET. What we're trying to prove right now is that we can do a lot of other things. It does not matter what language you program in. It does not matter where you want to deploy. Microsoft is here to help you. It's a company created by developers and we're still obsessed with developers, not just .NET developers, all developers even the citizen developer which is a developer which is a developer who doesn't have to see the code anymore but wants to be able to add that value to what they're doing in their organization. So if you're a developer, Microsoft is here to help full-stop. It's a powerful mission and a powerful message that you are really empowering everyone here. >> Donovan: Right. >> Excellent. >> And how many developers only program in one language now, right? I thought I remember I used to be a C++ programmer and I thought that was it, right. I knew the best language, I knew the fastest language. And then all of a sudden, I knew CSharp and I knew Java and I knew JavaScript and I brought a lot of PowerShell right now and I write it on and noticed like wow, no one knows one language. But I never leave Visual Studio code. I deploy all my workloads into Azure. I didn't have to change my infrastructure or my tools to switch languages. I just switched languages that fit whatever the problem was that I was trying to solve. So I live the mantra that we tell our customers. I don't just do .NET development. Although I love .NET and it's my go-to language if I'm starting from scratch but sometimes I'm going to go help in an open source project that's written in some other language and I want to be able to help them. With Visual Studio online, we made that extremely easy. I don't even have to set up my development machine anymore. I can only click a link in a GitHub repository and the environment I need will be provisioned for me. I'll use it, check in my commits and then throw it away when I'm done. It's the world of being a developer now and I always giggle 'cause I'm thinking I had to drive to a store and buy my first compiler and now I can have an entire environment in minutes that is ready to rock and roll. It's just I wish I would learn how to program now and not when I was on bulletin boards asking for help and waiting three days for someone to respond. I didn't have Stack Overflow or search engines and things like that. It's just an amazing time to be a developer. >> Yes, indeed. Indeed it is Donovan Brown, the man in the black shirt. Thank you so much for coming on theCUBE. >> My pleasure. Thank you for having me. >> It was really fun. Thank you. >> Take care. >> I'm Rebecca Knight for Stu Miniman. Stay tuned for more of theCUBE's live coverage of Microsoft Ignite. (upbeat music)

>>Live from Las Vegas, you covering splunk.com 19 brought to you by Splunk.. >>Okay. Welcome back everyone. That's the cubes live coverage in Las Vegas for Splunk's dot com user conference 10 years is their anniversary. It's cubes seventh year. I'm John Farah, your host with a great guest here. Joe Partlow, CTO of rely AQuESTT recently on the heels of vying thread care and Marcus, Carrie and team. Congratulations. They'd come on. Yeah. Yeah. It's been a been a fun month. So obviously security. We love it. Let's take a minute to talk about what you guys do. Talk about what your company does that I've got some questions for you. Yeah. So you know, obviously with the increasing cyber threats, uh, you know, uh, security companies had a lot or customers had a lot of tools. Uh, it's easy to get overwhelmed, um, really causes a lot of confusion. So really what we're trying to do is we have a platform called gray matter that is really kind of how we deliver security model management, which what that means is that's bringing together people, process technology in a way that's easy to kind of make sense of all the noise. >>Um, yeah, there's, there a, a lot of features in there that would help monitor the health, uh, the incident response, the hunt, um, any kind of features that you would need from a security. So you guys are a managed service, you said four? >> Yeah. Yeah, a different, a little different than a traditional MSSP. We um, you'll work very close with, uh, the customers. Uh, we work in their environment, we're working side by side with them, uh, in their tools and we're really maturing and getting better visibility in their environment to get that MSSP for newer. >> Right. That's where you guys are. M S S VP >> on steroids. A little bit different. >> Alright. Well you guys got some things going on. You got a partnership with Splunk for the dotcom sock. Oh yeah. Talk about that with set up out here. And what's it showing? Yeah, that's been a great experience. >>Uh, we, we work very close with the Splunk, uh, team. Uh, we monitored Splunk corporate, uh, from a work with skirt team monitoring them. Uh, so when.call came around, it was kind of a natural progression of Hey, uh, you know, Joel and team on their side said, Hey, how do we kind of build up the team and do a little bit extra and I'll see any way that we can help secure.com. Uh, it was really cool. I give credit to the team, both teams, uh, standing up a, uh, new Splunk install, getting everything stood up really in the last few weeks, uh, making sure that every, uh, everybody at the pavilion and the conference in general is protected and we're watching for any kind of threat. >> So it's, it's been great working with the Splunk team. So is that normal procedure that the bad guys want to target? >>The security congresses? This is gonna make a state visit more of graffiti kind of mentality. It's an act kind of lift, fun, malicious endpoints that they want to get out of here. Oh yeah. There's, there's a little bit of a, you know, let's do it for fun and mess with the conference a little bit. So we'll want to make sure that, that that's what happened. So is my end point protected here? My end points, my phone and my laptop. Uh, not the user specific but any of the conference provided demo stations. Okay. So or structure for the equipment, not me personally. You are not monitoring your personal okay. I give up my privacy years ago. Yes. This is a interesting thing to talk about working with spunk because you know, I hear all the time and again we're looking at this from an industry wide perspective. >>I hear we've got a sock, they got a slot. So these socks are popping up yesterday. Operation centers. What is, what is the state of the art for that now? Is it best practice to have a mega Monster's sock or is it distributed, is it decentralized? What's the current thinking around how to deploy Sox surgery operations center or centers? Yeah, we certainly grow with a decentralized model. We need to follow the sun. So we've got operations centers here in Vegas, Tampa and Dublin. Uh, really making sure that we've got the full coverage. Uh, but it is working very close with the Splunk socks. So they've got a phenomenal team and we work with them side by side. Uh, obviously we are providing a lot of the, uh, the tier one, tier two heavy lift, and then we escalate to Splunk team. They're obviously gonna know Splunk corporate better than we will. >>So, uh, we work very close hand in hand. So you guys acquired threat care and Marcus carries now in the office of CTO, which you're running. Yes. How is that going to shape rely a quest and the Europe business? >> Yeah, the acquisition has been extremely, uh, you know, uh, exciting for us. Uh, you know, after meeting Marcus, uh, I've known of Marcus, he's a very positive influence in the community, uh, but having worked with him, the vision for threat care and the vision for Lioncrest really closely aligned. So where we want to take, uh, the future of security testing, testing controls, making sure upstream controls are working, uh, where threats they're wanting to go for. That was very much with what we aligned more so it made sense to partner up. So, uh, very excited about that and I think we will roll that into our gray matter platform has another capability. >>Uh, gray matter, love the name by the way. I mean, first of all, the security companies have the best names or mission control gray matter, you know, red Canary, Canary in the coal mine. All good stuff. All fun. But you know, you guys work hard so I know the price gotta be good. I gotta ask you around the product vision around the customers and how they're looking at security because you know, it's all fun games. They'll, someone's hacking their business trash or this ransomware going on. Data protection has become a big part of it. What are customers telling you right now in terms of their, their fears and aspirations? What do they need? What's on the agenda? Guests for customers right now? Yeah. I think kind of the two biggest fears, um, and then the problems that we're trying to address is one, just a lack of visibility. >>Uh, customers have so many things on their network, a lot of mergers and acquisitions. So, uh, unfortunately with a lot of times the security team is the last one to know when something pops up. Uh, so anything that we can do to increase visibility and that and that, a lot of times we work very closely with Splunk or send that they have out to make sure that it happens. And then the other thing I think is, you know, most people want to get more proactive. Uh, you know, salmon logging by nature is very reactive. So when he tried to get out in front of those threats a little bit more, so anything that we can do to try to get more proactive, uh, may certainly going to be on their, their top of mind. Well, the machine learning toolkits, getting a lot of buzz here at the show, that's a really big deal. >>I think the other thing that I'm seeing I to get your reaction to is this concept of diverse data. That's my word, not Splunk's, but the idea of bringing in more data sets actually helps machine learning that's pretty much known by data geeks, but in making data addressable because data seems to be the one thing that is all doing a lot of the automation that's takes that headway heavy lift and also provides heavy lifting capabilities to set data up to look at stuff. So data is pretty critical. Data addressability data diversity, you got to have the data and it's gotta be addressable in real time and through tools like fabric search and other things. What's your reaction to that and thoughts around that? No, I agree 100%. Uh, you know, obviously most enterprise customers have a diverse set of data. So trying to search across those data sets, normalize that data, it's, it's a huge task. >>Um, but to get the visibility that we need, we really need to be able to search these multiple data sets and bring those into make sense. Whether you're doing threat hunting or responding to alerts. Um, or you need it from a compliance standpoint, being able to deal with those diverse data sets, uh, is is a key key issue. You know, the other thing I wanna get your thoughts on this one that we've been kind of commenting, I've kind of said a ticket position on this gonna from an opinion standpoint, but it's kind of obvious but it's not necessarily true. But my point is with the data volume going up so massive, that puts the tips, the scales and the advantage for the adversaries. Ransomware's a great example of it and you know, as little ransomware now is towns and cities, these ransomware attacks just one little vector, but with the data volume data is the surface area, not just devices. >>Oh yeah. So how is the data piece of it and the adversarial advantage, you think that that makes them stronger, more surface area? Yeah, definitely. And that's something that where we're leaning on machine learning for a lot is if you really kind of make sense of that data, a lot of times you want to baseline that environment and just find it what's normal in the environment, what's not normal. And once you to find that out, then we can start saying, all right, is this malicious or not? Uh, you know, some things that uh, yeah, maybe PowerShell or something and one environment is a huge red flag that Hey, we've been compromised in another one. Hey, that's just a good administrator automating his job. So making sense of that. Um, and then also just the sheer volume of data that we, that we see customers dealing with. >>Very easy to hide in if you're doing an attack, uh, from an adversary standpoint. So being able to see across that and make sure that you can at scale SyFy that data and find actionable event. You guys, I was just talking with a friend that I've known from the cloud, world, cloud native world. We're talking about dev ops versus the security operations and those worlds are coming together. There are more operational things than developer things, but yet CSOs that we talked to are fully investing in developer teams. So it's not so much dev ops dogma, if you will. But we gotta do dev ops, right? You know, see the CIC D pipeline. Okay, I get that. But developers play a critical role in this feature security architecture, but at the end of the day, it's still operations. So this is the new dev ops or sec ops or whatever it's called these days. >>What's your, how, how do customers solve this problem? Because it is operational, whether it's industrial IOT or IOT or cloud native microservices to on premise security practices with end points. I mean, I, the thing we see that, that kind of gets those teams the most success is making sure they're working with those teams. So having security siloed off by itself. Um, I think we've kind of proven in the past that doesn't work right? So get them involved with their development teams, get them involved with their net ops or, or, you know, sec ops teams, making sure they're working together so that security teams can be an enabler. Uh, they don't want to be the, uh, the team that says no to everything. Um, but at the end of the day, you know, most companies are not in the business of security. They're in the business of making widgets or selling widgets or whatever it is. >>So making sure that the security, yeah, yeah, that's an app issue. Exactly. Making sure that they're kind of involved in that life cycle so that, not that they can, you know, define what that needs to be, but at least be aware of, Hey, this is something we need to watch out for or get visibility into and, and keep the process moving. All right. Let's talk about Splunk. Let's set up their role in the enterprise. I'll see enterprise suite 6.0 is a shipping general availability. How are you guys deploying and optimizing Splunk for customers? What are some of the killer use cases that's there and new ones emerging? Yeah, we've, we provide, you know, really kind of three core areas. First one customers, you're one is obviously making sure that the platform is healthy. So a lot of times we'll go into a, a customer that, uh, you know, maybe they, they, there's one team has turned over or they rapidly expanded and, and in a quickly, you kind of overwhelming the system that's there. >>So making sure that the, the architecture is correct, maintained, patched, upgraded, and they're, they're really taking advantage of the power of Splunk. Uh, from an engineering standpoint. Uh, also another key area is building content. So as we were discussing earlier, making sure that we've got the visibility and all that data coming in, we've got to make sure that, okay, are we pursuing that data correctly? Are we creating the appropriate alerts and dashboards and reports and we can see what's going on. Um, and then the last piece is actually taking, you know, see you taking action on that. So, uh, from an incident response standpoint, watching those alerts and watching that content flyer and making sure that we're escalating and working with the customer security team, they'd love to get your thoughts. Final question on the, um, first of all, great, great insight. They'll, I love that. >>As customers who have personal Splunk, we buy our data is number one third party app for blogs work an app, work app workloads, and in cloud as well as more clients than you have rely more on cloud. AWS for instance, they have security hub, they're deploying some of this to lean on cloud providers, hyperscale cloud providers for security, but that doesn't diminish the roles flung place. So there's a lot of people that are debating, well, the cloud is going to eat Splunk's lunch. And so I don't think that's the case. I want to get your thoughts of it because they're symbionic. Oh yeah. So what's your thoughts on the relationship to the cloud providers, to the Splunk customer who's also going to potentially moves to the cloud and have a hybrid cloud environment? Yeah, and now I would agree there's, you know, there are going to exist side by side for a long time. >>Uh, most environments that we see are hybrid environments. While most organizations do have a cloud first initiative, there's still a lot of on premise stuff. So Splunk is still going to be a, a key cornerstone of just getting that data. Where I do see is maybe a, you know, in those platforms, um, kind of stretching the reach of Splunk of, Hey, let's, let's filter and parse this stuff maybe closer to the source and make sure that we're getting the actionable things into our Splunk ES dashboards and things like that so that we can really make sure that we're getting the good stuff. And maybe, you know, the stuff that's not actionable, we're, we've up in our AWS environment. Um, and that's, that's a lot of the technology that Splunk's coming out with. It's able to search those other environments is going to be really key I think for that where you don't have to kind of use up all your licensing and bring that non-actionable data in, but you still able to search across. >>But that doesn't sound like core Splunk services more. That's more of an operational choice there. Less of a core thing. You mentioned that you think splints to sit side by side for the clouds. What, what gives you that insight? What's, what's, uh, what's telling you that that's gonna happen? What's the, yeah, you still need the core functionality of Splunk running with spark provides is a, you know, it's a great way to bring data and it parses it, uh, extremely well. Um, having those, uh, you know, correlate in correlation engines and searches. Um, that's, that's very nice to have that prepackaged doing that from scratch. Uh, you can certainly, there's other tools that can bring data in, but that's a heavy riff to try to recreate the wheel so to speak. We're here with Joe Parlo, CTO, really a quest, a pardon with Splunk setting up this dotcom SOC for the exhibits and all the infrastructure. >>Um, final question, what's the coolest thing going on at dotcom this year? What's, what should customers or geeks look at that's cool and relevant that you think should be top line? Top couple of things. Yeah, I, I, uh, one of the things I like the most out of the keynote was, uh, the whole, uh, Porsche use case with that. The AR augmentation on my pet bear was really, really cool. Um, and then obviously the new features are coming out with, with VFS and some of another pricing model. So definitely exciting time to be a partner of Splunk. Alright, Joe, thanks for them. John furrier here with the cube live in Las Vegas day two of three days of coverage.com. Their 10th year anniversary, our seventh year covering the Silicon angle, the cube. I'm Sean furrier. Thanks for watching. We'll be right back.

