>>We're back at Super Computing, 22 in Dallas, winding down the final day here. A big show floor behind me. Lots of excitement out there, wouldn't you say, Dave? Just >>Oh, it's crazy. I mean, any, any time you have NASA presentations going on and, and steampunk iterations of cooling systems that the, you know, it's, it's >>The greatest. I've been to hundreds of trade shows. I don't think I've ever seen NASA exhibiting at one like they are here. Dave Nicholson, my co-host. I'm Paul Gell, in which with us is Satish Ier. He is the vice president of emerging services at Dell Technologies and Satit, thanks for joining us on the cube. >>Thank you. Paul, >>What are emerging services? >>Emerging services are actually the growth areas for Dell. So it's telecom, it's cloud, it's edge. So we, we especially focus on all the growth vectors for, for the companies. >>And, and one of the key areas that comes under your jurisdiction is called apex. Now I'm sure there are people who don't know what Apex is. Can you just give us a quick definition? >>Absolutely. So Apex is actually Dells for a into cloud, and I manage the Apex services business. So this is our way of actually bringing cloud experience to our customers, OnPrem and in color. >>But, but it's not a cloud. I mean, you don't, you don't have a Dell cloud, right? It's, it's of infrastructure as >>A service. It's infrastructure and platform and solutions as a service. Yes, we don't have our own e of a public cloud, but we want to, you know, this is a multi-cloud world, so technically customers want to consume where they want to consume. So this is Dell's way of actually, you know, supporting a multi-cloud strategy for our customers. >>You, you mentioned something just ahead of us going on air. A great way to describe Apex, to contrast Apex with CapEx. There's no c there's no cash up front necessary. Yeah, I thought that was great. Explain that, explain that a little more. Well, >>I mean, you know, one, one of the main things about cloud is the consumption model, right? So customers would like to pay for what they consume, they would like to pay in a subscription. They would like to not prepay CapEx ahead of time. They want that economic option, right? So I think that's one of the key tenets for anything in cloud. So I think it's important for us to recognize that and think Apex is basically a way by which customers pay for what they consume, right? So that's a absolutely a key tenant for how, how we want to design Apex. So it's absolutely right. >>And, and among those services are high performance computing services. Now I was not familiar with that as an offering in the Apex line. What constitutes a high performance computing Apex service? >>Yeah, I mean, you know, I mean, this conference is great, like you said, you know, I, there's so many HPC and high performance computing folks here, but one of the things is, you know, fundamentally, if you look at high performance computing ecosystem, it is quite complex, right? And when you call it as an Apex HPC or Apex offering offer, it brings a lot of the cloud economics and cloud, you know, experience to the HPC offer. So fundamentally, it's about our ability for customers to pay for what they consume. It's where Dell takes a lot of the day to day management of the infrastructure on our own so that customers don't need to do the grunge work of managing it, and they can really focus on the actual workload, which actually they run on the CHPC ecosystem. So it, it is, it is high performance computing offer, but instead of them buying the infrastructure, running all of that by themself, we make it super easy for customers to consume and manage it across, you know, proven designs, which Dell always implements across these verticals. >>So what, what makes the high performance computing offering as opposed to, to a rack of powered servers? What do you add in to make it >>Hpc? Ah, that's a great question. So, I mean, you know, so this is a platform, right? So we are not just selling infrastructure by the drink. So we actually are fundamentally, it's based on, you know, we, we, we launch two validated designs, one for life science sales, one for manufacturing. So we actually know how these PPO work together, how they actually are validated design tested solution. And we also, it's a platform. So we actually integrate the softwares on the top. So it's just not the infrastructure. So we actually integrate a cluster manager, we integrate a job scheduler, we integrate a contained orchestration layer. So a lot of these things, customers have to do it by themself, right? If they're buy the infrastructure. So by basically we are actually giving a platform or an ecosystem for our customers to run their workloads. So make it easy for them to actually consume those. >>That's Now is this, is this available on premises for customer? >>Yeah, so we, we, we make it available customers both ways. So we make it available OnPrem for customers who want to, you know, kind of, they want to take that, take that economics. We also make it available in a colo environment if the customers want to actually, you know, extend colo as that OnPrem environment. So we do both. >>What are, what are the requirements for a customer before you roll that equipment in? How do they sort of have to set the groundwork for, >>For Well, I think, you know, fundamentally it starts off with what the actual use case is, right? So, so if you really look at, you know, the two validated designs we talked about, you know, one for, you know, healthcare life sciences, and one other one for manufacturing, they do have fundamentally different requirements in terms of what you need from those infrastructure systems. So, you know, the customers initially figure out, okay, how do they actually require something which is going to require a lot of memory intensive loads, or do they actually require something which has got a lot of compute power. So, you know, it all depends on what they would require in terms of the workloads to be, and then we do havet sizing. So we do have small, medium, large, we have, you know, multiple infrastructure options, CPU core options. Sometimes the customer would also wanna say, you know what, as long as the regular CPUs, I also want some GPU power on top of that. So those are determinations typically a customer makes as part of the ecosystem, right? And so those are things which would, they would talk to us about to say, okay, what is my best option in terms of, you know, kind of workloads I wanna run? And then they can make a determination in terms of how, how they would actually going. >>So this, this is probably a particularly interesting time to be looking at something like HPC via Apex with, with this season of Rolling Thunder from various partners that you have, you know? Yep. We're, we're all expecting that Intel is gonna be rolling out new CPU sets from a powered perspective. You have your 16th generation of PowerEdge servers coming out, P C I E, gen five, and all of the components from partners like Invidia and Broadcom, et cetera, plugging into them. Yep. What, what does that, what does that look like from your, from your perch in terms of talking to customers who maybe, maybe they're doing things traditionally and they're likely to be not, not fif not 15 G, not generation 15 servers. Yeah. But probably more like 14. Yeah, you're offering a pretty huge uplift. Yep. What, what do those conversations look >>Like? I mean, customers, so talking about partners, right? I mean, of course Dell, you know, we, we, we don't bring any solutions to the market without really working with all of our partners, whether that's at the infrastructure level, like you talked about, you know, Intel, amd, Broadcom, right? All the chip vendors, all the way to software layer, right? So we have cluster managers, we have communities orchestrators. So we usually what we do is we bring the best in class, whether it's a software player or a hardware player, right? And we bring it together as a solution. So we do give the customers a choice, and the customers always want to pick what you they know actually is awesome, right? So they that, that we actually do that. And, you know, and one of the main aspects of, especially when you talk about these things, bringing it as a service, right? >>We take a lot of guesswork away from our customer, right? You know, one of the good example of HPC is capacity, right? So customers, these are very, you know, I would say very intensive systems. Very complex systems, right? So customers would like to buy certain amount of capacity, they would like to grow and, you know, come back, right? So give, giving them the flexibility to actually consume more if they want, giving them the buffer and coming down. All of those things are very important as we actually design these things, right? And that takes some, you know, customers are given a choice, but it actually, they don't need to worry about, oh, you know, what happens if I actually have a spike, right? There's already buffer capacity built in. So those are awesome things. When we talk about things as a service, >>When customers are doing their ROI analysis, buying CapEx on-prem versus, versus using Apex, is there a point, is there a crossover point typically at which it's probably a better deal for them to, to go OnPrem? >>Yeah, I mean, it it like specifically talking about hpc, right? I mean, why, you know, we do have a ma no, a lot of customers consume high performance compute and public cloud, right? That's not gonna go away, right? But there are certain reasons why they would look at OnPrem or they would look at, for example, Ola environment, right? One of the main reasons they would like to do that is purely have to do with cost, right? These are pretty expensive systems, right? There is a lot of ingress, egress, there is a lot of data going back and forth, right? Public cloud, you know, it costs money to put data in or actually pull data back, right? And the second one is data residency and security requirements, right? A lot of these things are probably proprietary set of information. We talked about life sciences, there's a lot of research, right? >>Manufacturing, a lot of these things are just, just in time decision making, right? You are on a factory floor, you gotta be able to do that. Now there is a latency requirement. So I mean, I think a lot of things play, you know, plays into this outside of just cost, but data residency requirements, ingress, egress are big things. And when you're talking about mass moments of data you wanna put and pull it back in, they would like to kind of keep it close, keep it local, and you know, get a, get a, get a price >>Point. Nevertheless, I mean, we were just talking to Ian Coley from aws and he was talking about how customers have the need to sort of move workloads back and forth between the cloud and on-prem. That's something that they're addressing without posts. You are very much in the, in the on-prem world. Do you have, or will you have facilities for customers to move workloads back and forth? Yeah, >>I wouldn't, I wouldn't necessarily say, you know, Dell's cloud strategy is multi-cloud, right? So we basically, so it kind of falls into three, I mean we, some customers, some workloads are suited always for public cloud. It's easier to consume, right? There are, you know, customers also consume on-prem, the customers also consuming Kohler. And we also have like Dell's amazing piece of software like storage software. You know, we make some of these things available for customers to consume a software IP on their public cloud, right? So, you know, so this is our multi-cloud strategy. So we announced a project in Alpine, in Delta fold. So you know, if you look at those, basically customers are saying, I love your Dell IP on this, on this product, on the storage, can you make it available through, in this public environment, whether, you know, it's any of the hyper skill players. So if we do all of that, right? So I think it's, it shows that, you know, it's not always tied to an infrastructure, right? Customers want to consume the best thumb and if we need to be consumed in hyperscale, we can make it available. >>Do you support containers? >>Yeah, we do support containers on hpc. We have, we have two container orchestrators we have to support. We, we, we have aner similarity, we also have a container options to customers. Both options. >>What kind of customers are you signing up for the, for the HPC offerings? Are they university research centers or is it tend to be smaller >>Companies? It, it's, it's, you know, the last three days, this conference has been great. We probably had like, you know, many, many customers talking to us. But HC somewhere in the range of 40, 50 customers, I would probably say lot of interest from educational institutions, universities research, to your point, a lot of interest from manufacturing, factory floor automation. A lot of customers want to do dynamic simulations on factory floor. That is also quite a bit of interest from life sciences pharmacies because you know, like I said, we have two designs, one on life sciences, one on manufacturing, both with different dynamics on the infrastructure. So yeah, quite a, quite a few interest definitely from academics, from life sciences, manufacturing. We also have a lot of financials, big banks, you know, who wants to simulate a lot of the, you know, brokerage, a lot of, lot of financial data because we have some, you know, really optimized hardware we announced in Dell for, especially for financial services. So there's quite a bit of interest from financial services as well. >>That's why that was great. We often think of Dell as, as the organization that democratizes all things in it eventually. And, and, and, and in that context, you know, this is super computing 22 HPC is like the little sibling trailing around, trailing behind the super computing trend. But we definitely have seen this move out of just purely academia into the business world. Dell is clearly a leader in that space. How has Apex overall been doing since you rolled out that strategy, what, two couple? It's been, it's been a couple years now, hasn't it? >>Yeah, it's been less than two years. >>How are, how are, how are mainstream Dell customers embracing Apex versus the traditional, you know, maybe 18 months to three year upgrade cycle CapEx? Yeah, >>I mean I look, I, I think that is absolutely strong momentum for Apex and like we, Paul pointed out earlier, we started with, you know, making the infrastructure and the platforms available to customers to consume as a service, right? We have options for customers, you know, to where Dell can fully manage everything end to end, take a lot of the pain points away, like we talked about because you know, managing a cloud scale, you know, basically environment for the customers, we also have options where customers would say, you know what, I actually have a pretty sophisticated IT organization. I want Dell to manage the infrastructure, but up to this level in the layer up to the guest operating system, I'll take care of the rest, right? So we are seeing customers who are coming to us with various requirements in terms of saying, I can do up to here, but you take all of this pain point away from me or you do everything for me. >>It all depends on the customer. So we do have wide interest. So our, I would say our products and the portfolio set in Apex is expanding and we are also learning, right? We are getting a lot of feedback from customers in terms of what they would like to see on some of these offers. Like the example we just talked about in terms of making some of the software IP available on a public cloud where they'll look at Dell as a software player, right? That's also is absolutely critical. So I think we are giving customers a lot of choices. Our, I would say the choice factor and you know, we are democratizing, like you said, expanding in terms of the customer choices. And I >>Think it's, we're almost outta our time, but I do wanna be sure we get to Dell validated designs, which you've mentioned a couple of times. How specific are the, well, what's the purpose of these designs? How specific are they? >>They, they are, I mean I, you know, so the most of these valid, I mean, again, we look at these industries, right? And we look at understanding exactly how would, I mean we have huge embedded base of customers utilizing HPC across our ecosystem in Dell, right? So a lot of them are CapEx customers. We actually do have an active customer profile. So these validated designs takes into account a lot of customer feedback, lot of partner feedback in terms of how they utilize this. And when you build these solutions, which are kind of end to end and integrated, you need to start anchoring on something, right? And a lot of these things have different characteristics. So these validated design basically prove to us that, you know, it gives a very good jump off point for customers. That's the way I look at it, right? So a lot of them will come to the table with, they don't come to the blank sheet of paper when they say, oh, you know what I'm, this, this is my characteristics of what I want. I think this is a great point for me to start from, right? So I think that that gives that, and plus it's the power of validation, really, right? We test, validate, integrate, so they know it works, right? So all of those are hypercritical. When you talk to, >>And you mentioned healthcare, you, you mentioned manufacturing, other design >>Factoring. We just announced validated design for financial services as well, I think a couple of days ago in the event. So yep, we are expanding all those DVDs so that we, we can, we can give our customers a choice. >>We're out of time. Sat ier. Thank you so much for joining us. Thank you. At the center of the move to subscription to everything as a service, everything is on a subscription basis. You really are on the leading edge of where, where your industry is going. Thanks for joining us. >>Thank you, Paul. Thank you Dave. >>Paul Gillum with Dave Nicholson here from Supercomputing 22 in Dallas, wrapping up the show this afternoon and stay with us for, they'll be half more soon.