>>Live from Las Vegas. That's the Q covering splunk.com 19 brought to you by Splunk. >>You know, kind of leaning on that heavily. Automation, certainly very important. But what does enterprise and what does enterprise security 6.0 bring to the table. So can you take us through the evolution of where you guys are at with, with Splunk, if you want to handle that enterprise security? So yeah, generally enterprise security has traditionally had really, really good use cases for like the external threats that we're talking about. But like you said, it's very difficult to crack the insider threat part. And so we leveraging machine learning toolkit has started to build that into Splunk to make sure that you know, you can protect your data. And, uh, you know, Tyler and I specifically did this because we saw that there was immaturity in the cybersecurity market for insider threat. And so one of the things that we're actually doing in this top, in addition to talking about what we've done, we're actually giving examples of actionable use cases that people can take home and do themselves. >>Like we're giving them an exact sample code of how to find some outliers. They give me an example of what, so the use case that we go over in the talk is a user logs in at a weird time of day outside of their baseline and they exfiltrate a large amount of data in a low and slow fashion. Um, but they're doing this obviously outside of the scope of their normal behavior. So we give some good searches that you can take home and look at how could I make a baseline, how could I establish that there's deviations from that baseline from a statistical standpoint, and identify this in the future and find the needle in the haystack using the machine learning toolkit. And then if I have a sock that I want to send notables to or some sort of some notification to how do we make that happen, how do we make the transition from machine learning toolkit over to enterprise security or however your SOC operates? >>How do you do that? Do you guys write your own code for that? Or you guys use Splunk? So Splunk has a lot of internal tools and there's a couple of things that need to be pointed out of how to make this happen because we're aggregating large amounts of data. We go through a lot of those finer points in the talk, but sending those through to make sure that they're high confidence is the, is the channel you guys are codifying the cross connect from the machine, learning to the other systems. All right, so I've got to ask, this is basically pattern recognition. You want to look at baselining, how do people, can people hide in that baseline data? So like I'll give you, if I'm saying I'm an evil genius, I say, Hey, I knew these guys looking for Romans anomalies in my baseline, so I'm going to go low and slow in my baseline. >>Can you look for that too? Yeah, there are. There absolutely are ways of, fortunately, uh, there's a lot of different people who are doing research in that space on the defensive side. And so there's a ton of use cases to look at and if you aggregate over a long enough period of time, it becomes incredibly hard to hide. And so the baselines that we recommend building generally look at your 90 day or 120 day out. Um, I guess viewpoint. So you really want to be able to measure that. And most insider threat that happen occur within that 30 to 90 day window. And so the research seems to indicate that those timelines will actually work. Now if you were in there and you read all the code and you did all of the work to see how all of the things come through and you really understood the machine learning minded, I'm sure there's absolutely a way to get in if you're that sophisticated. >>But most of the times they just trying to steal stuff and get out or compromise a system. Um, so is there other patterns that you guys have seen in terms of the that are kind of low hanging fruit priorities that people aren't paying attention to and what's the levels of importance to I guess get ahold of or have some sort of mechanism for managing insider threats? I passwords I've seen one but I mean like there's been a lot of recent papers that have come out in lateral movement and privilege escalation. I think it's an area where a lot of people haven't spent enough time doing research. We've looked into models around PowerShell, um, so that we can identify when a user's maliciously executing PowerShell scripts. I think there's stuff that's getting attention now that when it really needs to, but it is a little bit too late. >>Uh, the community is a bit behind the curve on it and see sharks becoming more of a pattern to seeing a lot more C sharp power shells kind of in hunted down kind of crippled or like identified. You can't operate that way, what we're seeing but, but is that an insider and do that. And do insiders come in with the knowledge of doing C sharp? Those are gonna come from the outside. So I mean, what's the sophistic I guess my question is what's the sophistication levels of an insider threat? Depends on the level a, so the cert inside of dread Institute has aggregated about 15,000 different events. And it could be something as simple as a user who goes in with the intent to do something bad. It could be a person who converted from the inside at any level of the enterprise for some reason. >>Or it could be someone who gets, you know, really upset after a bad review. That might be the one person who has access and he's being socially engineered as well as all kinds of different vectors coming in there. And so, you know, in addition to somebody malicious like that, that you know, there's the accidental, you're phishing campaigns here, somebody's important clicks on an email that they think is from somebody else important or something like that. And you know, we're looking fair for that as well. And that's definitely spear fishing's been very successful. That's a hard one to crack. It is. They have that malware and they're looking at, you can say HR data's out of this guy, just got a bad review, good tennis cinema, a resume or a job opening for, and that's got the hidden code built in. We've seen that move many times. >>Yeah, and natural language processing and more importantly, natural language understanding can be used to get a lot of those cases out. If you're ingesting the text of the email data, well you guys are at a very professional high end from Sai C I mean the history of storied history goes way back and a lot of government contracts do. They do a lot of heavy lifting from anywhere from development to running full big time OSS networks. So there's a lot of history there. What does sustain of the yard? What do you guys look at as state of the art right now in security? Given the fact that you have some visibility into some of the bigger contracts relative to endpoint protection or general cyber, what's the current state of the art? What's, what should people be thinking about or what are you guys excited about? What are some of the areas that is state of the art relative to cyber, cyber security around data usage. >>So, I mean, one of the things, and I saw that there were some talks about it, but not natural language processing and sentiment analysis has gotten, has come a long way. It is much easier to understand, you know, or to have machines understand what, what people are trying to say or what they're doing. And especially, for example, if somebody's like web searching history, you know, and you might think of somebody might do a search for how do I hide downloading a file or something like that. And, and that's something that, well, we know immediately as people, but you know, we have, our customer for example, has 1000000001.2 billion events a day. So you know, if the billion, a billion seconds, that's 30 years. Yeah. So like that's, it's, it's a big number. You know, we, we, we hear those numbers thrown around a lot, but it's a big number to put it in perspective. >>So we're getting that a day and so how do we pick out, it's hard to step of that problem. The eight staff, you can't put stamp on that. Most cutting edge papers that have come out recently have been trying to understand the logs. They're having them machine learning to understand the actual logs that are coming in to identify those anomalies. But that's a massive computation problem. It's a huge undertaking to kind of set that up. Uh, so I really have seen a lot of stuff actually at concierge, some of the innovations that they're doing to optimize that because finding the needle in the haystack is obviously difficult. That's the whole challenge. But there's a lot of work that's being done in Splunk to make that happen a lot faster. And there's some work that's being done at the edge. It's not a lot, but the cutting edge is actually logging and looking at every single log that comes in and understanding it and having a robot say, boom, check that one out. >>Yeah. And also the sentiment, it gets better with the data because we all crushed those billions of events. And you can get a, you know, smiley face or that'd be face depending upon what's happening. It could be, Oh this is bad. But this, this comes back down to the data points you mentioned logs is now beyond logs. I've got tracing other, other signals coming in across the networks. So that's not, that's a massive problem. You need automation, you've got to feed the beast by the machines and you got to do it within whatever computation capabilities you have. And I always say it's a moving train hard. The Target's moving all the time. You guys are standing on top of it. Um, what do you guys think of the event? What's the, what's the most important thing happening here@splunk.com this year? I'd love to have both of you guys take away in on that. >>There's a ton of innovation in the machine learning space. All of the pipelines really that I've, I've been working on in the last year are being augmented and improved by the staff. That's developing content in the machine learning and deep learning space that's belongs. So to me that's by far the most important thing. Your, your take on this, um, between the automation. I know in the last year or so, Splunk has just bought a lot of different companies that do a lot of things that now we can, instead of having to build it ourselves or having to go to three or four different people on top to build a complete solution for the federal government or for whoever your customer is, you can, you know, Splunk is becoming more of a one stop shop. And I think just upgrading all of these things to have all the capabilities working together so that, for example, Phantom, Phantom, you know, giving you that orchestration and automation after. >>For example, if we have an EMS notable events saying, Hey, possible insider threat, maybe they automate the first thing of checking, you know, pull immediately pulling those logs and emailing them or putting them in front of the SOC analyst immediately. So that in, in addition to, Hey, you need to check this person out, it's, you need to check this person out here is the first five pages of what you need to look at. Oh, talking about the impact of that because without that soar feature. Okay. The automation orchestration piece of it, security, orchestration and automation piece of it without where are you know, speed. What's the impact? What's the alternative? Yes. So when we're, right now, when we're giving information to our EES or analysts through yes, they look at it and then they have to click five, six, seven times to get up the tabs that they need to make it done. >>And if we can have those tabs pre populated or just have them, you know, either one click or just come up on their screen for once they open it up. I mean their time is important. Especially when we're talking about an insider threat whom might turn to, yeah, the alternative is five X increase in timespan by the SOC analyst and no one wants that. They want to be called vented with the data ready to go. Ready, alert on it. All right, so final few guys are awesome insights. Walking data upsets right here. Love the inside. Love the love the insights. So final question for the folks watching that are Splunk customers who are not as on the cutting edge, as you guys pioneering this field, what advice would you give them? Like if you had to, you know, shake your friend egg, you know, get off your button, do this, do that. What is the, what do people need to pay attention to that's super urgent that you would implore on them? What would you, what would your advice be once you start that one? >>One of the things that I would actually say is, you know, we can code really cool things. We can do really cool things, but one of the most important things that he and I do as part of our processes before we go to the machine and code, the really cool things. We sometimes just step back and talk for a half an hour talk for an hour of, Hey, what are you thinking about? Hey, what is a thing that you know or what are we reading? What and what are we? And you know, formulating a plan because instead of just jumping into it, if you formulate a plan, then you can come up with you know, better things and augmented and implemented versus a smash and grab on the other side of just, all right, here's the thing, let's let's dump it in there. So you're saying is just for you jump in the data pool and start swimming around, take a step back, collaborate with your peers or get some kind of a game thinking plan. >>We spent a lot of hours, white boarding, but I would to to add to that, it's augment that we spent a lot of time reading the scientific research that's being done by a lot of the teams that are out solving these types of problems. And sometimes they come back and say, Hey, we tried this solution and it didn't work. But you can learn from those failures just like you can learn from the successes. So I recommend getting out and reading. There's a ton of literature in that space around cyber. So always be moving. Always be learning. Always be collaborating. Yeah, it's moving training guys, thanks for the insights Epic session here. Thanks for coming on and sharing your knowledge on the cube, the cube. We're already one big data source here for you. All the knowledge here at.com our seventh year, their 10th year is the cubes coverage. I'm John furry with back after this short break.

>>Live from Las Vegas. That's the Q covering splunk.com 19 brought to you by Splunk. >>Hey, welcome back. Every once the Q's live coverage here in Las Vegas for Splunk's dot com 2019 it's Splunk's 10th year having the events, the cubes coverage seven years, the cube independent media company breaking down, extracting the signal from the noise dot on the top people, top experts, tell them the stories that matter. We're here with Mike EG, director of applied research for coming red Canary. Mike, thanks for coming on. I appreciate it. Thank you. So red Canary is a company doing here. What's the focus? What does it company do? Take a minute to explain red County area and why you're here at.com. Sure, thank you. So we are a managed endpoint detection and response organization. We partner with organizations of all sizes to help them eradicate evil, for instance. So we help them with monitoring their environment. We investigate, respond and act on threats or so on the notes here, you guys have a topic session finding titled finding evil is never an accident, how to hunt in bots. >>So using bots, hunting down evil, you guys are out there doing this as a business. What does it mean? What does he, what if, first of all, what is evil and how do you hunt it down? Take us through that Sarah. So the talk is based around the boss of the SOC data set that was released by Splunk. They have version two, version one and version three will be coming out soon and they just released version four here. And so the talks all focused on how to find evil within bots. The three are actually V forum, sorry, the one that just came out. And so what we do as an organization is we help businesses get through their data, kind of like your guys' mission as well. Like get through them all the haystack, find the bad things and present that to our customers in a really fast way. >>So that's kind of where we are today. Archives to find the good content. Great experts like yourself tell about your role. You're like a researcher, but it's not like you're sitting back there applied research we applied means it's not like just making it up, you know the next moonshot, you guys are applied specifically to hunting down evil. That's your role. What does that entail? You guys have to sit back, zoom back, look at the data that the Splunk's providing some benefits with their, they're exposing their data. What does it mean to hunt down? What's, what's the requirements? How do you set that up? What are you looking at you going through day? Those are the dashboards. What are the what? What, what do you deal with and your job? >> Yeah, so like a day to day or like kind of what our team does is we focus on like what's going on previously, what are we seeing in the wild? >>Like what campaigns are happening and then my role within my team is focused on what's coming. So what are, what are red team's working on? What are pen testers looking into? Take that information, begin testing and begin building proof of concepts. Put that back into our products so that whether it's two weeks, six months, two years, we have coverage for it, no matter what. So a of us, a lot of our time is generating proof of concepts on what may be coming. So there's a lot of very unique things that may be in the wild today. And then there's some things that we may never see that are just very novel and kind of once, once, once a time kind of thing. Right? >> So you know, we love talking about data that we've been covering data since 2010 the thing that's interesting and I want to get your thoughts on this because you know, eval has arbitrage built into it. >>They know where to hide. And so the question is, is that what are you looking at matters, right? So the so, so, so there's a lot of exposure. But the question I have for you is, what is the problem that you're solving? Why do you guys exist? Was it because evil was better to adversaries? Were better at hiding? Is it automation can solve patterns they haven't seen yet? Because if you automate something you haven't seen yet, so is it new things? So why, what's the problem statement that you guys are attacking? Yeah. So hit it. It's a lot. There's a lot, there's a lot to inbox. Um, so like in particular in this instance, seeing something that happened yesterday and then what's happening today is actors are working to break process lineage within what's happening on the employee. Because actors know that everything's happening on an employment. >>Yes, there's traffic coming in, but there's execution going on in a single place on that box. So their whole tactic now is to try to break that lineage. So it's not Microsoft word spawning something. It's now Microsoft word opens and as spawns over there off another process, right? So we're here to monitor those types of behaviors. And that's pretty much like the core of red Canary. We've always focused on the end points. We only do emblem implant based products. We don't like monitor networks. We don't monitor firewalls or anything like that. We're very focused, uh, hyper focus on employee behaviors. And so, and that, that's the cool part about our job is we get to see all the really new things that are happening. And if you look at it, these breaches in the past, it's happening on the endpoint and that's probably where we are. >>And actually day the Canary in the coal mines all expression, everyone knows that or if older might know that. But you know, identifying and being that early warning detection system really kind of was the whole purpose of the Canary in the coal mine, red Canary red teams. I'm kind of putting it together. What are some of the things that you've seen that, that as an example of why you exist? Because it, is it new things, is it that, you know, Hey, our known thing or balls, what are some of the examples that you can point to that, that point of why you guys exist? Yeah, sure. Um, a good example is kind of like the looking forward stuff where red team's going, where actor's going. So a lot of them are moving to C sharp and.net Tradecraft, which is very native to the operating system. >>And windows. Um, so if they're doing that, they're moving away from what they're always, what they've been used to the last few years, which is PowerShell. So our sales kind of dead then now we're going to C sharp and.net. So a lot of our focus today is how can we better detect those? And vendors are moving that way too. They're, they're starting to see that they have to evolve their products to the next level order to detect these behaviors. Cause I mean that's, that's the whole reason why a lot of these EDR vendors are here. Right? And, and it's all data like you said. And so feeding it into a Sam or with a Splunk in particular, you're able to correlate those behaviors and look at very specific things and find it real well know. One of the things that a lot of security practitioners and experts and advisors have been looking at over years is data. >>So it's not, it's no secret data and critical. But one of the things that's interesting is that data availability has always been an issue. Sharing data. And then the message here@splunk.com for the 19 is interesting. You've got data diversity now exposure to the fabric search concept there they got accelerated and realtime times too. We've always had that. But as it kind of comes together, they're looking to get more diverse aperture to data. Yup. Is that still an ongoing challenge and what are, cause if you have a blind spot, you only, this is where the potential danger. How do you guys talk about that? What's the narrative around diverse data sets? How to deal with them effectively and then if blind spots exist, what do they look like or how do you figure that out? Yeah, we, so I, I've been with red Canary for over three years, about three years now. >>And one of the things I started at was a technical account manager incident handler. And so I helped a lot of our customers go from, we bought you red Canary to monitor points, but what should we do next? And so we, our incident handling team will come in and assist a customer with, you guys should start going down this road. Like, how are you bringing everything together? How are you analyzing your data down to just operationalizing like some use cases and playbooks within their data. Like you got EDR. Now let's look at your firewalls. How, how rich of that data can be helped enrich what the EDR information like here's the IP address and carbon black response. Where's it going this way on your firewall or your appliance is going out and you know, and things like that. So we have a whole team dedicated to it and that's like the focus of the. >>We took a poll in our, we have a, you know, this acumen operate for 10 years. It's our seventh year squad, Dave and I took a poll of our cube community, um, but 5,000 alumni and we asked them about cloud security, which vendors are the best and Splunk is clearly number one in third party data management. I got him out, he's got a category but cloud security. How should the cloud vendors provide security, Google, AWS and Azure. But outside of the core cloud providers, Splunk's number one, clearly across the board. How is Splunk doing in your mind? How do you guys work with Splunk? What's the dynamic? What's your relationship with Splunk and where Splunk position in your mind? Because as cloud becomes more prevalent with cloud native, born in the cloud and with hybrid there's a unification, not just with data. They have infrastructure operations. >>Yup. So Splunk role and then their future prospects share. Um, so red Canary uses Splunk too. So we, we process I think like 30 terabytes plus of data a day coming to our engine that we built. And that's the kind of like proprietary piece of red Canary. 30 terabytes of data flows through. We use a like a DSL, like a language that sits on top of it, that queries they're looking for those behaviors. We send those tip offs as we call to Splunk and we actually track a lot of the efficiencies of our detectors that way. So we look for how low detectors doing, is it triggering, is that false positives? How many false positives over time. And then also how much time our analysts are spending on those detectors. You know, they get a detector or a in event and they review that event and they're spending 2030 minutes on it and well what's wrong with it? >>Is there something going on here? Do we need to cut something back and fix it? So we use Splunk a lot of, for like the analytics piece of just how our operation works. It's awesome. It's really neat to see >> him for, one of the things that I've been proud of with covering Splunk is we showed them early when they were just started, then they went public. Yeah. Just watching how they've grown. That did a lot of great things. But now the theme is applications on top of Splunk. They're an enabling platform. They had a couple of key pillars. I want you to talk about where you guys fit and where you see the upside. So swamp has the developer area, which is, they have all these deck, new developers, security and compliance and fraud, um, foundations and platform stuff. And then the it ops does this analytics, AI ops, they've got signal FX, cloud native. >>So those are the kind of the four key areas around their apps, their app strategy. Do you guys cut across all those? You are you guys developing? Are you doing all, what's the, what's the red Canary fit into that? Yeah, it seems like you've probably our cross section. Yeah, probably most likely fitting into a few areas within Ed's. My team has developed a couple apps for Splunk, so we've published those. We have like a app that we pushed out. We have a carbon black response app, which we co-developed many years ago. Those things are all out there. We've helped other people with their apps and, but yeah, it's, it's a little mix of everything. And I think the big core thing that we're all looking to today is like how can we use more of the machine learning toolkit with Splunk, um, for our customers and for us internally. >>Like how can we predict things better with it? So there's, there's a lot of little bit of focus of that same thing. In your opinion, B2B out in the field, you mean the front lines, now you're in research, you got that holistic view, you're looking down at the, on the field, the battlefield, if you will, the adversaries will evil out there. What do you look for? I mean, what's the, what's the triggering event for you? How do you know when you need to jump in and get full ready, alert and really kind of sound off that, you know, that Canary alarm saying, Hey, you know, let's take action here or let's kind of like look at that and take us through some of those priorities. What's the, some of the workflow you go through? Yeah, so um, we'll end up either sending a detection to a customer and either they'll trigger like, Hey, can you give us more context around this event that happened? >>Or it will be, we had a pen test, red team, bad thing happen. Can someone else investigate further? And so I'll come in might from my perspective, I'll come in kind of like a, almost like a tier three in a way. We'll come in, we'll do the additional research beyond what our detectors already caught looking for. Many things, you know, did, was there something we missed that we can do better at detecting next time? Is there any new behaviors involved with something drop that you know, that the actor had left within the environment that may have gone by antivirus prevention controls, anything like that. Um, and then also just understanding their trade craft. Right? So we track a lot of teams and disturbed behaviors and we're able to kind of explore and you know, build those you gotta you gotta be on everything. Basically you gotta survey the entire landscape. >>Yep. You come in post event. Yeah. Do the collateral damage analysis and the dead map. That's a really cool thing about like the Splunk boss's a sock data set. Right. And that's where my talks a lot about is it's a very like, basic talk, but it focuses on how to go from beginning to end investigating this big incident that happened. You know, cause when you get an a detection from like in organization you might just find that it was delivered to a word doc, a couple of things executed. But was there something else that happened? Right? And there's like your Canadian Nicole mind piece, right. You know, finding other things that occurred within the organization and helping ideally your data essentially is the foundation for essentially preventative side. So it's, yes, it's kind of a closed loop kind of life cycle of yep. Leverage operating leverage data standpoint. >>Yeah, it's a solid point. We, I coined the term like three years ago called driving, driving prevention with detection. So take all your detection logic and understanding and things you see with products, even EDR Avi, and use that to drive your prevention. So it's just a way that if you're just alerting on everything, take that data and put it into your preventative preventative controls. So Michael got asked you, how is cloud, how is cloud changing the security formulas? Because obviously scale and data are big themes we hear all the time. I mean has been around is not a new thing. But the constant theme that I see in all my cube interviews we've done over the years and this year is the Nord scale comes up, is unprecedented scale, both in data volume, surface area needs for things like red Canary teams to be in there. What do you see with the impact the cloud is it really should change the game in any way? >>He has it's speed as new cloud. It's the speed of new cloud technology that seems to constantly be coming out. Like one day it's Docker, next day it's Coobernetti's and then there's going to be something tomorrow. Right? Like it just constantly changes. So how can vendors keep up with logging, making sure it's the right type of logging and being able to write detection on it or even detect anything out of it. Right. One, the diversity too is a great point. I want to know. Firstly, blogs are great. Yeah, you got tracing. So you have, so there's now different signaling. Yeah. So this app now a new thing that you got to stay on top. Oh, totally. Like look at any, any MSSP, they have thousands of data sources coming in. And now I want you to monitor my Coubernetties cluster that scales horizontally from 100 to 5,000 all day, every day like Netflix or something. >>Right? And I want you to find the bad things in that. It's a lot going on. And this is where machine learning and automation come into play because the observability you need the machine learning. They've got to categorize this. Okay. Again, humans do all this. No, yeah, it takes a machine. I'm using machines with human intelligence in a way, right? So have a human driving the machine to pull out those indicators, those notables. Michael, thanks for coming on. Great insight. Great signal from the noise. You're still distracting there. Great stuff. Final question for that to end the segment. In your opinion, what's the top story in the security industry that needs to be continually told and covered and reported on? >> Ooh, that's, that's a good one. Um, you hear any threats, platform development, new stacks developing. Is there like a one area that you think deep that's the high order bit in terms of like impact? Yeah. I think focus on, I'm going to say point cause that's where everything's executing and everything's happening. Um, and that's the biggest thing that it's only gonna get more challenging with IOT edge and industrial IOT. Yes. The edge is the end point. End points are changing. The definition is changing at exact right stuff coming on from red Canary here in the queue, the Canary in the coal mine. That's the cube. Brand-new. The signal here from.com 19. I'm John furrier back with more after this short break.