(lively music) >> Good morning. Welcome back to theCUBE's coverage at Supercomputing Conference 2022, live here in Dallas. I'm Dave Nicholson with my co-host Paul Gillin. So far so good, Paul? It's been a fascinating morning Three days in, and a fascinating guest, Ian from AWS. Welcome. >> Thanks, Dave. >> What are we going to talk about? Batch computing, HPC. >> We've got a lot, let's get started. Let's dive right in. >> Yeah, we've got a lot to talk about. I mean, first thing is we recently announced our batch support for EKS. EKS is our Kubernetes, managed Kubernetes offering at AWS. And so batch computing is still a large portion of HPC workloads. While the interactive component is growing, the vast majority of systems are just kind of fire and forget, and we want to run thousands and thousands of nodes in parallel. We want to scale out those workloads. And what's unique about our AWS batch offering, is that we can dynamically scale, based upon the queue depth. And so customers can go from seemingly nothing up to thousands of nodes, and while they're executing their work they're only paying for the instances while they're working. And then as the queue depth starts to drop and the number of jobs waiting in the queue starts to drop, then we start to dynamically scale down those resources. And so it's extremely powerful. We see lots of distributed machine learning, autonomous vehicle simulation, and traditional HPC workloads taking advantage of AWS Batch. >> So when you have a Kubernetes cluster does it have to be located in the same region as the HPC cluster that's going to be doing the batch processing, or does the nature of batch processing mean, in theory, you can move something from here to somewhere relatively far away to do the batch processing? How does that work? 'Cause look, we're walking around here and people are talking about lengths of cables in order to improve performance. So what does that look like when you peel back the cover and you look at it physically, not just logically, AWS is everywhere, but physically, what does that look like? >> Oh, physically, for us, it depends on what the customer's looking for. We have workflows that are all entirely within a single region. And so where they could have a portion of say the traditional HPC workflow, is within that region as well as the batch, and they're saving off the results, say to a shared storage file system like our Amazon FSx for Lustre, or maybe aging that back to an S3 object storage for a little lower cost storage solution. Or you can have customers that have a kind of a multi-region orchestration layer to where they say, "You know what? "I've got a portion of my workflow that occurs "over on the other side of the country "and I replicate my data between the East Coast "and the West Coast just based upon business needs. "And I want to have that available to customers over there. "And so I'll do a portion of it in the East Coast "a portion of it in the West Coast." Or you can think of that even globally. It really depends upon the customer's architecture. >> So is the intersection of Kubernetes with HPC, is this relatively new? I know you're saying you're, you're announcing it. >> It really is. I think we've seen a growing perspective. I mean, Kubernetes has been a long time kind of eating everything, right, in the enterprise space? And now a lot of CIOs in the industrial space are saying, "Why am I using one orchestration layer "to manage my HPC infrastructure and another one "to manage my enterprise infrastructure?" And so there's a growing appreciation that, you know what, why don't we just consolidate on one? And so that's where we've seen a growth of Kubernetes infrastructure and our own managed Kubernetes EKS on AWS. >> Last month you announced a general availability of Trainium, of a chip that's optimized for AI training. Talk about what's special about that chip or what is is customized to the training workloads. >> Yeah, what's unique about the Trainium, is you'll you'll see 40% price performance over any other GPU available in the AWS cloud. And so we've really geared it to be that most price performance of options for our customers. And that's what we like about the silicon team, that we're part of that Annaperna acquisition, is because it really has enabled us to have this differentiation and to not just be innovating at the software level but the entire stack. That Annaperna Labs team develops our network cards, they develop our ARM cards, they developed this Trainium chip. And so that silicon innovation has become a core part of our differentiator from other vendors. And what Trainium allows you to do is perform similar workloads, just at a lower price performance. >> And you also have a chip several years older, called Inferentia- >> Um-hmm. >> Which is for inferencing. What is the difference between, I mean, when would a customer use one versus the other? How would you move the workload? >> What we've seen is customers traditionally have looked for a certain class of machine, more of a compute type that is not as accelerated or as heavy as you would need for Trainium for their inference portion of their workload. So when they do that training they want the really beefy machines that can grind through a lot of data. But when you're doing the inference, it's a little lighter weight. And so it's a different class of machine. And so that's why we've got those two different product lines with the Inferentia being there to support those inference portions of their workflow and the Trainium to be that kind of heavy duty training work. >> And then you advise them on how to migrate their workloads from one to the other? And once the model is trained would they switch to an Inferentia-based instance? >> Definitely, definitely. We help them work through what does that design of that workflow look like? And some customers are very comfortable doing self-service and just kind of building it on their own. Other customers look for a more professional services engagement to say like, "Hey, can you come in and help me work "through how I might modify my workflow to "take full advantage of these resources?" >> The HPC world has been somewhat slower than commercial computing to migrate to the cloud because- >> You're very polite. (panelists all laughing) >> Latency issues, they want to control the workload, they want to, I mean there are even issues with moving large amounts of data back and forth. What do you say to them? I mean what's the argument for ditching the on-prem supercomputer and going all-in on AWS? >> Well, I mean, to be fair, I started at AWS five years ago. And I can tell you when I showed up at Supercomputing, even though I'd been part of this community for many years, they said, "What is AWS doing at Supercomputing?" I know you care, wait, it's Amazon Web Services. You care about the web, can you actually handle supercomputing workloads? Now the thing that very few people appreciated is that yes, we could. Even at that time in 2017, we had customers that were performing HPC workloads. Now that being said, there were some real limitations on what we could perform. And over those past five years, as we've grown as a company, we've started to really eliminate those frictions for customers to migrate their HPC workloads to the AWS cloud. When I started in 2017, we didn't have our elastic fabric adapter, our low-latency interconnect. So customers were stuck with standard TCP/IP. So for their highly demanding open MPI workloads, we just didn't have the latencies to support them. So the jobs didn't run as efficiently as they could. We didn't have Amazon FSx for Lustre, our managed lustre offering for high performant, POSIX-compliant file system, which is kind of the key to a large portion of HPC workloads is you have to have a high-performance file system. We didn't even, I mean, we had about 25 gigs of networking when I started. Now you look at, with our accelerated instances, we've got 400 gigs of networking. So we've really continued to grow across that spectrum and to eliminate a lot of those really, frictions to adoption. I mean, one of the key ones, we had a open source toolkit that was jointly developed by Intel and AWS called CFN Cluster that customers were using to even instantiate their clusters. So, and now we've migrated that all the way to a fully functional supported service at AWS called AWS Parallel Cluster. And so you've seen over those past five years we have had to develop, we've had to grow, we've had to earn the trust of these customers and say come run your workloads on us and we will demonstrate that we can meet your demanding requirements. And at the same time, there's been, I'd say, more of a cultural acceptance. People have gone away from the, again, five years ago, to what are you doing walking around the show, to say, "Okay, I'm not sure I get it. "I need to look at it. "I, okay, I, now, oh, it needs to be a part "of my architecture but the standard questions, "is it secure? "Is it price performant? "How does it compare to my on-prem?" And really culturally, a lot of it is, just getting IT administrators used to, we're not eliminating a whole field, right? We're just upskilling the people that used to rack and stack actual hardware, to now you're learning AWS services and how to operate within that environment. And it's still key to have those people that are really supporting these infrastructures. And so I'd say it's a little bit of a combination of cultural shift over the past five years, to see that cloud is a super important part of HPC workloads, and part of it's been us meeting the the market segment of where we needed to with innovating both at the hardware level and at the software level, which we're going to continue to do. >> You do have an on-prem story though. I mean, you have outposts. We don't hear a lot of talk about outposts lately, but these innovations, like Inferentia, like Trainium, like the networking innovation you're talking about, are these going to make their way into outposts as well? Will that essentially become this supercomputing solution for customers who want to stay on-prem? >> Well, we'll see what the future lies, but we believe that we've got the, as you noted, we've got the hardware, we've got the network, we've got the storage. All those put together gives you a a high-performance computer, right? And whether you want it to be redundant in your local data center or you want it to be accessible via APIs from the AWS cloud, we want to provide that service to you. >> So to be clear, that's not that's not available now, but that is something that could be made available? >> Outposts are available right now, that have this the services that you need. >> All these capabilities? >> Often a move to cloud, an impetus behind it comes from the highest levels in an organization. They're looking at the difference between OpEx versus CapEx. CapEx for a large HPC environment, can be very, very, very high. Are these HPC clusters consumed as an operational expense? Are you essentially renting time, and then a fundamental question, are these multi-tenant environments? Or when you're referring to batches being run in HPC, are these dedicated HPC environments for customers who are running batches against them? When you think about batches, you think of, there are times when batches are being run and there are times when they're not being run. So that would sort of conjure, in the imagination, multi-tenancy, what does that look like? >> Definitely, and that's been, let me start with your second part first is- >> Yeah. That's been a a core area within AWS is we do not see as, okay we're going to, we're going to carve out this super computer and then we're going to allocate that to you. We are going to dynamically allocate multi-tenant resources to you to perform the workloads you need. And especially with the batch environment, we're going to spin up containers on those, and then as the workloads complete we're going to turn those resources over to where they can be utilized by other customers. And so that's where the batch computing component really is powerful, because as you say, you're releasing resources from workloads that you're done with. I can use those for another portion of the workflow for other work. >> Okay, so it makes a huge difference, yeah. >> You mentioned, that five years ago, people couldn't quite believe that AWS was at this conference. Now you've got a booth right out in the center of the action. What kind of questions are you getting? What are people telling you? >> Well, I love being on the show floor. This is like my favorite part is talking to customers and hearing one, what do they love, what do they want more of? Two, what do they wish we were doing that we're not currently doing? And three, what are the friction points that are still exist that, like, how can I make their lives easier? And what we're hearing is, "Can you help me migrate my workloads to the cloud? "Can you give me the information that I need, "both from a price for performance, "for an operational support model, "and really help me be an internal advocate "within my environment to explain "how my resources can be operated proficiently "within the AWS cloud." And a lot of times it's, let's just take your application a subset of your applications and let's benchmark 'em. And really that, AWS, one of the key things is we are a data-driven environment. And so when you take that data and you can help a customer say like, "Let's just not look at hypothetical, "at synthetic benchmarks, let's take "actually the LS-DYNA code that you're running, perhaps. "Let's take the OpenFOAM code that you're running, "that you're running currently "in your on-premises workloads, "and let's run it on AWS cloud "and let's see how it performs." And then we can take that back to your to the decision makers and say, okay, here's the price for performance on AWS, here's what we're currently doing on-premises, how do we think about that? And then that also ties into your earlier question about CapEx versus OpEx. We have models where actual, you can capitalize a longer-term purchase at AWS. So it doesn't have to be, I mean, depending upon the accounting models you want to use, we do have a majority of customers that will stay with that OpEx model, and they like that flexibility of saying, "Okay, spend as you go." We need to have true ups, and make sure that they have insight into what they're doing. I think one of the boogeyman is that, oh, I'm going to spend all my money and I'm not going to know what's available. And so we want to provide the, the cost visibility, the cost controls, to where you feel like, as an HPC administrator you have insight into what your customers are doing and that you have control over that. And so once you kind of take away some of those fears and and give them the information that they need, what you start to see too is, you know what, we really didn't have a lot of those cost visibility and controls with our on-premises hardware. And we've had some customers tell us we had one portion of the workload where this work center was spending thousands of dollars a day. And we went back to them and said, "Hey, we started to show this, "what you were spending on-premises." They went, "Oh, I didn't realize that." And so I think that's part of a cultural thing that, at an HPC, the question was, well on-premises is free. How do you compete with free? And so we need to really change that culturally, to where people see there is no free lunch. You're paying for the resources whether it's on-premises or in the cloud. >> Data scientists don't worry about budgets. >> Wait, on-premises is free? Paul mentioned something that reminded me, you said you were here in 2017, people said AWS, web, what are you even doing here? Now in 2022, you're talking in terms of migrating to cloud. Paul mentioned outposts, let's say that a customer says, "Hey, I'd like you to put "in a thousand-node cluster in this data center "that I happen to own, but from my perspective, "I want to interact with it just like it's "in your data center." In other words, the location doesn't matter. My experience is identical to interacting with AWS in an AWS data center, in a CoLo that works with AWS, but instead it's my physical data center. When we're tracking the percentage of IT that's that is on-prem versus off-prem. What is that? Is that, what I just described, is that cloud? And in five years are you no longer going to be talking about migrating to cloud because people go, "What do you mean migrating to cloud? "What do you even talking about? "What difference does it make?" It's either something that AWS is offering or it's something that someone else is offering. Do you think we'll be at that point in five years, where in this world of virtualization and abstraction, you talked about Kubernetes, we should be there already, thinking in terms of it doesn't matter as long as it meets latency and sovereignty requirements. So that, your prediction, we're all about insights and supercomputing- >> My prediction- >> In five years, will you still be talking about migrating to cloud or will that be something from the past? >> In five years, I still think there will be a component. I think the majority of the assumption will be that things are cloud-native and you start in the cloud and that there are perhaps, an aspect of that, that will be interacting with some sort of an edge device or some sort of an on-premises device. And we hear more and more customers that are saying, "Okay, I can see the future, "I can see that I'm shrinking my footprint." And, you can see them still saying, "I'm not sure how small that beachhead will be, "but right now I want to at least say "that I'm going to operate in that hybrid environment." And so I'd say, again, the pace of this community, I'd say five years we're still going to be talking about migrations, but I'd say the vast majority will be a cloud-native, cloud-first environment. And how do you classify that? That outpost sitting in someone's data center? I'd say we'd still, at least I'll leave that up to the analysts, but I think it would probably come down as cloud spend. >> Great place to end. Ian, you and I now officially have a bet. In five years we're going to come back. My contention is, no we're not going to be talking about it anymore. >> Okay. >> And kids in college are going to be like, "What do you mean cloud, it's all IT, it's all IT." And they won't remember this whole phase of moving to cloud and back and forth. With that, join us in five years to see the result of this mega-bet between Ian and Dave. I'm Dave Nicholson with theCUBE, here at Supercomputing Conference 2022, day three of our coverage with my co-host Paul Gillin. Thanks again for joining us. Stay tuned, after this short break, we'll be back with more action. (lively music)

(upbeat music) >> Good Friday morning everyone from Motor City, Lisa Martin here with John Furrier. This is our third day, theCUBE's third day of coverage of KubeCon + CloudNativeCon 22' North America. John, we've had some amazing conversations the last three days. We've had some good conversations about observability. We're going to take that one step further and look beyond its three pillars. >> Yeah, this is going to be a great segment. Looking forward to this. This is about in depth conversation on observability. The guest is technical and it's on the front lines with customers. Looking forward to this segment. Should be great. >> Yeah. Ian Smith is here, the field CTO at Chronosphere. Ian, welcome to theCUBE. Great to have you. >> Thank you so much. It's great to be here. >> All right. Talk about the traditional three pillars, approach, and observability. What are some of the challenges with that, and how does Chronosphere solve those? >> Sure. So hopefully everyone knows people think of the three pillars as logs, metrics and traces. What do you do with that? There's no action there. It's just data, right? You collect this data, you go put it somewhere, but it's not actually talking about any sort of outcomes. And I think that's really the heart of the issue, is you're not achieving anything. You're just collecting a whole bunch of data. Where do you put it? What are you... What can you do with it? Those are the fundamental questions. And so one of the things that we're focused on at Chronosphere is, well, what are those outcomes? What is the real value of that? And for example, thinking about phases of observability. When you have an incident or you're trying to investigate something through observability, you probably want to know what's going on. You want to triage any problems you detect. And then finally, you want to understand the cause of those and be able to take longer term steps to address them. >> What do customers do when they start thinking about it? Because observability has that promise. Hey, you know, get the data, we'll throw AI at it. >> Ian: Yeah. >> And that'll solve the problem. When they get over their skis, when do they realize that they're really not tackling it properly, or the ones that are taking the right approach? What's the revelation? What's your take on that? You're in the front lines. What's going on with the customer? The good and the bad. What's the scene look like? >> Yeah, so I think the bad is, you know, you end up buying a lot of things or implementing even in open source or self building, and it's very disconnected. You're not... You don't have a workflow, you don't have a path to success. If you ask different teams, like how do you address these particular problems? They're going to give you a bunch of different answers. And then if you ask about what their success rate is, it's probably very uneven. Another key indicator of problems is that, well, do you always need particular senior engineers in your instance or to help answer particular performance problems? And it's a massive anti pattern, right? You have your senior engineers who are probably need to be focused on innovation and competitive differentiation, but then they become the bottleneck. And you have this massive sort of wedge of maybe less experienced engineers, but no less valuable in the overall company perspective, who aren't effective at being able to address these problems because the tooling isn't right, the workflows are incorrect. >> So the senior engineers are getting pulled in to kind of fix and troubleshoot or observe what the observability data did or didn't deliver. >> Correct. Yeah. And you know, the promise of observability, a lot of people talk about unknown unknowns and there's a lot of, you know, crafting complex queries and all this other things. It's a very romantic sort of deep dive approach. But realistically, you need to make it very accessible. If you're relying on complex query languages and the required knowledge about the architecture and everything every other team is doing, that knowledge is going to be super concentrated in just a couple of heads. And those heads shouldn't be woken up every time at 3:00 AM. They shouldn't be on every instant call. But oftentimes they are the sort of linchpin to addressing, oh, as a business we need to be up 99.99% of the time. So how do we accomplish that? Well, we're going to end up burning those people. >> Lisa: Yeah. >> But also it leads to a great dissatisfaction in the bulk of the engineers who are, you know, just trying to build and operate the services. >> So talk... You mentioned that some of the problems with the traditional three pillars are, it's not outcome based, it leads to silo approaches. What is Chronosphere's definition and can you walk us through those three phases and how that really gives you that competitive edge in the market? >> Yeah, so the three phases being know, triage and understand. So just knowing about a problem, and you can relate this very specifically to capabilities, but it's not capabilities first, not feature function first. So know, I need to be able to alert on things. So I do need to collect data that gives me those signals. But particularly as you know, the industry starts moving towards as slows. You start getting more business relevant data. Everyone knows about alert storms. And as you mentioned, you know, there's this great white hope of AI and machine learning, but AI machine learning is putting a trust in sort of a black box, or the more likely reality is that really statistical model. And you have to go and spend a very significant amount time programming it for sort of not great outcomes. So know, okay, I want to know that I have a problem, I want to maybe understand the symptoms of that particular problem. And then triage, okay, maybe I have a lot of things going wrong at the same time, but I need to be very precise about my resources. I need to be able to understand the scope and importance. Maybe I have five major SLOs being violated right now. Which one is the greatest business impact? Which symptoms are impacting my most valuable customers? And then from there, not getting into the situation, which is very common where, okay, well we have every... Your customer facing engineering team, they have to be on the call. So we have 15 customer facing web services. They all have to be on that call. Triage is that really important aspect of really mitigating the cost to the organization because everyone goes, oh, well I achieved my MTTR and my experience from a variety of vendors is that most organizations, unless you're essentially failing as a business, you achieve your SLA, you know, three nines, four nines, whatever it is. But the cost of doing that becomes incredibly extreme. >> This is huge point. I want to dig into that if you don't mind, 'cause you know, we've been all seeing the cost of ownership miles in it all, the cost of doing business, cost of the shark fan, the iceberg, what's under the water, all those metaphors. >> Ian: Yeah. >> When you look at what you're talking about here, there are actually, actually real hardcore costs that might be under the water, so to speak, like labor, senior engineering time, 'cause Cloud Native engineers are coding in the pipelines. A lot of impact. Can you quantify and just share an example or illustrate where the costs are? 'Cause this is something that's kind of not obvious. >> Ian: Yeah. >> On the hard costs. It's not like a dollar amount, but time resource breach, wrong triage, gap in the data. What are some of the costs? >> Yeah, and I think they're actually far more important than the hard costs of infrastructure and licensing. And of course there are many organizations out there using open source observability components together. And they go, Oh it's free. No licensing costs. But you think again about those outcomes. Okay, I have these 15 teams and okay, I have X number of incidents a month, if I pull a representative from every single one of those teams on. And it turns out that, you know, as we get down in further phases, we need to be able to understand and remediate the issue. But actually only two teams required of that. There's 13 individuals who do not need to be on the call. Okay, yes, I met my SLA and MTTR, but if I am from a competitive standpoint, I'm comparing myself to a very similar organization that only need to impact those two engineers versus the 15 that I had over here. Who is going to be the most competitive? Who's going to be most differentiated? And it's not just in terms of number of lines of code, but leading to burnout of your engineers and the churn of that VPs of engineering, particularly in today's economy, the hardest thing to do is acquire engineers and retain them. So why do you want to burn them unnecessarily on when you can say, okay, well I can achieve the same or better result if I think more clearly about my observability, but reduce the number of people involved, reduce the number of, you know, senior engineers involved, and ultimately have those resources more focused on innovation. >> You know, one thing I want, at least want get in there, but one thing that's come up a lot this year, more than I've ever seen before, we've heard about the skill gaps, obviously, but burnout is huge. >> Ian: Yes. >> That's coming up more and more. This is a real... This actually doesn't help the skills gap either. >> Ian: Correct. >> Because you got skills gap, that's a cost potentially. >> Ian: Yeah. >> And then you got burnout. >> Ian: Yeah. >> People just kind of sitting on their hands or just walking away. >> Yeah. So one of the things that we're doing with Chronosphere is, you know, while we do deal with the, you know, the pillar data, but we're thinking about it more, what can you achieve with that? Right? So, and aligning with the know, triage and understand. And so you think about things like alerts, you know, dashboards, you be able to start triaging your symptoms. But really importantly, how do we bring the capabilities of things like distributed tracing where they can actually impact this? And it's not just in the context of, well, what can we do in this one incident? So there may be scenarios where you, absolutely do need those power users or those really sophisticated engineers. But from a product challenge perspective, what I'm personally really excited about is how do you capture that insight and those capabilities and then feed that back in from a