from our studios in the heart of Silicon Valley Palo Alto California this is a cute conversation welcome to the cube studios for another cube conversation where we go in-depth with thought leaders driving business outcomes with technology I'm your host Peter Burris every Enterprise that is trying to do digital transformation finds themselves facing two challenges one their digital assets themselves are a source of value and to other assets that are sources of value are becoming increasingly digitized and that creates a lot of challenges a lot of security concerns that bad agents out in the internet are exploiting and requires a programmatic fundamental response to try to ensure that the digital assets or digitized assets aren't mucked with by bad guys so to have that conversation we're here with Tony Jian Domenico Tony's a senior security strategist and a researcher and the CTI lead at Ford NIT Tony welcome back to the cube hey Pete it's great to be here man so as you get to see you yeah well we've been doing this for a couple of years now Tony and so let's get just kick it off what's new so what's new should we start talking about a little bit about the index here what we saw with the overall threat landscape sure well cool so you know y'all like you know like we always do we always like to start off with an overall threat landscape at least they give an overview of what that index looks like and it really consists of malware botnets application exploits and what we looked at over the quarter there was a lot of volatility throughout the quarter but at the end of the day it ended up only 1% higher than the quarter before now some of that volatility really is being driven by what we've talked about a lot of times Peter and a lot of these other episodes is that swarm like activity whenever an actual vulnerability is successfully exploited by an adversary everybody swarms in on that vulnerability and our fertig are labs you see that really like super spike up a great example of that would be in the last year in December think PHP which is an application that's a framework to rapidly develop web apps they had a vulnerability that if you successfully exploited it it would give you remote the remote access or I'm sorry remote code execution and they were exploiting that and we definitely seen a huge uptick now that wasn't the only one for the quarter but that and along with some of the other ones it's really what's kind of driving on volume so the index has been around for a few quarters now and it's a phenomenal way for folks out there to observe how overall trends are evolving but as you said one of the key things that's being discovered is that or you're discovering as you do this research is this notion of swarming it seems as though there ought to be a couple of reasons why that's the case Tony it's it's we've talked about this in the past there's folks who want to get a little bit more creative in creating bad stuff and there's other folks who just want to keep the cost low and just leverage what's out there which approach are the bad guys tend to using more and or is there an approach one of the other approach is more targeted to one or another kind of attack well it's funny you usually see the folks in the cyber crime ecosystem that are really focusing on you know identifying them not so much where they're doing more sort of targeted attacks it's more of a you know pray and spray you know type of thing and you see a lot of that you know anytime they can hire you can get a life of cybercrime right in the leverage some of these common you know you know services you have code reuse you know which is out there so you have that sort of like group there right and then you have more of the you know more of the you know hands-on sort of keyboard the more you know targeted attacks that are really focused on specific you know victims so you have those you know those two groups I say now with that though there kind of is a commonality there where there's this concept and it's nothing new we've been talking about this for years in the cybersecurity industry it's living off the land right where once a victim is on the actual machine itself they start leveraging some of the tools that are already available there and usually these tools their administration tools to be able to minister the actual network but these tools can also be used in the farías ways from example here would be you know PowerShell they you know a lot of admins use PowerShell for efficiencies on the network but that also can be used in the forest ways and the bad guys are using that and then this past quarter you know we did see a lot of PowerShell activity now you know Peter having said that though I think as a whole with the security community we're getting better at being able to identify these types of PowerShell attacks one we got better technology on the endpoint and I think to Microsoft is in a better job of being able to provide us more hardening capabilities for PowerShell like being able to restrict access to PowerShell as well as giving us better logging capability to be able to identify that malicious activity so we are getting better and the bad guys know this so I think what we can probably look for in the future is them leveraging either a different interface or different language because all they really need to do is interface with that dotnet framework which is part of a Windows system and they can start doing the same exact things they were doing with PowerShell and we're seeing that it in the open-source community now things like Silent Trinity open source tool that allows you to do those same things so for C an open source pretty much guarantee we're gonna see it out there in the wild here soon so we've got a group of bad actors that are using this living off the land approach to leverage technology that's out there and we've still got kind of the big guys having to worry about being targeted because you know that's how you make a lot of money if you're successful but it certainly does sound is that a general business practice for a lot of these guys is to leverage common infrastructure and that this common infrastructure is increasingly becoming you know better understood have I got that right no I you know Peter you're spot-on here what we did we did some exploratory research in this last quarter and what we found out is with the exploits within that quarter or or or the axe will come threats sixty percent of those threats are using the same infrastructure what I mean by infrastructure you know I I mean things like you know infrastructure to download malware maybe to redirect you to some other site and then downloads malware and that makes a lot of sense Peter you know why because in this cybercrime ecosystem if you didn't realize this it's a vicious competitive market everybody is trying to sell their wares and they want to make sure that their service is the best it's better than someone else's and they want to make sure that it's stable so they find these you know community you know infrastructures that are tried-and-true you know some of them are from you know bulletproof hosting so you know services you know things of that nature so you see a lot of the folks in a cybercrime ecosystem using them now on the flip side though you definitely see some of the thread actors that are more sort of you know more the advanced threat actors maybe what they want to do is hide a little bit so they'll hide in that larger community to be able to possibly be able to bypass that that attribution back to them because they don't want to be sort of labeled with oh hey this particular thread actor always uses this infrastructure so if they can blend in a lot harder to find them so they can use what is available but at the same time differentiate themselves in this bad actor ecosystem to take on even more challenging the potentially lucrative exploits now tell me if we know something about this common infrastructure as you said sixty percent of these attacks are using this common infrastructure that suggests we can bring a common set of analysis frameworks to bear as we consider who these actors are and what their practices are have I got that right yeah yeah absolutely if you can align your PlayBook defenses with the offensive actual playbook that the threat actors are using they're better off you're gonna be right because then you can be able to combat them a lot better and as a matter of fact I mean we've kind of introduced this sort of concept in conjunction with our our partnership with the cyber threat Alliance we're actually producing these thread actor play books you know and what we're doing is the idea behind this is if we can identify the malicious activity the threat actors are actually doing to complete their cyber mission expose some of them tactics those techniques those procedures we could possibly disrupt some of that malicious activity and you know this past this past quarter here we focused on a group you know Peter called the the silence group and they're really focused on identifying and stealing financial data they're looking at banks banking infrastructure and ATM machines and you'll get a kick out of this with the ATM machines they're doing something called jackpot II where they if they can find the axle software behind the ATM machine find that ATM process they can inject a malicious DLL into that process giving them total control over the ATM machine and now they can dispense money at will and they can have these money mules on the other side receive that actual money so you know we have a lot of different campaigns in play books that we've identified on our website and that once we understand that we align that with our security fabric and ensure that our customers are protected against that particular playbook Tony I'm not happy to hear that so this is this is my distressed face that I use during these types of interviews but it's if if we're able to look at how bad guy play books are operating then we ought to be able to say and what are those fundamentals that a shop should be using the security professionals should be using that are just you know so basic and so consistent and it seems that are you guys have identified three to do a better job of taking a fabric approach that starts to weave together all assets into a more common security framework to to do a better job of micro and macro segmentation so that you can identify where problems are and then finally increase your overall use of automation with AI and m/l how is this translating into your working with customers as they try to look at these playbooks and apply their own playbooks for how they set up their response regimes yeah so I mean I think overall I mean I think you can hit it on the head computer you kind of nailed down really those some it was kind of fundamental sort of concepts here now you can identify and you can document as many playbooks as you want but if you're not able to quickly respond when you identify those actual playbooks you know that's really half the battle I mean if you need to be able to identify you know one not only when the threat actors in your environment but then also you need to be able to quickly you know take action and like you were saying with that fabric if we can have that actual fabric being able to talk to the other controls within that fabric and take some action they're better off you're gonna be because you can align your defenses there and that's a great would you gotta make sure that all the controls within that fabric are all communicating together they're working together they're sharing information and they're responding together sure enough yeah are you starting to advise customers I'm curious you advising customers that even as they increase the capabilities of their fabric and how they handle their architectures from a micro macro segmentation and increase their use of automation or are there things that they can do from a practice standpoint just to ensure that their responses are appropriate fast and accurate yeah sure sure I mean I think a lot of the actual fabric once you actually build that fabric there's certain you know playbook responses that you can program into that fabric and I'll also even go I know we talked about you know fundamentals but I'll even dive a little bit lower here and you know you have that fabric but you also have to make sure you understand all the assets you have in your in you know your environment because that that information and that knowledge helps you with that macro and micro segmentation because when you can isolate you know different areas if there is a certain area that gets infected you can quickly turn the knobs to isolate that particular threat and that specific you know area or that's a specific segmented area and that is really gonna allow you to fight through the attack give you more time and ultimately reduce the impact of that particular breach so Tony we got the summer months coming up that means more vacations which is you jest less activity but then we got summer interns coming in which you know may involve additional clicking on things that shouldn't be clicked on any ideas what what should security pros be thinking about in the summer months what's the trend show well I think we're gonna continue to see that you know I I think the same type of threats that we've seen in the first quarter but I would say you know there may be a slight sort of drop-off right we got kind of kids that are gonna be out on vacation so you know schools may not see as much activity you got you know folks gonna be taking vacations and at the end of the day most of these exploits are client-side exploits which means you know a lot of times you need somebody to do something on the actual computer either you know clicking that link or clicking the attachment and if they're not there to do that they'll just sit there and you'll see less activity over time so we might see a little reduction in volume but I still think we'll see very similar types of you know threats in the coming months so good time good time are a good opportunity for security pros to double down on putting in place new architecture practices and response regime so that when stuff kicks up in the fall they're that much more prepared da Tony G on Domenico fort Ned great once again thanks very much for being on the cube hey you know Peter it's always a pleasure being here man hope to see you again soon you will and once again I'm Peter Burroughs until next time [Music]