product perspective so it's accessible. So you know, everyone talks about unknown unknowns in observability and then everyone sort of is a little dismissive of monitoring, but monitoring that thing, that democratizes access and the decision making capacity. So if you say I once worked at an organization and there were three engineers in the whole company who could generate the list of customers who were impacted by a particular incident. And I was in post sales at the time. So anytime there was a major incident, need to go generate that list. Those three engineers were on every single incident until one of them got frustrated and built a tool. But he built it entirely on his own. But can you think from an observability perspective, can you build a thing that it makes all those kinds of capabilities accessible to the first point where you take that alert, you know, which customers are affected or whatever other context was useful last time, but took an hour, two hours to achieve. And so that's what really makes a dramatic difference over time, is it's not about the day one experience, but how does the product evolve with the requirements and the workflow- >> And Cloud Native engineers, they're coding so they can actually be reactive. That's interesting, a platform and a tool. >> Ian: Yes. >> And platform engineering is the hottest topic at this event. And this year, I would say with Cloud Native hearing a lot more. I mean, I think that comes from the fact that SREs not really SRE, I think it's more a platform engineer. >> Ian: Yes. >> Not everyone's an... Not company has an SRE or SRE environment. But platform engineering is becoming that new layer that enables the developers. >> Ian: Correct. >> This is what you're talking about. >> Yeah. And there's lots of different labels for it, but I think organizations that really think about it well they're thinking about things like those teams, that developer efficiency, developer productivity. Because again, it's about the outcomes. It's not, oh, we just need to keep the site reliable. Yes, you can do that, but as we talked about, there are many different ways that you can burn unnecessary resources. But if you focus on developer efficiency and productivity, there's retainment, there's that competitive differentiation. >> Let's uplevel those business outcomes. Obviously you talked about in three phases, know, triage and understand. You've got great alignment with the Cloud Native engineers, the end users. Imagine that you're facilitating company's ability to reduce churn, attract more talent, retain talent. But what are some of the business outcomes? Like to the customer experience to the brand? >> Ian: Sure. >> Talk about it in some of those contexts. >> Yeah. One of the things that not a lot of organizations think about is, what is the reliability of my observability solution? It's like, well, that's not what I'm focused on. I'm focused on the reliability of my own website. Okay, let's take the, common open source pattern. I'm going to deploy my observability solution next to my core site infrastructure. Okay, I now have a platform problem because DNS stopped working in cloud provider of my choice. It's also affecting my observability solution. So at the moment that I need- >> And the tool chain and everything else. >> Yeah. At the moment that I need it the most to understand what's going on and to be able to know triage and understand that fails me at the same time. It's like, so reliability has this very big impact. So being able to make sure that my solution's reliable so that when I need it the most, and I can affect reliability of my own solution, my own SLA. That's a really key aspect of it. One of the things though that we, look at is it's not just about the outcomes and the value, it's ROI, right? It's what are you investing to put into that? So we've talked a little bit about the engineering cost, there's the infrastructure cost, but there's also a massive data explosion, particularly with Cloud Native. >> Yes. Give us... Alright, put that into real world examples. A customer that you think really articulates the value of what Chronosphere is delivering and why you're different in the market. >> Yeah, so DoorDash is a great customer example. They're here at KubeCon talking about their experience with Chronosphere and you know, the Cloud Native technologies, Prometheus and those other components align with Chronosphere. But being able to undergo, you know, a transformation, they're a Cloud Native organization, but going a transformation from StatsD to very heavy microservices, very heavy Kubernetes and orchestration. And doing that with your massive explosion, particularly during the last couple of years, obviously that's had a very positive impact on their business. But being able to do that in a cost effective way, right? One of the dirty little secrets about observability in particular is your business growth might be, let's say 50%, 60%, your infrastructure spend in the cloud providers is maybe going to be another 10, 15% on top of that. But then you have the intersection of, well my engineers need more data to diagnose things. The business needs more data to understand what's going on. Plus we've had this massive explosion of containers and everything like that. So oftentimes your business growth is going to be more than doubled with your observability data growth and SaaS solutions and even your on-premises solutions. What's the main cost driver? It's the volume of data that you're processing and storing. And so Chronosphere one of the key things that we do, because we're focused on organizational pain for larger scale organizations, is well, how do we extract the maximum volume of the data you're generating without having to store all of that data and then present it not just from a cost perspective, but also from a performance perspective. >> Yes. >> John: Yeah. >> And so feeding all into developer productivity and also lowering that investment so that your return can stand out more clearly and more valuably when you are assessing that TCO. >> Better insights and outcomes drives developer productivity for sure. That also has top theme here at KubeCon this year. It always is, but this is more than ever 'cause of the velocity. My question for you, given that you're the field chief technology officer for Chronosphere and you have a unique position, you've got a great experience in the industry, been involved in some really big companies and cutting edge. What's the competitive landscape? 'Cause the customers sometimes are confused by all the pitches they're getting from other vendors. Some are bolting on observability. Some have created like I would say, a shim layer or horizontally scalable platform or platform engineering approach. It's a data problem. Okay. This is a data architecture challenge. You mentioned that many times. What's the difference between a pretender and a player in this space? What's the winning architecture look like? What's a, I won't say phony or fake solution, but ones that customers should be aware of? Because my opinion, if you have a gap in the data or you configure it wrong, like a bolt on and say DNS crashes you're dead in the water. >> Ian: Yeah. >> What's the right approach from a customer standpoint? How do they squint through all the noise to figure out what's the right approach? >> Yeah, so I mean, I think one of the ways, and I've worked with customers in a pre-sales capacity for a very long time I know all the tricks of guiding you through. I think it needs to be very clear that customers should not be guided by the vendor. You don't talk to one vendor and they decide, Oh, I'm going to evaluate based off this. We need to particularly get away from feature based evaluations. Features are very important, but they're all have to be aligned around outcomes. And then you have to clearly understand, where am I today? What do I do today? And what is going to be the transformation that I have to go through to take advantage of these features? They can get very entrancing to say, Oh, there's a list of 25 features that this solution has that no one else has, but how am I going to get value out of that? >> I mean, distributed tracing is a distributed word. Distributed is the key word. This is a system architecture. The holistic big picture comes in. How do they figure that out? Knowing what they're transforming into? How does it fit in? >> Ian: Yeah. >> What's the right approach? >> Too often I say distributed tracing, particularly, you know, bought, because again, look at the shiny features look at the the premise and the MTTR expectations, all these other things. And then it's off to the side. We go through the traditional usage of metrics very often, very log heavy approaches, maybe even some legacy APM. And then it's sort of at last resort. And out of all the tools, I think distributed tracing is the worst in the problem we talked about earlier where the most sophisticated engineers, the ones who are being longest tenured, are the only ones who end up using it. So adoption is really, really poor. So again, what do we do today? Well, we alert, we probably want to understand our symptoms, but then what is the key problem? Oh, we spend a lot of time digging into the where the problem exists in my architecture, we talked about, you know, getting every engineer in at the same time, but how do we reduce the number of engineers involved? How do we make it so that, well, this looks like a great day one experience, but what is my day 30 experience like? Day 90. How is the product get more valuable? How do I get my most senior engineers out of this, not just on day one, but as we progress through it? >> You got to operationalize it. That's the key. >> Yeah, Correct. >> Summarize this as we wrap here. When you're in customer conversations, what is the key factor behind Chronosphere's success? If you can boil it down to that key nugget, what is it? >> I think the key nugget is that we're not just fixated on sort of like technical features and functions and frankly gimmicks of like, Oh, what could you possibly do with these three pillars of data? It's more about what can we do to solve organizational pain at the high level? You know, things like what is the cost of these solutions? But then also on the individual level, it's like, what exactly is an engineer trying to do? And how is their quality of life affected by this kind of tooling? And it's something I'm very passionate about. >> Sounds like it. Well, the quality of life's important, right? For everybody, for the business, and ultimately ends up affecting the overall customer experience. So great job, Ian, thank you so much for joining John and me talking about what you guys are doing beyond the three pillars of observability at Chronosphere. We appreciate your insights. >> Thank you so much. >> John: All right. >> All right. For John Furrier and our guest, I'm Lisa Martin. You're watching theCUBE live Friday morning from KubeCon + CloudNativeCon 22' from Detroit. Our next guest joins theCUBE momentarily, so stick around. (upbeat music)

>>Welcome back to NYC the Cube's coverage of Mongo DB 2022, a few thousand people here at least bigger than many people, perhaps expected, and a lot of buzz going on and we're gonna talk devs. I'm really excited to welcome back. Robbie Bellson who's the developer relations lead at Verizon and Ian Massingham. Who's the vice president of developer relations at Mongo DB Jens. Good to see you. Great >>To be here. >>Thanks having you. So Robbie, we just met a few weeks ago at the, the red hat summit in Boston and was blown away by what Verizon is doing in, in developer land. And of course, Ian, you know, Mongo it's rayon Detra is, is developers start there? Why is Mongo so developer friendly from your perspective? >>Well, it's been the ethos of MongoDB since day one. You know, back when we launched the first version of MongoDB back in 2009, we've always been about making developers lives easier. And then in 2016, we announced and released MongoDB Atlas, which is our cloud managed service for MongoDB, you know, starting with a small number of regions built on top of AWS and about 2,500 adoption events per week for MongoDB Atlas. After the first year today, MongoDB Atlas provides a managed service for MongoDB developers around the world. We're present in almost a hundred cloud regions across S DCP and Azure. And that adoption number is now running at about 25,000 developers a week. So, you know, the proof are in proof is really in the metrics. MongoDB is an incredibly popular platform for developers that wanna build data-centric applications. You just can't argue with the metrics really, >>You know, Ravi, sometimes there's an analyst who come up with these theories and one of the theories I've been spouting for a long time is that developers are gonna win the edge. And now to, to see you at Verizon building out this developer community was really exciting to me. So explain how you got this started with this journey. >>Absolutely. As you think about Verizon 5g edge or mobile edge computing portfolio, we knew from the start that developers would play a central role and not only consuming the service, but shaping the roadmap for what it means to build a 5g future. And so we started this journey back in late 20, 19 and fast forward to about a year ago with Mongo, we realized, well, wait a minute, you look at the core service offerings available at the edge. We didn't know really what to do with data. We wanted to figure it out. We wanted the vote of confidence from developers. So there I was in an apartment in Colorado racing, your open source Mongo against that in the region edge versus region, what would you see? And we saw tremendous performance improvements. It was so much faster. It's more than 40% faster for thousands and thousands of rights. And we said, well, wait a minute. There's something here. So what often starts is an organic developer, led intuition or hypothesis can really expand to a much broader go to market motion that really brings in the enterprise. And that's been our strategy from day one. Well, >>It's interesting. You talk about the performance. I, I just got off of a session talking about benchmarks in the financial services industry, you know, amazing numbers. And that's one of the hallmarks of, of Mongo is it can play in a lot of different places. So you guys both have developer relations in your title. Is that how you met some formal developer relations? >>We were a >>Program. >>Yeah, I would say that Verizon is one of the few customers that we also collaborate with on a developer relations effort. You know, it's in our mutual best interest to try to drive MongoDB consumption amongst developers using Verizon's 5g edge network and their platform. So of course we work together to help, to increase awareness of MongoDB amongst mobile developers that want to use that kind of technology. >>But so what's your story on this? >>I mean, as I, as I mentioned, everything starts with an organic developer discovery. It all started. I just cold messaged a developer advocate on Twitter and here we are at MongoDB world. It's amazing how things turn out. But one of the things that's really resonated with me as I was speaking with one of, one of your leads within your organization, they were mentioning that as Mongo DVIA developed over the years, the mantra really became, we wanna make software development easy. Yep. And that really stuck with me because from a network perspective, we wanna make networking easy. Developers are not gonna care about the internals of 5g network. In fact, they want us to abstract away those complexities so that they can focus on building their apps. So what better co-innovation opportunity than taking MongoDB, making software easy, and we make the network easy. >>So how do you think about the edge? How does you know variety? I mean, to me, you know, there's a lot of edge use cases, you know, think about the home Depot or lows. Okay, great. I can put like a little mini data center in there. That's cool. That's that's edge. Like, but when I think of Verizon, I mean, you got cell towers, you've got the far edge. How do you think about edge Robbie? >>Well, the edge is a, I believe a very ambiguous term by design. The edge is the device, the mobile device, an IOT device, right? It could be the radio towers that you mentioned. It could be in the Metro edge. The CDN, no one edge is better than the other. They're all just serving different use cases. So when we talk about the edge, we're focused on the mobile edge, which we believe is most conducive to B2B applications, a fleet of IOT devices that you can control a manufacturing plant, a fleet of ground and aerial robotics. And in doing so you can create a powerful compute mesh where you could have a private network and private mobile edge computing by way of say an AWS outpost and then public mobile edge computing by way of AWS wavelength. And why keep them separate. You could have a single compute mesh even with MongoDB. And this is something that we've been exploring. You can extend Atlas, take a cluster, leave it in the region and then use realm the mobile portfolio and spread it all across the edge. So you're creating that unified compute and data mesh together. >>So you're describing what we've been expecting is a new architecture emerging, and that's gonna probably bring new economics of new use cases, right? Where are we today in that first of all, is that a reasonable premise that this is a sort of a new architecture that's being built out and where are we in that build out? How, how do you think about the, the future of >>That? Absolutely. It's definitely early days. I think we're still trying to figure it out, but the architecture is definitely changing the idea to rip out a mobile device that was initially built and envisioned for the device and only for the device and say, well, wait a minute. Why can't it live at the edge? And ultimately become multi-tenant if that's the data volume that may be produced to each of those edge zones with hypothesis that was validated by developers that we continue to build out, but we recognize that we can't, we can't get that static. We gotta keep evolving. So one of our newest ideas as we think about, well, wait a minute, how can Mongo play in the 5g future? We started to get really clever with our 5g network APIs. And I, I think we talked about this briefly last time, 5g, programmability and network APIs have been talked about for a while, but developers haven't had a chance to really use them and our edge discovery service answering the question in this case of which database is the closest database, doesn't have to be invoked by the device anymore. You can take a thin client model and invoke it from the cloud using Atlas functions. So we're constantly permuting across the entire portfolio edge or otherwise for what it means to build at the edge. We've seen such tremendous results. >>So how does Mongo think about the edge and, and, and playing, you know, we've been wondering, okay, which database is actually gonna be positioned best for the edge? >>Well, I think if you've got an ultra low latency access network using data technology, that adds latency is probably not a great idea. So MongoDB since the very formative years of the company and product has been built with performance and scalability in mind, including things like in memory storage for the storage engine that we run as well. So really trying to match the performance characteristics of the data infrastructure with the evolution in the mobile network, I think is really fundamentally important. And that first principles build of MongoDB with performance and scalability in mind is actually really important here. >>So was that a lighter weight instance of, of Mongo or not >>Necessarily? No, not necessarily. No, no, not necessarily. We do have edge cashing with realm, the mobile databases Robbie's already mentioned, but the core database is designed from day one with those performance and scalability characteristics in mind, >>I've been playing around with this. This is kind of a, I get a lot of heat for this term, but super cloud. So super cloud, you might have data on Preem. You might have data in various clouds. You're gonna have data out at the edge. And, and you've got an abstraction that allows a developer to, to, to tap services without necessarily if, if he or she wants to go deep into the S great, but then there's a higher level of services that they can actually build for their customers. So is that a technical reality from a developer standpoint, in your view, >>We support that with the Mongo DB multi-cloud deployment model. So you can place Mongo DB, Atlas nodes in any one of the three hyperscalers that we mentioned, AWS, GCP or Azure, and you can distribute your data across nodes within a cluster that is spread across different cloud providers. So that kinds of an kind of answers the question about how you do data placement inside the MongoDB clustered environment that you run across the different providers. And then for the abstraction layer. When you say that I hear, you know, drivers ODMs the other intermediary software components that we provide to make developers more productive in manipulating data in MongoDB. This is one of the most interesting things about the technology. We're not forcing developers to learn a different dialect or language in order to interact with MongoDB. We meet them where they are by providing idiomatic interfaces to MongoDB in JavaScript in C sharp, in Python, in rust, in that in fact in 12 different pro programming languages that we support as a first party plus additional community contributed programming languages that the community have created drivers for ODMs for. So there's really that model that you've described in hypothesis exist in reality, using >>Those different Compli. It's not just a series of siloed instances in, >>In different it's the, it's the fabric essentially. Yeah. >>What, what does the Verizon developer look like? Where does that individual come from? We talked about this a little bit a few weeks ago, but I wonder if you could describe it. >>Absolutely. My view is that the Verizon or just mobile edge ecosystem in general for developers are present at this very conference. They're everywhere. They're building apps. And as Ian mentioned, those idiomatic interfaces, we need to take our network APIs, take the infrastructure that's being exposed and make sure that it's leveraging languages, frameworks, automation, tools, the likes of Terraform and beyond. We wanna meet developers where they are and build tools that are easy for them to use. And so you had talked about the super cloud. I often call it the cloud continuum. So we, we took it P abstraction by abstraction. We started with, will it work in one edge? Will it work in multiple edges, public and private? Will it work in all of the edges for a given region, public or private, will it work in multiple regions? Could it work in multi clouds? We've taken it piece by piece by piece and in doing so abstracting way, the complexity of the network, meaning developers, where they are providing those idiomatic interfaces to interact with our API. So think the edge discovery, but not in a silo within Atlas functions. So the way that we're able to converge portfolios, using tools that dev developers already use know and love just makes it that much easier. Do, >>Do you feel like I like the cloud continuum cause that's really what it is. The super cloud does the security model, how does the security model evolve with that? >>At least in the context of the mobile edge, the attack surface is a lot smaller because it's only for mobile traffic not to say that there couldn't be various configuration and human error that could be entertained by a given application experience, but it is a much more secure and also reliable environment from a failure domain perspective, there's more edge zones. So it's less conducive to a regionwide failure because there's so many more availability zones. And that goes hand in hand with security. Mm. >>Thoughts on security from your perspective, I mean, you added, you've made some announcements this week, the, the, the encryption component that you guys announced. >>Yeah. We, we issued a press release this morning about a capability called queryable encryption, which actually as we record this Mark Porter, our CTO is talking about in his keynote, and this is really the next generation of security for data stored within databases. So the trade off within field level encryption within databases has always been very hard, very, very rigid. Either you have keys stored within your database, which means that your memory, so your data is decrypted while it's resident in memory on your database engine. This allow, of course, allows you to perform query operations on that data. Or you have keys that are managed and stored in the client, which means the data is permanently OBS from the engine. And therefore you can't offload query capabilities to your data platform. You've gotta do everything in the client. So if you want 10 records, but you've got a million encrypted records, you have to pull a million encrypted records to the client, decrypt them all and see performance hit in there. Big performance hit what we've got with queryable encryption, which we announced today is the ability to keep data encrypted in memory in the engine, in the database, in the data platform, issue queries from the client, but use a technology called structural encryption to allow the database engine, to make decisions, operate queries, and find data without ever being able to see it without it ever being decrypted in the memory of the engine. So it's groundbreaking technology based on research in the field of structured encryption with a first commercial database provided to bring this to market. >>So how does the mobile edge developer think about that? I mean, you hear a lot about shifting left and not bolting on security. I mean, is this, is this an example of that? >>It certainly could be, but I think the mobile edge developer still stuck with how does this stuff even work? And I think we need to, we need to be mindful of that as we build out learning journeys. So one of my favorite moments with Mongo was an immersion day. We had hosted earlier last year where we, our, from an enterprise perspective, we're focused on BW BS, but there's nothing stopping us. You're building a B2C app based on the theme of the winner Olympics. At the time, you could take a picture of Sean White or of Nathan Chen and see that it was in fact that athlete and then overlaid on that web app was the number of medals they accrued with the little trumpeteer congratulating you for selecting that athlete. So I think it's important to build trust and drive education with developers with a more simple experience and then rapidly evolve overlaying the features that Ian just mentioned over time. >>I think one of the keys with cryptography is back to the familiar messaging for the cloud offloading heavy lifting. You actually need to make it difficult to impossible for developers to get this wrong, and you wanna make it as easy as possible for developers to deal with cryptography. And that of course is what we're trying to do with our driver technology combined with structure encryption, with query encryption. >>But Robbie, your point is lots of opportunity for education. I mean, I have to say the developers that I work with, it's, I'm, I'm in awe of how they solve problems and I, and the way they solve problems, if they don't know the answer, they figure out how to go get it. So how, how are your two communities and other communities, you know, how are they coming together to, to solve such problems and share whether it's best practices or how do I do this? >>Well, I'm not gonna lie in person. Events are a bunch of fun. And one of the easiest domain knowledge exchange opportunities, when you're all in person, you can ideate, you can whiteboard, you can brainstorm. And often those conversations are what leads to that infrastructure module that an immersion day features. And it's just amazing what in person events can do, but community groups of interest, whether it's a Twitch stream, whether it's a particular code sample, we rely heavily on digital means today to upscale the developer community, but also build on by, by means of a simple port request, introduce new features that maybe you weren't even thinking of before. >>Yeah. You know, that's a really important point because when you meet people face to face, you build a connection. And so if you ask a question, you're more likely perhaps to get an answer, or if one doesn't exist in a, in a search, you know, you, oh, Hey, we met at the, at the conference and let's collaborate on this guys. Congratulations on, on this brave new world. You're in a really interesting spot. You know, developers, developers, developers, as Steve bomber says screamed. And I was glad to see Dave was not screaming and jumping up and down on the stage like that, but, but the message still resonates. So thank you, definitely appreciate. All right, keep it right there. This is Dave ante for the cubes coverage of Mago DB world 2022 from New York city. We'll be right back.