>> Live from Orlando, Florida, it's theCUBE, covering Enterprise Connect 2019. Brought to you by Five9. >> Hi, welcome back to theCUBE's coverage of Enterprise Connect 2019. I'm Lisa Martin with my co-host for the week Stu Miniman, we are in Five9's booth here at this event, excited to welcome to theCUBE for the first time Jace Moreno, Microsoft Teams Developer Platform Lead from Microsoft, Jace, welcome to theCUBE. >> Thank you for having me, it's a pleasure. >> So we're excited that you're here because you are on the main stage tomorrow morning with Lori Wright. But talk to us about Microsoft Teams. You've been with Microsoft for awhile now, about 10 months with Teams. Talk to us about this tool for collaboration that companies can use from 10 people in a meeting to 10,000? >> Yeah, you'll hear us tomorrow. The phrase we're coining is an intelligent workplace for everyone, right? And I think for a long time, we've been perceived as an organization who builds tools, a lot of times with the Enterprise Knowledge Worker, the whole goal is to dispel that. There's multiple people out there, millions of people who are frontline workers, whatever you want to call 'em but the folks that are interfacing with your actual customers. And so we need to make sure that we are developing tools that are for them. But overall as I look at the product and what we've delivered, it's about bringing you one single place to go to for collaboration, right? So and that is bringing together your tools, whether or not Microsoft built them into one experience and then process these in workflows around them. >> So do you find that in terms of traction that the, like the enterprises and maybe the more senior generations that have been working with Microsoft tools for a long time get it or I mean, 'cause I can imagine there's kind of a cultural gap there with, whether it's a large enterprise like a Microsoft or maybe a smaller organization, There are people in this modern workforce that have very different perspectives, different cultures. How can Teams help to maybe break down some of those barriers and really be a platform for innovation? >> That's a great question. I think we've been battling that cultural, digital clash for a long time to be fair. I think it really comes out with Teams, though. Because it is an entirely different way of working. It's not just chat anymore, right? It's collaboration. It's bringing together all of these experiences and so I think there's a maturity curve for some of our average users to be fair. We're already seeing that curve take off as we speak. But what I often give advice to customers and to partners, I call 'em superpowers but you got to find that one reason that really gets people over the line because we get asked all the time, "Hey, everybody loves it "but we want to get 'em to use this as the one tool, "the one place that I go so I know that everything "I send in our organization goes to that single place. "How do I deliver that?" And I go, "Just give 'em a reason." That's what it comes down to honestly and I genuinely see that with organizations. We're seeing incredible examples of organizations leveraging partner integrations where it's bringing out their culture rather than them trying to evolve it, if that makes sense. >> So Jace, I'm glad you brought up the partners there and when I hear developer platform, all right, bring us inside a little bit. Everything API compatible, when people think about developers, there have been developers in the Microsoft space. .NET's got its great ecosystem there but what is it like to be in the Microsoft ecosystem here in 2019? >> It's a fun place to be. I will say, I've even stopped using the term developer when I say platform though to be fair because, and the reason I bring this up, what we've actually built allows a lot of IT professionals to build as well on Teams. PowerShell Scripts as an example is a huge opportunity for customers. Frankly, I've never written a line of code in my life and I built a bot for Teams. So it's pretty amazing what we're enabling but when we look at a lot of what partners are building, it's where are they seeing opportunities in the marketplace? So Five9 as an example with customer care, great opportunity there where we can extend the capabilities that a contact center as an example might need inside of Teams if they want to explore that. >> I love, I actually got to interview Jeffrey Snover at Microsoft Ignite last year who of course created PowerShell and he was like more excited now than he was when it was created quite a long time ago. So when I look around this platform, tell us some of the partners that you're working with. I saw some of the early notes that things like Zoom, and gosh you know, talk about some of the partners you're working with. >> So one thing I'll touch on too that I don't know if I fully answered your last question is what I'm hearing from our partners who have built on Teams and I'll touch on which ones in a second, we call it the extensibility of our platform but quite literally what it means is they are, we are allowing partners to allow their solutions to render in different ways inside of Teams and what we're hearing from partners, I had a conversation with Disco the other day as an example, so they built a, I'm not doing them a service by explaining it like this but it's a kudos bot essentially that they've delivered and it's actually bringing out that culture. But they told us the beauty of the Teams platform is that they don't only show up as a bot to the end users, they actually, we've offered them other ways to interact with the end user, so whatever's more comfortable for me inside of team, and my interaction with that solution, it's easy for them to have that correspondence. But in terms of top partnerships that we're looking at, we've had some incredible integrations built recently. ADP just launched theirs pretty recently to check payroll and build sort of a time off process flow if you will, with the bot. Polly's been a great one from day one. We have integrations with partners like Atlassian for a DevOps tool, so Jira and Confluence Cloud, Trello for project management, I could go on forever but we have over 250 in the store right now and that is growing very rapidly. This is what we spend most of our time on. So the initial focus was what are the tools out there that most people need to get their job done every day? That's where we'll start and now we're really evolving that and we're seeing some incredible things being built as we speak. >> So Jace, being at Enterprise Connect, this is an event where it's been around for a long time and has evolved quite considerably as Enterprise Communication and Collaborations has but one of things that when I was doing research to prep for the show that I'm reading is that the customer experience is table stakes. It's make or break. But some of the recommendations that when a company is, whether it's within a business unit buying software and services or at the corporate level, the customer has to have a seat there so that the decision is being made. Are we implementing tools and technologies and services that are actually going to delight our customers, not just retain them but drive customer lifetime value? In your role, where are some of Microsoft's customers in terms of helping to evolve the evolution of the platform? >> That's a great question, I'm really glad you asked it. It's been fun in my role because what we're seeing is a lot of customers who have taken the platform and built integrations to their tools. So think outside of productivity for a second, think IT support, think employee resources, they're building those integrations and they're leveraging those as a way to drive that organic broad adoption inside of their companies. Because they don't want to do the IT force anymore, they want people to love it like you said and naturally take to it and so I keep coming back to that, I call it superpowers, again it might be a ridiculous term but it's those superpowers you deliver to your people that allow them to get their work done better, get them to love that product and to your point, not want to ever leave it 'cause you can get a majority of your work done every day in that place. So we've seen some really cool ones. A couple examples that we just shared recently, Dentsu's a great one, so they have a three person Change Management Team for a 50,000 person global organization, okay? Three people, got to scale that right? Can't do that one on one training and so they initially took Teams and integrated it into their current website, internet, internal portals to essentially create a chatbot that helped people learn how to use the technology they delivered. Now they're taken that one step further because they saw such great success and they're going to different centers of excellence inside the organization saying, "Hey, do you want to get on board? "Because we'd like to make this the bot "that you interact with as an employee of Dentsu." So it's just incredible but it's driving again that adoption they're seeing, leveraging some of the simple stuff that we have on the platform. Does that answer your question? >> Yes very well, thank you. >> So when I look at some of the macro trends about communication, where I've heard some great success stories is internally just being able to collaborate with some of my internal people, Teams has done really well. Collaborating between various organizations still seems to have more challenges. Can you just bring us a little bit of insight as to why I hear great success stories there and not negatives on Teams but just it's still challenging if I have multiple organizations? We all understand even just doing a conference call or heck, a video call between lots of different companies still in 2019's a challenge. >> Yeah look, I mean I'll give you a couple answers here. We are young, I mean it's two years old as a product. So the momentum's been incredible but I'm not going to sit here and tell you we don't have things to work on, we absolutely do. What I will say though, take Enterprise Connect for example, we actually have a Teams team for Enterprise Connect. There's, I actually checked this morning, there's 181 people in that team and a majority of them are guests, so external users, So vendors that we work with to help us plan this conference and bring it all together and a lot of that has been seamless. Yes, there are little things here or there that we're working on but in that respect it's been pretty incredible. I constantly am using it with external parties and I find though, I don't necessarily know if the challenge is in the interface itself, I think it ends up becoming this opportunity to really educate people on this new way of working. And so going back to our partners again, we're sitting here with Five9, but that becomes critical. How do we work better with these organizations who we have mutual customers with to create that experience together, right? And bring again, superpowers to the users. >> What about a security as a superpower? Where is that in these conversations? >> I mean everything we build has a layer of security. I actually just got out of a meeting, you'll see, we've got an announcement around this tomorrow. So I can't blow it unfortunately but the bottom, the foundation and core of everything that we do will be security focused, absolutely. >> All right, so I went to the Microsoft show last year, AI is also one of those things besides security. AI's infused anywhere, so where does AI fit into the whole Teams story? >> The way we see it, I look at this in a couple angles. So most people get onto Teams and it's kind of chat and collab at first, right? Not always the case but a lot of organizations do that. Then it goes to meetings then I think, and you'll see a lot of this cool stuff tomorrow, we're doing it on AI but it's how then do you proactively start delivering better experiences to your end users? So I think of things that we're looking at right now is taking data, and sending those as an example to your IT admins about giving them insight into how users are leveraging Teams. How do you improve that experience for them? So again, you drive that natural broad adoption but kind of assist them a little bit along the way. So tons of great examples around the board. I'm not sure if that fully answers your question but just the sky's the limit. I think of some other things we're looking at though, you'll see a lot coming in the form of transcription, translation, those services that really create inclusiveness which is a big focus for us. Again back to that point earlier, it's the intelligent workplace for everyone. We want to be able to provide services with our partnerships that can really reach anybody in the business world, right? And even in the consumer world in some sense. >> Well Jace, thanks so much for joining Stu and me on the program this afternoon. We're looking forward to hearing your keynote in the morning and sharing with us some of the excitement and things that are happening and announcements we're going to hear from Microsoft Teams tomorrow. >> My pleasure. Thank you so much for having me, appreciate it. >> Our pleasure, fFor Stu Miniman, I'm Lisa Martin. You're watching theCUBE's coverage of day one, Enterprise Connect 2019 from Orlando. Stick around, Stu and I will be right back with our next guest. 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(bright instrumental music) >> Hi everyone, I'm John Furrier. Here in the Cube Studios in Palo Alto, for a special Cube conversation on some big news from PureStorage. We're here with Chadd Kenney, who's the Vice President of Product and Solutions at PureStorage. Big Cloud news. A historic announcement from PureStorage. One of the fastest growing startups in the storage business. Went public, I've been following these guys since creation. Great success story in Silicon Valley and certainly innovative products. Now announcing a Cloud product. Cloud data services, now in market. Chadd, this is huge. >> It's exciting time. Thank you so much for having us. >> So you guys, obviously storage success story, but now the reality is changed. You know we've been saying in the Cube, nothing changes, you get storage computer networking, old way, new way in the Cloud. Game is still the same. Storage isn't going away. You got to store the data somewhere and the data tsunami is coming. Still coming with Edge and a bunch of other things. Cloud more important than ever. To get it right is super important. So, what is the announcement of Cloud Data Service. Explain what the product is, why you guys built it, why now. >> Awesome. So, a couple different innovations that are part of this launch to start with. We have Cloud Block Store which is taking Purity, which is our operating system found on-prem and actually moving it to AWS. And we spent a bunch of time optimizing these solutions to make it so that, we could actually take on tier one, mission critical applications. A key differentiator is that most folks were really chasing after test-dev and leveraging the Cloud for that type of use case. Whereas Cloud Block Storage, really kind of industry strength and ready for mission critical applications. We also took protection mechanisms from FlashArray on-premises and actually made it so that you could use CloudSnap and move and protect data into the public Cloud via portable snapshot technology. Which we can dig into a little bit later. And then the last part is, we thought it was really ripe to change data protection, just as a whole. Most people are doing kind of disc to disc, to tape, and then moving tape offsite. We believe the world has shifted. There's a big problem in data protection. Restoring data is not happening in the time frame that its needed, and SLAs aren't being met, and users are not happy with the overall solution as a whole. We believe that restorations from Flash are incredibly important to the business, but in order to get there you have to offset the economics. So what we're building is a Flash to Flash to Cloud solution which enables folks to be able to take the advantages of the economics of Cloud and be able to then have a caching mechanism of Flash on-premises. So that they can restore things relatively quickly for the predominant set of data that they have out there. >> And just so I get everything right here. You guys only been on-premises only, this is now a cloud solution. It's software. >> Correct. >> Why now? Why wait 'til now, is the timing right? What's the internal conversation? And why should customers know, is this the right time. >> So, the evolution of cloud has been pretty interesting as we've gone through it. Most customers went from kind of a 100% on-premises, the Cloud came out and said, hey, I'm going to move everything to the Cloud. They found that didn't work great for enterprise applications. And so they kind of moved back and realized that hybrid cloud was going to be a world with they wanted to leverage both. We're seeing a lot of other shifts in the market. VMware already having RDS in platform. Now it's true hybrid cloud kind of playing out there. Amazon running an AWS. It's a good mixture just to showcase where people really want to be able to leverage the capabilities of both. >> So it's a good time because the customers are re-architecting as well. >> It's all about- >> Hybrid applications are definitely what people want. >> 100% and the application stack, I think was the core focus that really shifted over time. Instead of just focusing on hybrid cloud infrastructure, it was really about how applications could leverage multiple types of clouds to be able to leverage the innovation and services that they could provide. >> You know, I've always been following the IT business for 30 years or so and it's always been an interesting trend. You buy something from a vendor and there's a trade-offs. And there's always the payback periods, but now I think with this announcement that's interesting is you got the product mix that allows customers to have choice and pick what they want. There's no more trade-offs. If they like cloud, they go to cloud. If they like on-premise, you go on-premises. >> It sounds like an easy concept, but the crazy part to this is the Cloud divide is real. They are very different environments. As we've talked to customers, they were very lost on how it was going to take and enterprise application and actually leverage the innovations within the Cloud. They wanted it, they needed it, but at the same time, they weren't able to deliver up on it. And so, we realized that the data layer, fundamentally was the area that could give them that bridge between those two environments. And we could add some core values to the Cloud for even the next generation developer who's developing in the Cloud to bring in, better overall resiliency. Management and all sorts of new features that they weren't able to take advantage of in traditional public cloud. >> You know Chugg wants to do minimal about the serviceless trend and how awesome that is. It's just, look at the resource pool as a serviceless pool of resource. So is this storageless? >> So it's still backed by storage, obviously. >> No, I was just making a joke. No wait, that you're looking at it as what serviceless is to the user. You guys are providing that