>>Okay, welcome back everyone. Cube's coverage here. Live on the floor at AWS summit, 2022, an in person event in San Francisco. Of course, AWS summit, 2022 in New York city is coming up this summer. The cube will be there as well. Make sure you check us out then too, but we day two of coverage had a great guest here. I Han VP of developer relations, Mongo DB, formally of AWS. We've been known each other for a long time doing, uh, developer relations at Mongo DB. Welcome to the queue. Good to see >>You. Thank to be here. Thanks for inviting me, John. It's great >>To, so Mongo DB is, um, first of all, stocks' doing really well right now. Businesswise is good, but I still think it's undervalue. A lot of people think is, is a lot more going huge success with Atlas. So congratulations to the team over there. Um, what's the update? What's the relationship withs, you know, guys have been great partners for years. What's the new thing. Yeah. >>So MongoDB Atlas obviously runs on several different major cloud providers, but AWS is the largest partner that we work with in the public cloud. So the majority of our Atlas workloads for our customers are running on the AWS platform. And just earlier this year, we announced a new strategic collaboration agreement with AWS. That's gonna further strengthen and deepen that partnership that we have with them. >>What's the main product value right now on the scale on, on Atlas, what's the drive in the revenue momentum. >>So, I mean, you know, there's a huge trend in the industry towards cloud managed databases, right? You look back 10, 15 years ago when we first met, most customers were only and operating their own data infrastructure, either running it in their own data centers, or maybe if they were really early using the primitives that cloud providers like AWS offered to run their databases in the cloud when Amazon launched RDS back in 2009, I think it was, we started to see this trend towards cloud managed databases. We followed that with our own Atlas offering back in 2016. And as Andy jazzy from AWS would say very often it's offloading that UND differentiated, heavy lifting, allowing developers to focus on building applications. They don't have to win and operate the data infrastructure. We do it for them, and that has proven incredibly popular amongst our customers. You know, Atlas route right now is growing at 50, sorry, 85% car year on year growth. >>You know, um, I've been following MongoDB for a long, long time. I mean, going back to the lamp stack days, you know, and you think about what Mongo has done as a product because of the developer traction, you know, Mongo can't do this, just keeps getting better every year. And, and the, I think the stickiness with developers is a real big part of that. Can you your view there cuz you're in VE relations. I mean, developers all love Mongo. They're teaching in school. People are picking up a side hustles, they're coding on it, using it all everywhere. I mean it's well known. >>There's a few different reasons for that. I think the main one is the, the document orientated model that we use, the document data models that are used by Mongo DB, just a net way for developers to work with data. And then, uh, we've invested in creating 16 first party drivers that allow developers using various different programming languages, whether that's JavaScript or Python or rust to integrate MongoDB, natively and idiomatic with their software. So it's very, very easy for a developer to pick up MongoDB, grab one of these drivers from their package manager of their choice and then build applications that natively manipulate data inside MongoDB, whether that's MongoDB Atlas or our enterprise edition on their own premises. They get a very consistent and very easy to, I easy to use developer experience with our, with our platform. >>Talk about the go to market with AWS. You guys also have a tightly coupled relationships. There's been announcements there recently. Uh, what's changing most right now that people should pay attention to. Well, >>The first thing is there's a huge amount of technical integration between MongoDB and AWS services. And that's the basis for many of our customers choosing to run Mon Mongo DB on AWS. We're active in 23 AWS regions around the world. And there's many other integration points as well, like cryptographic protection of Mongo MongoDB, stored data using Amazon cryptographic services, for example, or building serverless applications with AWS Lambda and MongoDB servers. So there's a ton of technical integration. Yeah, but what we started to work on now is go to market integration with AWS as well. So you can buy Mongo DB Atlas through AWS's marketplace. You can use the payer, you go offering to pay for it with your AWS bill. And then we're collaborating with AWS on migrations and other joint go to market activities as well. That >>Means get incentives, the sales people at AWS. >>Of course our moreover I mean, it's just really easy for customers, really easy for developers to consume. Yeah, they don't need to contract with MongoDB. They can use their existing AWS contracting, their existing discounting relationships and pre purchasing arrangements with AWS to consume Atlas. >>It's the classic meet the customers where they >>Are exactly right. Meet the developer where they are and meet the customers where they are now with this new model as well. >>Yeah. I love marketplace. I think it's been great. You know, even with its kind of catalog and vibe, I think it's gonna get better and better, uh, over there teams doing good work. Um, and it's easy to consume. That's key. >>Yeah. Super easy. Reduce that friction and make it real easy for developers to adopt this. Right. >>Talk about some of the top customers that you guys share with AWS. What are some of the customers you guys have together and what the benefits of the >>Relationship joint references that we talk about? A lot, one of them is Shutterfly. So in the photographic products area, they built a eCommerce offering with MongoDB and AWS. The second is seven 11 with seven 11. We're doing a lot in the mobile space. So edge applications, we've got a feature in MongoDB Atlas that allows you to synchronize data with databases on mobile devices. Those can be phones point of sale devices or handheld devices that might be used in the parcel industry, for example. So seven 11 using us in that way. And then lastly with Pitney Bowes, we've got a big digital transformation project with Pitney Bowes where they've reimagined their, uh, postage and packaging services, delivering those to their customers, using MongoDB as a data store as well. >>I wanna get in some of the trends, you've got a great per you know, you know, Mongo from Amazon side and now you're there. Um, Mongo's, as you pointed out has, has been around for a long time. What are some of the stats? I mean, how many customers, how many countries? Well, it's pretty massive >>Mind. We've got almost quarter of a billion downloads today, 240 million MongoDB downloads since we launched the first product <laugh>, we've got 33,000 active customers that are using MongoDB Atlas today and we're running well over a million free tier clusters on MongoDB Atlas across all of the different providers where we operate the service as well. So these numbers are, you know, mind blowing in terms of scale. Uh, but of course at the core of that is operational excellence. Customers love Mongo DBS because they don't have to operate it themselves. They don't have to deal with fairly conditions. They don't have to deal with scaling. They don't have to deal with deployment. We all, we do all of those things as part of the service offering and customers get an endpoint that they can use with their applications to store and retrieve data reliably. And with consistently high perform, >>You know, it's, you know, in the media, something has to be dead. Someone's the death of the iPhone, the death of this, nothing that really dies. Mongo DB has always been kind of like talked about, well, it doesn't scale on the high end. Of course, Oracle was saying that, I mean, all the, all the big database vendors were kind of throwing darts at, at Mongo, uh, DB, uh, but it kept scaling. Atlas is a whole nother. Could you just unpack that a little bit more? Why is it so important? Because scale is just, I mean, it's, it's horizontal, but it's also performant. >>Exactly. Right. So with, uh, Mongo DB's document access model that I've described already, you break some of the limitations that exist inside traditional relational databases. So, you know, they don't scale well, if you've got high concurrent and see of data access, and they're typically difficult and expensive to scale because you need to share data. Once you grow beyond individual cluster nodes, and you'll know that all relational databases suffer from these same kinds of issues with non relational systems, no SQL systems like MongoDB, you have to think a little bit more about design at the beginning. So designing database to cater for the different access patterns that you have, but in return for that upfront preparation, that design work, you get near limitless, scalability and performance will scale nearly linearly with that scalability as well. So very much more high performance, very much more simplicity for the developer as their database gets larger and their cluster gets larger to support it. >>Yeah. You know, Amazon web service has always had an a and D jazz. We talk to us all the time, every interview I've done with Swami and Matt wood or whoever on the team and executive levels always said the same thing. There's not one database to rule the world, right? Obvious you're talking about Oracle, but even within AWS customers, they're mixing and matching databases based on use cases. So in distributed environment, they're all working together. So, um, you guys fit nicely into that. So how does that, >>I think strategy slightly counterbalances that so, you know, they would say use the specific tool for the specific task that you have in hand. Yeah. What we try to focus on is creating the simple and most effective developer experience that we can, and then supporting different facets to the product in order to allow developers to different use cases. A really good example with something like MongoDB Atlas search. So we integrated Apache Luine into MongoDB Atlas. Customers can very simply apply Apache Luine search indexes to the data that they've got in MongoDB. And then they can interact with that search data using the same drivers as an API. Yeah, yeah. That they use for regular queries. So if you want to run search on your application data, you don't need a separate open search or elastic search cluster, just turn on MongoDB Atlas search and use that, that search facet. So it's interest and we have other capabilities that it's >>Vertically integrating inside within Mongo, >>Correct? Yes. That's better. Yeah. With the guy, all of creating a really simple and effective developer experience, boosting developer productivity and helping developers get more done in less time. >>You mentioned serverless earlier, what's the serverless angle with AWS when Mongo, >>Is there one? Yeah. So we have MongoDB serverless currently in preview, uh, has the same kind of characteristics that you would, or the characteristics that you would expect from a serverless data base. So consumption based model, you provision an endpoint and that will scale elastically in accordance with your usage and you get billed by consumption units so much like the serverless paradigm that we've seen delivered by AWS, the same kind of model for Mongo, DB, Atlas serverless. >>What, what attracted you to Mongo DBS? So you knew them before, or you moved over there. Um, what's going on there? What's the culture like right now? Oh, >>The culture's great. I mean, it's a much smaller company than AWS where I was before, you know, it's a very large organization. And one of the things that I really like about MongoDB is, as I've said earlier, we can serve the different use cases that a developer might have with a single product, with different aspects, to it, different facets to it. Uh, and it's a really great conversation to have with a, with a developer, with a developer customer, to be able to offer one thing that helps them solve five or six different problems that have traditionally been quite hard for them to wrestle with quite difficult for them to, to deal with. And then we've got this focus on developer experience through these driver packages that we have as well. So it's really great to have as a developer relations pro have that kind of tooling in my kit bag that can help developers become more effective. >>Talk about tooling, cuz you know, I always have, uh, kind of moments where I waffle between more. I love platforms, tools are being over overused, too many tools tool with the tool, you know, the expressions, but we're seeing from developers, the ones that don't want to go into the hood, we serverless plays beautifully. Yep. They want tools. They do. And, and the, the new engineering developers that are coming outta college and universities, they love tools. >>Yeah. And we actually have quite a few of those built into Mongo, DB Atlas. So inside Mongo, DB Atlas, we've got things like an index optimizer, which will suggest the best way that you might index your data for better perform months inside MongoDB, running on Atlas, we've got a data Explorer, which is much like another product that we've got called MongoDB compass that allows you to see and manipulate the data that you have stored within your database natively within the Atlas interface. Uh, and then we also have, uh, whole slew of different metrics, monitoring capabilities built into the platform as well. So these are aspects of Atlas that developers can take advantage of. And then over on the client side, visual studio code plugins. Yeah. So you can manipulate and operate with data directly inside visual studio code, which is obviously the most common and popular IDE out there today, as well as integration with things like infrastructure is code tools. So we support cloud formation for provisioning. We have CDK constructs inside. Yeah. The CDK construct library. We also have a lot of customers using Terraform to provision MongoDB across both AWS and other providers. So having that developer tooling of course is super important. Yeah. Aspect of the developer experience, trying to >>Build out deploying observability is a big one. How does that fit in? Cuz you knew need to talk and not only measure everything here, but talk to other systems. >>Yeah. So we recently announced a provider for Prometheus and Grafana. So we can emit metrics into those providers. Obviously CNCF projects, very common and popular inside customers that are running on Kubernetes. We've got a Kubernetes operator for MongoDB Atlas as well. Good. So you can provision MongoDB Atlas from within Kubernetes as well as having our own native metrics directly within Atlas as well. >>Ian you're crushing it. You got all the, the data, the fingertips. Are you gonna be at Cuban this year? Uh, >>I will be, but some of our team members will definitely be there. >>Yeah, we'll be at, uh, EU. The cube will be there. Great. Thanks for coming on. Appreciate the insight final world. I'll give you the last word. Tell the audience what's going on. What's at Mongo DB. What should they pay attention to? If they've used Mongo and are aware of it? What's the update. What's >>The so you should come to MongoDB world actually in New York at the beginning of June, June 7th, the ninth in the Javit center in New York. Gonna have our own show there. And of course we'd love to see you there. >>Okay. Cube comes here day two of eight, us summit, 2020, this Cub I'm John for your host. Stay with us more. Our coverage as day two winds down. Great coverage.

(upbeat music) >> Welcome to the AWS start up showcase, theCUBE's premiere platform and show. This is our second season, episode one of this program. I'm Lisa Martin, your host here with two guests here to talk about open source. Please welcome Andrew Backes, the VP of engineering at Armory, and one of our alumni, Ian Delahorne, the staff site, reliability engineer at Patreon. Guys, it's great to have you on the program. >> Thank you. >> Good to be back. >> We're going to dig into a whole bunch of stuff here in the next fast paced, 15 minutes. But Andrew, let's go ahead and start with you. Give the audience an overview of Armory, who you guys are, what you do. >> I'd love to. So Armory was founded in 2016 with the vision to help companies unlock innovation through software. And what we're focusing on right now is, helping those companies and make software delivery, continuous, collaborative, scalable, and safe. >> Got it, those are all very important things. Ian help the audience, if anyone isn't familiar with Patreon, it's a very cool platform. Talk to us a little bit about that Ian. >> Absolutely, Patreon is a membership platform for creators to be able to connect with their fans and for fans to be able to subscribe to their favorite creators and help creators get paid and have them earn a living with, just by being connected straight to their audience. >> Very cool, creators like podcasters, even journalists video content writers. >> Absolutely. There's so many, there's everything from like you said, journalists, YouTubers, photographers, 3D modelers. We have a nightclub that's on there, there's several theater groups on there. There's a lot of different creators. I keep discovering new ones every day. >> I like that, I got to check that out, very cool. So Andrew, let's go to your, we talk about enterprise scale and I'm using air quotes here. 'Cause it's a phrase that we use in every conversation in the tech industry, right? Scalability is key. Talk to us about what enterprise scale actually means from Armory's perspective. Why is it so critical? And how do you help enterprises to actually achieve it? >> Yeah, so the, I think a lot of the times when companies think about enterprise scale, they think about the volume of infrastructure, or volume of software that's running at any given time. There's also a few more things that go into that just beyond how many EC2 instances you're running or containers you're running. Also velocity, count how much time does it take you to get features out to your customers and then stability and reliability. Then of course, in enterprises, it isn't as simple as everyone deploying to the same targets. It isn't always just EC2, a lot of the time it's going to be multiple targets, EC2, it's going to be ECS, Lambda. All of these workloads are out there running. And how does a central platform team or a tooling team at a site enable that for users, enable deployment capabilities to those targets? Then of course, on top of that, there's going to be site specific technologies. And how do, how does your deployment tooling integrate with those site specific technologies? >> Is, Andrew is enterprise scale now even more important given the very transformative events, we've seen the last two years? We've seen such acceleration, cloud adoption, digital transformation, really becoming a necessity for businesses to stay alive. Do you think that, that skill now is even more important? >> Definitely, definitely. The, what we see, we've went through a wave of the, the first set of digital transformations, where companies are moving to the cloud and we know that's accelerating quite a bit. So that scale is all moving to the cloud and the amount of multiple targets that are being deployed to at any given moment, they just keep increasing. So that is a concern that companies need to address. >> Let's talk about the value, but we're going to just Spinnaker here in the deployment. But also let's start Andrew with the value that, Armory delivers on top of Spinnaker. What makes this a best of breed solution? >> Yeah, so on top of open-source Spinnaker, there are a lot of other building blocks that you're going to need to deploy at scale. So you're going to need to be able to provide modules or some way of giving your users a reusable building block that is catered to your site. So that is one of the big areas that Armory focuses on, is how can we provide building blocks on top of open source Spinnaker that sites can use to tailor the solution to their needs. >> Got it, tailor it to their needs. Ian let's bring you back into the conversation. Now, talk to us about the business seeds, the compelling event that led Patreon to choose Spinnaker on top of Armory. >> Absolutely. Almost three years ago, we had an outage which resulted in our payment processing slowed down. And that's something we definitely don't want to have happen because this would hinder creator's ability to get paid on time for them to be able to pay their employees, pay their rent, hold that hole, like everything that, everyone that depends on them. And there were many factors that went into this outage and one of them we identified is that it was very hard for us to, with our custom belt deploy tooling, to be able to easily deploy fast and to roll back if things went wrong. So I had used Spinnaker before to previous employer early on, and I knew that, that would be a tool that we could use to solve our problem. The problem was that the SRE team at Patreon at that time was only two people. So Spinnaker is a very complex product. I didn't have the engineering bandwidth to be able to, set up, deploy, manage it on my own. And I had happened to heard of Armory just that week before and was like, "This is the company that could probably help me solve my problems." So I engaged early on with Andrew and the team. And we migrated our customers deployed to, into Spinnaker and help stabilize our deploys and speed them up. >> So you were saying that the deployments were taking way too long before. And of course, as you mentioned from a payment processing perspective, that's people's livelihoods. So that's a pretty serious issue there. You found Armory a week into searching this seems like stuff went pretty quickly. >> And the week before the incident, they had randomly, the, one of the co-founders randomly reached out to me and was like, "We're doing this thing with Armory. You might be interested in this, we're doing this thing with Spinnaker, it's called Armory." And I kind of filed it away. And then they came fortuitous that we were able to use them, like just reach out to them like a week later. >> That is fortuitous, my goodness, what a good outreach and good timing there on Armory's part. And sticking with you a little bit, talk to us about what it is that the business challenges that Armory helps you to resolve? What is it about it that, that just makes you know this is the exact right solution for us? Obviously you talked about not going direct with Spinnaker as a very lean IT team. But what are some of the key business needs that it's solving? >> Yeah, there's several business things that we've been able to leverage Armory for. One of them as I mentioned, they, having a deployment platform that we know will give us, able deploys has been very important. There's been, they have a policy engine module that we use for making sure that certain environments can only be deployed to by certain individuals for compliance issues. We definitely, we use their pipelines as code module for being able to use, build, to build reusable deploy pipelines so that software engineers can easily integrate Spinnaker into their builds. Without having to know a lot about Spinnaker. There's like here, take these, take this pipeline module and add your variables into it, and you'll be off to the races deploying. So those are some of the value adds that Armory has been able to add on top of Spinnaker. On top of that, we use their managed products. So they have a team that's managing our Spinnaker installation, helping us with upgrades, helping up the issues, all that stuff that unlocks us to be able to focus on building our creators. Instead of focusing on operating Spinnaker. >> Andrew, back to you. Talk to me a little bit about as the VP of engineering, the partnership, the relationship that Armory has with Patreon and how symbiotic is it? How much are they helping you to develop the product that Armory is delivering to its customers? >> Yeah, one of the main things we want to make sure we do is help Patreon be successful. So that's, there are going to be some site specific needs there that we want to make sure that we are in tune with and that we're helping with, but really we view it as a partnership. So, Patreon has worked with us. Well, I can't believe it's been three years or kind of a little bit more now. But it's, it, we have had a lot of inner, a lot of feedback sessions, a lot of going back and forth on how we can improve our product to meet the needs of Patreon better. And then of course the wider market. So one thing that is neat about seeing a smaller team, SRE team that Ian is on, is they can depend on us more. They have less bandwidth with themselves to invest into their tooling. So that's the opportunity for us to provide those more mature building blocks to them. So that they can combine those in a way that makes them, that meets their needs and their business needs. >> And Ian, back to you, talk to me about how has the partnership with Armory? You said it's been almost three years now. How has that helped you do your job better as an SRE? What are some of the advantages of that, to that role? >> Yeah, absolutely. Armory has been a great partner to work with. We've used their expertise in helping to bring new features into the open-source Spinnaker. Especially when we decided that we wanted to not only deploy to EC2 instances, but we wanted to play to elastic container service and Lambdas to shift from our normal instance based deploys into the containerization. There were several warrants around the existing elastic container service deploy, and Lambda deploys that we were able to work with Armory and have them champion some changes inside open-source as well as their custom modules to help us be able to shift our displays to those targets. >> Got it. Andrew back over to you, talk to me, I want to walk through, you talked about from an enterprise scale perspective, some of the absolute critical components there. But I want to talk about what Armory has done to help customers like Patreon to address things like speed to market, customer satisfaction as Ian was talking about, the compelling event was payment processing. A lot of content creators could have been in trouble there. Talk to, walk me through how you're actually solving those key challenges that not just Patreon is facing, but enterprises across industries. >> Yeah, of course, so the, talking to specifically to what brought