same kind of storage pool, addressable through the application of, >> Correct >> as if it's storageless. And what's great about taking 100% software platform and moving it to the Cloud is, customer can spin this up in like minutes. And what's great about it is, they can spend many, many, many instances of these things for various different use cases that they have out there, and get true utility out of it. So they're getting the agility that they really want while not having to offset the values that they really come to love about PureStorage on-premises. Now they can actually get it all on the public cloud as well. >> I want to dig into the products a little bit. Before we get there, I want you to answer the question that's probably on people's minds. I know you've been at Pure, really from the beginning. So, you've seen the history. Most people look at you guys and say, well you're a hardware vendor. I have Pure boxes everywhere, you guys doing a great job. You've pioneered the Flash, speed game on storage. People want, kill latency as they say. You guys have done a great job. But wait a minute, this is software. Explain how you guys did this, why it's important. People might not know that this is a software solution. They might be know you for hardware. What's the difference? Is there a difference? Why should they care and what's the impact? >> So, great question. Since we sell hardware products, most people see us as a hardware company. But at the end of the day, the majority of vinge and dev is software. We're building software to make, originally, off the shelf components to be enterprise worthy. Over time we decided to optimize the hardware too, and that pairing between the software and hardware gets them inherently great values. And this is why we didn't just take our software and just kind of throw it into every cloud and say have it, to customers. Like a lot of folks did. We spent a lot of time, just like we did on our hardware platform, optimizing for AWS to start with. So that we could truly be able to leverage the inherent technologies that they have, but build software to make it even better. >> It's interesting, I interviewed Andy Bechtolsheim at VMworld, and he's a chairman of Arista. He's called, Les Peckgesem calls him the Rembrandt of motherboards. And he goes, "John, we're in the software business." And he goes, "Let me tell ya, hardware's easy. Software's hard." >> I agree. >> So everyone's pretty much in the software business. This is not a change for Pure. >> No, this is the same game we've been in. >> Great. Alright, let's get into the products. The first one is Cloud Block Store for AWS. Which is the way Amazon does the branch. So it's on Amazon, or for Amazon as they say. They use different words. So this is Pure software in the Cloud. Your company, technically Pure software. >> Yup. >> In the Cloud as software, no hardware. >> Yup. >> A 100% storage, API support, always encrypted, seamless management and orchestration, DR backup migration between clouds. >> Yup. >> That's kind of the core premise. So what does the product do, what's the purpose of the product. On the Amazon piece, if I'm a customer of Pure or a prospect for Pure, what does the product give me? What's the capabilities? >> Great. I would say that the biggest thing that customers get is just leverage for their application stack to be able to utilize the Cloud. And let me give you a couple of examples 'cause they're kind of fun. So first off, Cloud Block Storage is just software that sits in the Cloud that has many of the same utilities that run on-premises. Any by doing so, you get the ability to be able to do stuff like I want to replicate, as a DR target. So maybe I don't have a secondary site out there, and I want to have a DR target that spin up in the event of a disaster. You can easily set up bi-directional replication to the instance that you have running in the Cloud. It's the exact same experience. The exact same APIs and you get our cloud data management with Pure1 to be able to see both sites. One single pane of glass, and make sure everything is up and running and doing well. You could also though, leverage a test-dev environment. So let's saying I'm running production on-premises, I can then go ahead and replicate to the Cloud, spin up an instance for test-dev, and running reporting, run analytics. Run anything else that I wanted on top of that. And spin up compute relatively quickly. Maybe I don't have it on-prem. Next, we could focus on replicating for protection. Let's say for compliance, I want to have many instances to be able to restore back in the event of a disaster or in the event that I just want to look back during a period of time. The last part is, not just on-prem to the Cloud, but leveraging the Cloud for even better resiliency to take enterprise applications and actually move them without having to do massive re-architecture. If you look at what happens, Amazon recommends typically, that you have data in two different availability zones. So that when you put an application on top of it, it can be resilient to any sort of failures within an AZ. What we've done is we've taken our active cluster technology which is active-active replication between two instances, and made it so that you can actually replicate between two availability zones. And your application now doesn't need to be re-architected whatsoever. >> So you basically, if I get this right, you had core software that made all that Flash, on the box which is on-premise, which is a hardware solution. Which sounds like it was commodity boxes so this, components. >> Just like the Cloud. >> You take it to the Cloud as an amazing amount of boxes out there. They have tons of data centers. So you treat the Cloud as if it's a virtual device, so to speak. >> Correct. I mean the Cloud functionally is just compute and storage, and networking on the back end has been abstracted by some sort of layer in front of it. We're leveraging compute resources for our controllers and we're leveraging persistent storage media for our storage. But what we've done in software is optimize a bunch of things. An example just as one is, in the Cloud when you, procure storage, you pay for all of it, whether you leverage it or not. We incorporate de-dupe, compression, thin provisioning, AES 256 encryption on all data arrest. These are data services that are just embedded in that aren't traditionally found in a traditional cloud. >> This makes so much sense. If you're an application developer, you focus on building the app. Not worrying about where the storage is and how it's all managed. 'Cause you want persistent data and uni-managed state, and all this stuff going on. And I just need a dashboard, I just need to know where the storage is. Is it available and bring it to the table. >> And make it easy with the same APIs that you were potentially running on, on-premises. And that last part that I would say is that, the layered services that are built into Purity, like our snapshot technology and being able to refresh test-dev environments or create 10 sandboxes for 10 developers in the Cloud and add compute instances to them, is not only instantaneous, but it's space saving as you actually do it. Where as in the normal cloud offerings, you're paying for each one of those instances. >> And the agility is off the charts, it's amazing. Okay, final question on this one is, how much is it's going to cost? How does a customer consume it? Is it in the marketplace? Do I just click a button, spin up things? How's the interface? What's the customer interaction and engagement with the product? How they buy it, how much it costs? Can you share the interaction with the customer? >> So we're just jumping into beta, so a lot of this is still being worked out. But what I will tell you is it's the exact same experience that customers have come to love with Pure. You can go download the Cloud formation template into your catalog with an AWS. So you can spin up instances. The same kind of consumption models that we've built on-prem will be applied to cloud. So it will be a very similar consumption model, which has been super consumer friendly that customers have loved from us over the years. And it will be available in the mid part of next year, and so people will be able to beta it today, test it out, see how it works, and then put it into full production in mid part of next year. >> And operationally, in the work flows, the customers don't skip a beat. It's the same kind of format, languages and the words, the word flow. It feels like Pure all the way through. >> Correct. And not only are we a 100% built on a rest API, but all of the things we've built in with, Python libraries that automate this for developers, to PowerShell toolkits, to Ansible playbooks. All the stuff we've built on codeupyourstorage.com are all applicable to both sites and you get Pure1, our Cloud based management system to be able to see all of it in one single pane of glass. >> Okay, let's move on. So the next piece I think is interesting. I'll get your thoughts on this is that the whole protection piece. On-premises, really kind of held back from the Cloud, mainly to protect the data. So you guys got CloudSnap for AWS, what does this product do? Is this the protection piece? How does this work? What is the product? What's the features and what's the value? >> So, StorReduce was a recent acquisition that we did that enables de-duplication on top of an S3 target. And so it allows you to store an S3 de-duplicated into a smaller form factor and we're pairing that with both an on-premises addition which will have a flash plate behind it for super fast restores. So think of that as a caching tier for your backups, but then also be able to replicate that out to the public cloud and leverage store reduce natively in the public cloud as well. >> So that's the store reduce product. So store reduce on it is that piece. As an object store? >> It is, yes. And we pair that with CloudSnap which is natively integrated within FlashArray, so you can also do snapshots to a FlashBlade for fast restores for both NFS, and you can send it also to S3 in the public cloud. And so you get the inherent abilities to even do, VM level granularity or volume level granularity as well from a FlashArray directly, without needing to have any additional hardware. >> Okay so the data services are the; Block Storage, Store Reduce and CloudSnap on a four AWS. >> Correct. >> How would you encapsulate this from a product and solution standpoint? How would you describe that to a customer in an elevator or just a quick value statement? What's in it for them? >> Sure. So Pure's been seen by customers as innovation engine that optimized applications and allowed them to do, I would say, amazing things into the enterprise. What we're doing now, is we're evolving that solution out of just an on-premises solution and making it available in a very agile Cloud world. We know this world is evolving dramatically. We know people really want to be able to take advantage of the innovations within the Cloud, and so what we're doing is we're finally bridging the gap between on-premises and the Cloud. Giving them the same user experience that they've come to love with Pure and all of the Clouds that they potentially need to develop in. >> Okay so from the announcement standpoint, you guys got Cloud Block Storage limited public beta, right out of the gate. GA in mid 2019. CloudSnap is GA at announcement and Store Reduce is going into beta, first half of 2019. >> Correct, we're excited about it. >> So for the skeptics out there who are- Hey you know, Chadd, I got to tell ya. I love the Cloud, but I'm a little bit nervous. How do I test and get a feeling for- this is going to be simple, if I'm going to jump in and look at this. What should I look at first? What sequence, should I try this? Do you guys have a playbook, for them to either kick the tires or how should they explore to get proficient in the new solution. >> Good question. Right, so for one if you're a FlashArray customer, CloudSnap gives you the ability to be able to take this new entity, called a portable Snapshot. Which is data paired with metadata, and allow you to be able to move data off of a FlashArray. You can put it to an NFS target or you can send it to the Cloud. And so that's the most logical one that folks will probably leverage first because it's super exciting for them to be able to leverage the Cloud and spin up instances, if they'd like to. Or protect back to their own prem. Also, Cloud Block Storage, great because you can spin it up relatively quickly and test out applications between the two. One area that I think customers are going to be really excited about is you could run an analytics environment in the Cloud and spin up a bunch of compute from your production instance by just replicating it up into the Cloud. The last part is, I think backup is not super sexy. Nobody like to talk about it, but it's a significant pain point that's out there, and I think we can make some major in-roads in helping businesses get better SLAs. We're very, very interested to see the great solutions people bring with- >> So, I'm going to put you on the spot here and ask you, there's always the, love the cliche, is it a vitamin or is it an Asprin. Is there a pain point? So obviously backup, I would agree. Backup and recovery, certainly with the disaster, you see the wildfires going on here in California. You can't stop thinking about what the, disaster recovery plan and then you got top line growth with application developers. The kind of the vitamin, if you will. What are the use cases, low hanging fruit for someone to like test this out from a pain point standpoint. Is it backup and what's the growth angle? I wanted to test out this new solution, what should I look at first? What would you recommend? >> It's a very tough question. So, CloudSnap is obviously the easy one. I'd say Cloud Block Store is one that I think, people will. I look at my biggest, customers biggest challenges out there it's how do I get application portable. So I think Cloud Block Store really gives you the application portability. So I think it's finally achieving that whole, hybrid cloud world. But at the end of the day, backup is really big pain point that the enterprise deals with, like right this second. So there's areas where we believe we can add inherent values to them with being able to do fast restores from Flash. That meets SLA's very quickly and is an easy fix. >> And you guys feel good about the data protection aspect of this? >> Yes, very much so. >> Awesome. I want to get your personal take on this. You were early on in Pure. What's the vibe inside the company? This is Cloud and people love Cloud. There's benefits for Cloud, as well as on-premises. What's the mood like inside PureStorage? You've seen from the beginning, now you're a public company and growing up really, really fast. What's the vibe like inside PureStorage? >> It's funny, it hasn't really changed all that much, in the cultural side of the thing, of the business. I love where I work because of the people. The people bring so much fun to the business, so much innovation and we have a mindset that's heavily focused on customer first. And that's one of the things. I always tell this kind of story is, when we first started, we sat in a room on a whiteboard and wrote up, what is everything that sucks about storage. And instead of trying to figure out how we make a 2.0 version of some storage array, we actually figured out what are all the customer pain points that we needed to satisfy and then we built innovations to go do that. Not go chase the competition, but actually go alleviate customer challenges. And we just continue to kind of focus on customer first and so the whole company kind of, rallies around that. And I think you see a very different motion that what you do in most companies because we love hearing about customer results of our products. Engineering just will rally around when a customer shows up just to hear exactly their experience associated to it. And so with this, I think what they see is a continued evolution of the things we've been doing and they love seeing and providing customer solutions in areas that they were challenged to deal with in the past. >> What was some of the customer feedback when you guys started going, hey, you've got a new product, you're doing all of that early work. And you got to go talk to some people and knock on the, hey, what do you think, would you like the Cloud, a little bit of the Cloud. How would you like the Cloud to be implemented? What was some of the things you heard from customers? >> A lot of them said, if you can take your core tenets, which was simplicity, efficiency, reliability, and customer focus around consumption, and if you could give that to me in the Cloud, that would be the Nirvana. So, when we looked at this model, that's exactly what we did. We said, let's take what people love about us on-prem, and give 'em the exact same experience in the Cloud. >> That's great and that's what you guys have done. Congratulations. >> Thanks so much. >> Great to hear the Cloud story here Chadd Kenney, Vice President of Products and Solutions at PureStorage. Taking the formula of success on-premises with Flash and the success there, and bringing it to the Cloud. That's the big deal in this announcement. I'm John Furrier here in the Palo Alto studios, thanks for watching. (upbeat instrumental music)

>> Live from Orlando, Florida, it's theCUBE covering Microsoft Ignite. Brought to you by Cohesity and theCUBE's ecosystem partners. >> Welcome back everyone, we are wrapping up day three of Microsoft Ignite here in Orlando, Florida. CUBE's live coverage, I'm your host Rebecca Knight, along with Stu Miniman, my esteemed cohost for these past three days, it's been fun working with you, Stu. >> Rebecca, it's been a great show, real excited. Our first time at a Microsoft show and it's a big one. I mean, the crowds are phenomenal. Great energy at the show and yeah, it's been great breaking down this ecosystem with you. >> So, three days, what do we know, what did you learn, what is your big takeaway, what are you going to to go back to Boston with? >> You know, it's interesting, we've been all talking and people that I know that have been here a couple of years, I've talked to people that have been at this show for decades, this is a different show. There's actually a friend of mine said, he's like, "Well look, Windows pays the bills for a lot of companies." There's a lot of people that all the Windows components, that's their job. I mean, I think back through my career when I was on the vendor side, how many rollouts of Exchange and SharePoint and all these things we've done over the years. Office 365 been a massive wave that we watched. So Microsoft has a broad portfolio and they've got three anchor shows. I was talking with one of the partners here and he's like, "You know, there's not a lot of channel people "at this event, at VMworld there's a lot of channel people." I'm like, "Well yeah because there's a separate show "that Microsoft has for them." You and I were talking at an earlier analytics session with Patrick Moorhead and he said, "You know when I look at the buy versus build, "a lot of these people are buying and I don't "feel I have as many builders." Oh wait, what's that other show that they have in the Spring, it's called Microsoft Build. A lot of the developers have moved there so it's a big ecosystem, Microsoft has a lot of products. Everything from, my son's excited about a lot of the Xbox stuff that they have here. Heck, a bunch of our crew was pickin' up Xbox sweatshirts while they're here. But a lot has changed, as Tim Crawford said, this is a very, it feels like a different Microsoft, than it even was 12 or 24 months ago. They're innovating, so look at how fast Microsoft moves and some of these things. There's good energy, people are happy and it's still trying to, you know. It's interesting, I definitely learned a lot at this show even though it wasn't the most sparkly or shiny but that's not necessarily a bad thing. >> Right, I mean, I think as you made a great point about just how integral Microsoft is to all of our lives as consumers, as enterprise, the Xbox, the Windows, the data storage, there's just so much that Microsoft does that if we were to take away Microsoft, I can't even imagine what life would be like. What have been your favorite guests? I mean, we've had so many really, really interesting people. Customers, we've had partners, we're going to have a VC. What are some of the most exciting things you've heard? >> Yeah, it's interesting, we've had Jeffrey Snover on the program a couple of years ago and obviously a very smart person here. But at this show, in his ecosystem, I mean, he created PowerShell. And so many people is like, I built my career off of what he did and this product that he launched back in 2001. But we talked a little bit about PowerShell with him but then we were talking about Edge and the Edge Boxes and AI and all those things, it's like this is really awesome stuff. And help connecting the dots to where we hid. So obviously, big name guest star, always, and I always love talking to the customers. The thing I've been looking at the last couple of years is how all of these players fit into a multicloud world. And Microsoft, if you talk about digital transformation, and you talk about who will customers turn to to help them in this multicloud world. Well, I don't think there's any company that is closer to companies applications across the spectrum of options. Office 365 and other options in SaaS, all the private cloud things, you start with Windows Server, you've got Windows on the desktop, Windows on the server. Virtualization, they're starting to do hyperconversion everything, even deeper. As well as all the public cloud with Azure and developers. I talked to the Azure functions team while I was here. Such breadth and depth of offering that Microsoft is uniquely positioned to play in a lot of those areas even if, as I said, certain areas if the latest in data there might be some other company, Google, Amazon, well positioned there. We had a good discussion Bernard Golden, who's with Capital One, gave us some good commentary on where Alibaba fits in the global scheme. So, nice broad ecosystem, and I learned a lot and I know resonated with both of us, the "you want to be a learn it all, not a know it all." And I think people that are in that mindset, this was a great show for them. >> Well, you bring up the mindset, and that is something that Satya Nadella is really such a proponent of. He says that we need to have a growth mindset. This is off of the Carol Dweck and Angela Duckworth research that talks about how important that is, how important continual learning is for success. And that is success in life and success on the job and organization success and I think that that is something that we are also really picked up on. This is the vibe of Microsoft, this is a company, Satya Nadella's leadership, talking to so many of the employees, and these are employees who've been there for decades, these are people who are really making their career, and they said, "Yeah, I been here 20 years, if I had my way, "I'll be here another 30." But the point is that people have really recommitted to Microsoft, I feel. And that's really something interesting to see, especially in the tech industry where people, millennials especially, stay a couple years and then move on to the next shiny, new thing. >> Yeah, there was one of our first guests on for Microsoft said that, "Been there 20 years and what is different about "the Satya Nadella Microsoft to the others is "we're closer and listening even more to our customers." We talk about co-creation, talk about how do we engage. Microsoft is focusing even deeper on industries. So that's really interesting. An area that I wanted to learn a little bit more about is we've been talking about Azure Stack for a number of years, we've been talking about how people are modernizing their data center. I actually had something click with me this week because when I look at Azure Stack, it reminds me of solutions I helped build with converged infrastructure and I was a big proponent of the hyper-converged infrastructure wave. And what you heard over and over again, especially from Microsoft people, is I shouldn't think of Azure Stack in that continuum. Really, Azure Stack is not from the modernization out but really from the cloud in. This is the operating model of Azure. And of course it's in the name, it's Azure, but when I looked at it and said, "Oh, well I've got partners like "Lenovo and Dell and HPE and Sysco." Building this isn't this just the next generation of platform there? But really, it's the Azure model, it's the Azure operating stack, and that is what it has. And it's more, WSSD is their solution for the converged and then what they're doing with Windows Server 2019 is the hyper-converged. Those the models that we just simplify what was happening in the data center and it's similar but a little bit different when we go to things like Azure and Azure Stack and leads to something that I wanted to get your feedback on. You talk business productivity because when we talk to companies like Nutanix, we talk to companies like Cohesity who we really appreciate their support bringing us here, giving us this great thing right in the center of it, they talk about giving people back their nights and weekends, giving them back time, because they're an easy button for a lot of things, they help make the infrastructure invisible and allow that. Microsoft says we're going to try to give you five to ten percent back of your business productivity, going to allow you to focus on things like AI and your data rather than all the kind of underlying spaghetti underneath. What's your take on the business productivity piece of things? >> I mean, I'm in favor of it; it is a laudable goal. If I can have five to ten percent of my day back of just sort of not doing the boring admin stuff, I would love that. Is it going to work, I don't know. I mean, the fact of the matter is I really applaud what Cohesity said and the customers and the fact that people are getting, yes, time back in their day to focus on the more creative projects, the more stimulating challenges that they face, but also just time back in their lives to spend with their children and their spouse and doing whatever they want to do. So those are really critical things, and those are critical things to employee satisfaction. We know, a vast body of research shows, how much work life balance is important to employees coming to their office or working remotely and doing their best work. They need time to recharge and rest and so if Microsoft can pull that off, wow, more power to them. >> And the other thing I'll add to that is if you, say, if you want that work life balance and you want to be fulfilled in your job, a lot of times what we're getting rid of is some of those underlying, those menial tasks the stuff that you didn't love doing in the first place. And what you're going to have more time to do, and every end user that we talked to says, "By the way, I'm not getting put out of a job, "I've got plenty of other tasks I could do." And those new tasks are really tying back to what the business needs. Because business and IT, they need to tie together, they need to work together, it is a partnership there. Because if IT can't deliver what the business needs, there's other alternatives, that's what Stealth IT was and the public cloud could be. And Microsoft really positions things as we're going to help you work through that transition and get there to work on these environments. >> I want to bring up another priority of Microsoft's and that is diversity. So that is another track here, there's a lot of participants who are learning about diversity in tech. It's not a good place right now, we know that. The tech industry is way too male, way too white. And Satya Nadella, along with a lot of other tech industry leaders, has said we need more underrepresented minorities, we need more women, not only as employees but also in leadership positions. Bev Crair, who was on here yesterday, she's from Lenovo. She said that things are starting to change because women are buying a lot of the tech and so that is going to force changes. What do you think, do you buy it? >> And I do, and here's where I'd say companies like Lenovo and Microsoft, when you talk about who makes decisions and how are decisions made, these are global companies. Big difference between a multi-national company or a company that's headquartered in Silicon Valley or Seattle or anything versus a global company. You look at both of those companies, they have, they are working not just to localize but have development around the world, have their teams that are listening to requirements, understand what is needed in those environments. Going back to what we talked about before, different industries, different geographies, and different cultures, we need to be able to fit and work and have products that work in those environments, everything. I think it was Bev that talked about, even when we think about what color lights. Well, you know, oh well default will use green and red. Well, in different cultures, those have different meanings. So yeah, it is, it's something that definitely I've heard the last five to ten years of my career that people understand that, it's not just, in the United States, it can't just be the US or Silicon Valley creating great technology and delivering that device all the way around the world. It needs to be something that is globally developed, that co-creation, and more, and hopefully we're making progress on the diversity front. We definitely try to do all we can to bring in diverse voices. I was glad we had a gentleman from Italy shouting back to his daughters that were watching it. We had a number of diverse guests from a geography, from a gender, from ethnicity, on the program and always trying to give those various viewpoints on theCUBE. >> I want to ask you about the show itself: the 30,000 people from 5,000 different organizations around the globe have convened here at the Orange County Convention Center, what do you think? >> Yeah, so it was impressive. We go to a lot of shows, I've been to bigger shows. Amazon Reinvent was almost 50,000 last year. I've been to Oracle OpenWorld, it's like takes over San Francisco, 60 or 70,000. This convention center is so sprawling, it's not my favorite convention center, but at least the humidity is to make sure I don't get dried out like Las Vegas. But logistics have run really well, the food has not been a complaint, it's been good, the show floor has been bustling and sessions are going well. I was talking to a guy at breakfast this morning that was like, "Oh yeah, I'm a speaker, "I'm doing a session 12 times." I'm like, "You're not speaking on the same thing 12 times?" He's like, "No, no it's a demo and hands on lab." I'm like, "Oh, of course." So they make sure that you have lots of different times to be able to do what you want. There is so much that people want to see. The good news is that they can go watch the replays of almost all of them online. Even the demos are usually something that they're cloud enabled and they get on live. And of course we help to bring a lot of this back to them to give them a taste of what's there. All of our stuff's always available on the website of thecube.net. This one, actually, this interview goes up on a podcast we call theCUBE Insights. So please, our audience, we ask you, whether it's iTunes or your favorite podcast reader, go to Spotify, theCUBE Insights. You can get a key analysis from every show that we do, we put that up there and that's kind of a tease to let you go to thecube.net and see the hundreds and thousands of interviews that we do across all of our shows. >> Great, and I want to give a final, second shout out to Cohesity, it's been so fun having them, being in the Cohesity booth, and having a lot of great Cohesity people around. >> Yeah, absolutely, I mean, so much I wish we could spend a little more time even. AI, if we go back to the keynote analysis then, but you can watch that, I can talk about the research we've done, and said how the end user information that Microsoft can get access to to help people when you talk about what they have, the TouchPoint to Microsoft Office. And even things like Xbox, down to the consumer side, to understand, have a position in the marketplace that really is unparalleled if you look at kind of the breadth and depth that Microsoft has. So yeah, big thanks to Cohesity, our other sponsors of the program that help allow us to bring this great content out to our community, and big shout out I have to give out to the community too. First time we've done this show, I reached out to all my connections and the community reached back, helped bring us a lot of great guests. I learned a lot: Cosmos DB, all the SQL stuff, all the Office and Microsoft 365, so much. My brain's full leaving this show and it's been a real pleasure. >> Great, I agree, Stu, and thank you so much to Microsoft, thank you to the crew, this has been a really fun time. We will have more coming up from the Orange County Civic Center, Microsoft Ignite. I'm Rebecca Knight for Stu Miniman, we will see you in just a little bit. (digital music)

(electronic music) >> Live from Orlando, Florida, it's theCUBE! Covering Microsoft Ignite. Brought to you by Cohesity, and theCUBE's ecosystem partners. >> Welcome back everyone to theCUBE's live coverage of Microsoft Ignite here in Orlando, Florida. I'm your host, Rebecca Knight, along with my cohost, Stu Miniman. We're joined by Jeffrey Snover. He is the technical fellow and chief architect for Azure Storage and Cloud Edge at Microsoft. Thanks so much for coming, for returning to theCUBE, I should say, Jeffrey, you're a CUBE alum. >> Yes, I enjoyed the last time. So can't wait to do it again this time. >> Well we're excited to have you. So before the camera's were rolling, we were talking about PowerShell. You invented PowerShell. >> Yeah, I did. >> It was invented in the early 2000's, it took a few years to ship, as you said. But can you give our viewers an update of where we are? >> Yeah, you know, it's 2018, and it's never been a better time for PowerShell. You know, basically the initial mission is sort of complete. And the mission was provide sort of general purpose scripting for Windows. But now we have a new mission. And that new mission is to manage anything, anywhere. So we've taken PowerShell, we've open sourced it. It's now running, we've ported it to macOS and Linux. There's a very large list of Linux distributions that we support it on, and it runs everywhere. And so, now, you can manage from anywhere. Your Windows box, your Linux box, your Mac box, even in the browser, you can manage, and then anything. You can manage Windows, you can manage Linux, you can manage macOS. So manage anything, anywhere. Any cloud, Azure, or AWS, or Google. Any hypervisor, Hyper-V or VMware, or any physical server. It's amazing. In fact, our launch partners, when we launched this, our launch partners, VMware, Google, AWS. Not Microsoft's traditional partners. >> That's great to hear. It was actually, one of the critiques we had, at the key note this morning, was partnerships are critically important. But felt that Satya gave a little bit of a jab towards, the kind of, the Amazon's out there. When we talk to customers, we know it's a heterogeneous, multi-cloud world. You know, you work all over the place, with your solutions that you had. There's not, like, Azure, Azure Stack, out to The Edge. The Edge, it is early, it's going to be very heterogeneous. So connect the dots for us a little. You know, we love having the technical fellows on, as to, you go from PowerShell, to now this diverse set of solutions that you work on today. >> Yeah, exactly. So basically, from PowerShell, they asked me to be the chief architect for Windows Server. Right, because if you think about it, an operating system is largely management, right? And, so, that's what I did, resource management. And, so, I was the chief architect for that, for many years, and we decided that, as part of that, we were developing cloud-inspired infrastructure. So, basically, you know, Windows Server had grown up. You know, sort of focused in on a machine. Azure had gone and needed to build a new set of infrastructure for the cloud. And we looked at what they were doing. And they say, hey, that's some great ideas. Let's take the ideas there, and put them into the general purpose operating system. And that's what we call our software-defined data center. And the reason why we couldn't use Azure's directly is, Azure's, really, design center is very, very, very large systems. So, for instance, the storage stamp, that starts at about 10 racks. No customer wants to start with 10 racks. So we took the inspiration from them and re-implemented it. And now our systems can start with two servers. Our Azure Stack systems, well, so, then, what we decided was, hey, this is great technology. Let's take the great cloud-inspired infrastructure of Windows Server, and match it with the Azure services themselves. So we take Azure, put it on top of Windows Server, package it as an appliance experience, and we call that Azure Stack. And that's where I have been mostly focused for the last couple of years. >> Right, can you help us unpack a little bit. There's a lot of news today. >> Yes. >> You know, Windows 2019 was announced. I was real interested in the Data Box Edge solution, which I'm sure. >> Isn't that crazy? >> Yeah, really interesting. You're like, let's do some AI applications out at the Edge, and with the same kind of box that we can transport data. Because, I always say, you got to follow customers applications and data, and it's tough to move these things. You know, we've got physics that we still have to, you know, work on until some of these smart guys figure out how to break that. But, yeah, maybe give us a little context, as to news of the show, things your teams have been working on. >> Yeah, so the Data Box Edge, big, exciting stuff. Now, there's a couple scenarios for Data Box Edge. First is, first it's all kind of largely centered on storage and the Edge. So Storage, you've got a bunch of data in your enterprise, and you'd like it to be in Azure. One flavor of Data Box Edge is a disk. You call us up, we send you a disk, you fill up that disk, you send it back to us, it shows up in Azure. Next. >> A pretty big disk, though? >> Well, it can be a small disk. >> Oh, okay. >> Yeah, no, it can be a single SSD, okay. But then you can say, well, no, I need a bunch more. And so we send you a box, the box is over there. It's like 47 pounds, we send you this thing, it's about 100 terabytes of data. You fill that thing up, send it to us, and we upload it. Or a Data Box Heavy. Now this thing has a handle and wheels. I mean, literally, wheels, it's specially designed so that a forklift can pick this thing up, right? It's like, I don't know, like 400 pounds, it's crazy. And that's got about a petabyte worth of storage. Again, we ship it to you, you fill it up, ship it back to us. So that's one flavor, Data Box transport. Then there's Data Box Edge. Data Box Edge, you go to the website, say, I'd like a Data Box Edge, we send you a 1u server. You plug that in, you keep it plugged in, then you use it. How do you use it? You connect it to your Azure storage, and then all your Azure storage is available through here. And it's exposed through SMB. Later, we'll expose it through NFS and a Blob API. But, then, anything you write here is available immediately, it gets back to Azure, and, effectively, it looks like near-infinite storage. Just use it and it gets backed up, so it's amazing. Now, on that box, we're also adding the ability to say, hey, we got a bunch of compute there. You can run IoT Edge platforms. So you run the IoT Edge platform, you can run gateways, you can run Kubernetes clusters on this thing, you can run all sorts of IoT software. Including, we're integrating in brainwave technology. So, brainwave technology is, and, by the way, we'll want to talk about this a little bit, in a second. It is evidence of the largest transformation we'll see in our industry. And that is the re-integration of the industry. So, basically, what does that mean? In the past, the industry used to be, back when the big key players were digital. Remember digital, from DEC? We're all Massachusetts people. (Rebecca laughs) So, DEC was the number one employer in Massachusetts, gone. IBM dominant, much diminished, a whole bunch of people. They were dominant when the industry was vertically integrated. Vertically integrated meant all those companies designed their own silicone, they built their own boards, they built their own systems, they built their OS, they built the applications, the serviced them. Then there was the disintegration of the computer industry. Where, basically, we went vertically integrated. You got your chips from Intel or Motorola. The operating system, you got from Sun or Microsoft. The applications you got from a number of different vendors. Okay, so we got vertically integrated. What you're seeing, and what's so exciting, is a shift back to vertical integration. So Microsoft is designing its own hardware, right? We're designing our own chips. So we've designed a chip specially for AI, we call it a brainwave chip, and that's available in the Data Box Edge. So, now, when you do this AI stuff, guess what? The processing is very different. And it can be very, very fast. So that's just one example of Microsoft's innovation in hardware. >> Wow, so, I mean. >> What do you do with that? >> One of the things that we keep hearing so much, at this conference, is that Microsoft products and services are helping individual employees tap into their own creativity, their ingenuity, and then, also, collaborate with colleagues. I'm curious about where you get your ideas, and how you actually put that into practice, as a technical fellow. >> Yeah. >> How do you think about the future, and envision these next generation technologies? >> Yeah, well, you know, it's one of those things, honestly, where your strength is your weakness, your weakness is your strength. So my weakness is, I can't deal with complexity, right. And, so, what I'm always doing is I'm taking a look at a very complex situation, and I'm saying, what's the heart of it, like, give me the heart of it. So my background's physics, right? And so, in physics, you're not doing, you're looking for the F equals M A. And if you have that, when you find that, then you can apply it over, and over, and over again. So I'm always looking at what are the essential things here. And so that's this, well, you see a whole bunch of confusing things, like, what's up with this? What's with this? That idea of there is this narrative about the reintegration of the computer industry. How very large vendors, be it Microsoft, or AWS, are, because we operate at such large scales, we are going to be vertically integrated. We're developing our own hardware, we do our own systems, et cetera. So, I'm always looking for the simple story, and then applying it. And, it turns out, I do it pretty accurately. And it turns out, it's pretty valuable. >> Alright, so that's a good set up to talk about Azure Stacks. So, the value proposition we heard, of course, is, you know, start everything in the cloud first, you know, Microsoft does Azure, and then lets, you know, have some of those services in the same operating model in your data center, or in your hosting service provider environment. So, first of all, did I get that right? And, you know, give us the update on Azure Stack. I've been trying to talk to customers that are using it, talking to your partners. There is a lot of excitement around it. But, you know, proof points, early use cases, you know, where is this going to be pointing towards, where the future of the data center is? >> So, it's a great example. So what I figured out, when I thought about this, and kind of drilled in, like what's really, what really matters here? What I realized was that what the gestalt of Azure Stack is different than everything we've done in the past. And it really is an appliance, okay? So, in the past, I just had a session the other day, and people were asking, well, when are you going to, when is Azure Stack going to have the latest version of the operating system? I said, no, no, no, no, no. Internals are internal, it's an appliance. Azure Stack is for people who want to use a cloud, not for people who want to build it. So you shouldn't be concerned about all the internals. You just plug it in, fill out some forms, and then you use it, just start using it. You don't care about the details of how it's all configured, you don't do the provisioning, we do all that for you. And so that's what we've done. And it turns out that that message resonates really well. Because, as you probably know, most private clouds fail. Most private clouds fail miserably. Why? And there's really two reasons. There's two flavors of failure. But one is they just never work. Now that's because, guess what, it's incredibly hard. There are so many moving pieces and, guess what, we learned that ourselves. The numbers of times we stepped on the rakes, and, like, how do you make all this work? There's a gazillion moving parts. So if any of your, you have a team, that's failed at private cloud, they're not idiots. It's super, super, super hard. So that's one level of failure. But even those teams that got it working, they ultimately failed, as well, because of lack of usage. And the reason for that is, having done all that, they then built a snowflake cloud. And then when someone said, well, how do I use this? How do I add another NIC to a VM? The team that put it together were the only ones that could answer that. Nope, there was no ecosystem around it. So, with Azure Stack, the gestalt is, like, this is for people who want to use it, not for people who want to build it. So you just plug it in, you pick a vendor, and you pick a capacity. This vendor, four notes, this vendor 12 or 16 notes. And that's it. You come in, we ask you what IP range is, how do I integrate with your identity? Within a day, it's up and running, and your users are using it, really using it. Like, that's craziness. And then, well what does it mean to use it? Like, oh, hey, how do I ad a NIC to a VM? It's Azure, so how does Azure do it? I have an entire Azure ecosystem. There's documentation, there's training, there's videos, there's conferences. You can go and put on a resume, I'd like to hire someone with Azure skills, and get someone, and then they're productive that day. Or, and here's the best part, you can put on your resume, I have Azure skills, and you knock on 10 doors, and nine of them are going to say, come talk to me. So, that was the heart of it. And, again, it goes back to your question of, like, the value, or what does a technical fellow do. It's to figure out what really matters. And then say, we're all in on that. There was a lot of skepticism, a lot of customers like, I must have my security agent on there. It's like, well, no, then you're not a good candidate. What do you mean? I say, well, look, we're not going to do this. And they say, well you'll never be able to sell to anyone in my industry. I said, no, you're wrong. They say, what do you mean, I'm wrong? I say, well, let me prove it to ya, do you own a SAN? They say, well, of course we own a SAN. I said, I know you own a SAN. Let me ask you this, a SAN is a general purpose server with a general purpose operating system. So do you put your security and managing agents on there? And they said, no, we're not allowed to. I said, right, and that's the way Azure Stack is. It's a sealed appliance. We take care of that responsibility for you. And it's worked out very, very well. >> Alright, you got me thinking. One of the things we want to do is, we want to simplify the environment. That's been the problem we've had in IT, for a long time, is it's this heterogeneous mess. Every group did their own thing. I worry a multi-cloud world has gotten us into more silos. Because, I've got lots of SAS providers, I've got multiple cloud providers, and, boy, maybe when I get to the Edge, every customer is going to have multiple Edge applications, and they're going to be different, so, you know. How do you simplify this, over time, for customers? Or do we? >> Here's the hard story, back to getting at the heart of it. Look, one of the benefits of having done this a while, is I've stepped on a lot of these rakes. You're looking at one of the biggest, earliest adopters of the Boolean cross-platform, Gooey Framework. And, every time, there is this, oh, there's multiple platforms? People say, oh, that's a problem, I want a technology that allows me to bridge all of those things. And it sound so attractive, and generates a lot of early things, and then it turned out, I was rocking with this Boolean cross-breed platform. I wrote it, and it worked on Mac's and Windows. Except, I couldn't cut and paste. I couldn't print, I couldn't do anything. And so what happens is it's so attractive, blah, blah, blah. And then you find out, and when the platforms aren't very sophisticated, the gap between what these cross-platform things do, and the platform is not so much, so it's like, eh, it's better to do this. But, over time, the platform just grows and grows and grows. So the hard message is, people should pick. People should pick. Now, one of the benefits of Azure, as a great choice, is that, with the other guys, you are locked to vendor. Right, there is exactly one provider of those API's. With Azure, you can get an implementation of Azure from Microsoft, the Azure Public Cloud. Or you can get an implementation from one of our hardware vendors, running Azure Stack. They provide that to you. Or you can get it from a service provider. So, you don't have to get, you buy into these API's. You optimize around that, but then you can still use vendor. You know, hey, what's your price for this? What's your price for that, what can you give me? With the other guys, they're going to give you whatcha give ya, and that's your deal. (Rebecca laughs) >> That's a good note to end on. Thank you so much, Jeffrey, for coming on theCUBE again. It was great talking to you. >> Oh, that was fast. (Rebecca laughs) Enjoyed it, this was great. >> Great. I'm Rebecca Knight, for Stu Miniman, stay tuned to theCUBE. We will have more from Microsoft Ignite in just a little bit. (electronic music)