Ian in was, a problem that they needed to fix inside of their system. So when you are rolling out a change like that, you want it to be fast. You want to get that chain, change out very quickly, but you also want to make sure that the deployment system itself is stable and reliable. So the last thing you're going to want is any sort of hiccup with the tool that you're using to fix your product, to roll out changes to your customers. So that is a key focus area for us in everything that we do is we make sure that whenever we're building features that are going to expand capabilities, deployment capabilities. That we're, we are focusing firstly on stability and reliability of the deployment system itself. So those are a few features, a few focus areas that we continually build into the product. And you can, I mean, I'm sure a lot of enterprises know that as soon as you start doing things at massive scale, sometimes the stability and reliability, can, you'll be jeopardized a little bit. Or you start hitting against those limits or what are the, what walls do you encounter? So one of the key things we're doing is building ahead of that, making sure that our features are enabling users to hit deployment scales they've never seen or imagined before. So that's a big part of what Armory is. >> Ian, can you add a number to that in terms of the before Armory and the after in terms of that velocity? >> Absolutely, before Armory our deploys would take some times, somewhere around 45 minutes. And we cut that in half, if not more to down to like the like 16 to 20 minute ranges where we are currently deploying to a few hundred hosts. So, and that is the previous deployment strategy would take longer. If we scaled up the number of instances for big events, like our payment processing we do the first of the month currently. So being able to have that and know that our deploys will take about the same amount of time each time, it will be faster. That helps us bring features to create some fans a lot faster. And the stability aspect has also been very important, knowing that we have a secure way to roll back if needed, which you didn't have previously in case something goes wrong, that's been extremely useful. >> And I can imagine, Ian that velocity is critical because I mean more and more and more these days, there are content creators everywhere in so many different categories that we've talked about. Even nightclubs, that to be able to deliver that velocity through a part, a technology like Armory is table-stakes for against business. >> Absolutely, yeah. >> Andrew, back over to you. I want to kind of finish out here with, in the last couple of years where things have been dynamic. Have you seen any leading indices? I know you guys work with enterprises across organizations and Fortune 500s. But have you seen any industries in particular that are really leaning on Armory to help them achieve that velocity that we've been talking about? >> We have a pretty good spread across the market, but since we are focused on cloud, to deploy to cloud technologies, that's one of the main value props for Armory. So that's going to be enabling deployments to AWS in similar clouds. So the companies that we work with are really ones that have either already gone through that transformation or are on their journey. Then of course, now Kubernetes is a force, it's kind of taken over. So we're getting pulled into even more companies that are embracing Kubernetes. So I wouldn't say that there's an overall trend, but we have customers all across the Fortune 500, all across mid-market to Fortune 500. So there's depending on the complexity of the corporation itself or the enterprise itself we're able to do. I think Ian mentioned our policy engine and a few other features that are really tailored to companies that have restricted environments and moving into the cloud. >> Got it, and that's absolutely critical these days to help organizations pivot multiple times and to get that speed to market. 'Cause that's, of course as consumers, whether we're on the business side or the commercial side, we have an expectation that we're going to be able to get whatever we want A-S-A-P. And especially if that's payments processing, that's pretty critical. Guys, thank you for joining me today, talking about Armory, built on Spinnaker, what it's doing for customers like Patreon. We appreciate your time and your insights. >> Thank you so much. >> Thank you. Thank you so much. >> For my guests, I'm Lisa Martin. You're watching theCUBE's, AWS startup showcase, season two, episode one. (upbeat music)

>>Welcome to the cube conversation here. I'm Sean ferry with the cube in Palo Alto, California. We've got two great guests here featuring armory who has with them Patrion open-source and talking open source and the enterprise. I'm your host, John ferry with the cube. Thanks for watching guys. Thanks for coming on. Really appreciate. I've got two great guests, Ben mapping, and SVP, a strategic partner in the armory and Ian Della horn, S staff SRE at Patrion gentlemen, you know, open source and enterprise is here and we wouldn't talk about thanks for coming. I appreciate it. >>Yeah. Thank you, John. Really happy to be here. Thank you to the Cuban and your whole crew. I'll start with a quick intro. My name is Ben Mappin, farmers founders, lead strategic partnerships. As John mentioned, you know, it all, it really starts with a premise that traditional businesses, such as hotels, banks, car manufacturers are now acting and behaving much more like software companies than they did in the past. And so if you believe that that's true. What does it mean? It means that these businesses need to get great at delivering their software and specifically to the cloud, like AWS. And that's exactly what armory aims to do for our customers. We're based on opensource Spinnaker, which is a continuous delivery platform. And, and I'm very happy that Ian from Patrion is here to talk about our journey together >>And introduce yourself what you do at Patriot and when Patrion does, and then why you guys together here? What's the, what's the story? >>Absolutely. Hi, John and Ben. Thanks for, thanks for having me. So I am Ian. I am a site reliability engineer at Patrion and Patrion is a membership platform for creators. And what we're our mission is to get creators paid, changing the way the art is valued so that creators can make money by having a membership relationship with, with fans. And we are, we're built on top of AWS and we are using Spinnaker with armory to deploy our applications that, you know, help, help creators get paid. Basically >>Talk about the original story of Ben. How are you guys together? What brought you together? Obviously patron is well-known in the creator circles. Congratulations, by the way, all your success. You've done a great service for the industry and have changed the game you were doing creators before it was fashionable. And also you got some cutting-edge decentralization business models as well. So again, we'll come back to that in a minute, but Ben, talk about how this all comes together. Yeah, >>Yeah. So Ian's got a great kind of origin story on our relationship together. I'll give him a lead in which is, you know, what we've learned over the years from our large customers is that in order to get great at deploying software, it really comes down to three things or at least three things. The first being velocity, you have to ship your software with velocity. So if you're deploying your software once a quarter or even once a year, that does no good to your customers or to your business, like just code sitting in a feature branch on a shelf, more or less not creating any business value. So you have to ship with speed. Second, you have to ship with reliability. So invariably there will be bugs. There will be some outages, but you know, one of the things that armory provides with Spinnaker open sources, the ability to create hardened deployment pipeline so that you're testing the right things at the right times with the right folks involved to do reviews. >>And if there is hopefully not, but if there is a problem in production, you're isolating that problem to a small group of users. And then we call this the progressive deployment or Canary deployment where you're deploying to a small number of users. You measure the results, make sure it's good, expand it and expand it. And so I think, you know, preventing outages is incredibly incredibly important. And then the last thing is being able to deploy multi target multi-cloud. And so in the AWS ecosystem, we're talking about ECS, EKS Lambda. And so I think that these pieces of value or kind of the, the pain points that, that enterprises face can resonate with a lot of companies out there, including ENN Patriot. And so I'll, I'll, I'll let you tell the story. >>Yeah, go ahead. Absolutely. Thanks. Thanks for the intro, man. So background background of our partnership with armory as back in the backend, February of 2019, we had a payments payments slowed down for payments processing, and we were risking not getting creators paid on time, which is a doc great for creators because they rely on us for income to be able to pay themselves, pay their rent or mortgage, but also pay staff because they have video editors, website admins, people that nature work with them. And there were, they're a very, there's a very many root causes to this, to this incident, all kind of culminate at once. One of the things that we saw was that deploying D point fixes to remediate. This took too long or taking at least 45 minutes to deploy a new version of the application. And so we've had continuous delivery before using a custom custom home built, rolling deploy. >>We needed to get that time down. We also needed to be secure in our knowledge of like that deploy was stable. So we had had to place a break in the middle due to various factors that that can happen during the deploy previously, I had used a Spinnaker at previous employers. I have been set it up myself and introduced it. And I knew about, I knew like, oh, this is something we could, this would be great. But the Patriot team, the patron SRE team at that time was two people. So I don't have the ability to manage Spinnaker on my own. It's a complex open-source product. It can do a lot of things. There's a lot of knobs to tweak a lot of various settings and stuff you need to know about tangentially. One of the co-founders of, of armory had been, had to hit, had hit me up earlier. I was like, Hey, have you heard of armory? We're doing this thing, opens our Spinnaker, we're packaging this and managing it, check us out if you want. I kind of like filed it away. Like, okay, well that might be something we can use later. And then like two weeks later, I was like, oh wait, this company that does Spinnaker, I know of them. We should probably have a conversation with them and engage with them. >>And so you hit him up and said, Hey, too many knobs and buttons to push what's the deal. >>Yeah, exactly. Yeah. So I was, I was like, Hey, so by the way, I about that thing, how, how soon can you get someone get someone over here? >>So Ben take us through the progression. Cause that really is how things work in the open source. Open source is really one of those things where a lot of community outreach, a lot of people are literally a one degree or two separation from someone who either wrote the project or is involved in the project. Here's a great example. He saw the need for Spinnaker. The business model was there for him to solve. Okay. Fixes rolling deployments, homegrown all the things, pick your pick, your use case, but he wanted to make it easier. This tends to, this is kind of a pattern. What did you guys do? What's the next step? How did this go from here? >>Yeah. You know, Spinnaker being source is critical to armory's success. Many companies, not just pastry on open source software, I think is not really debatable anymore in terms of being applicable to enterprise companies. But the thing with selling open source software to large companies is that they need a backstop. They need not just enterprise support, but they need features and functionality that enable them to use that software at scale and safely. And so those are really the things that, that we focus on and we use open source as a really, it's a great community to collaborate and to contribute fixes that other companies can use. Other companies contribute fixes and functionality that we then use. But it's, it's really a great place to get feedback and to find new customers that perhaps need that enhanced level of functionality and support. And, and I'm very, very happy that Patrion was one of those companies. >>Okay. So let's talk about the Patrion. Okay. Obviously scaling is a big part of it. You're an SRE site, reliability engineers with folks who don't know what that is, is your, your job is essentially, you know, managing scale. Some say you the dev ops manager, but that's not really right answer. What is the SRE role at patriotics share with folks out there who are either having an SRE. They don't even know it yet or need to have SRS because this is a huge transition that, and new, new and emerging must have role in companies, >>Right? Yeah. We're the history of Patrion covers a lot. We cover a wide swath of a wide swath of, of, of things that we work with and, and areas that we consider to be our, our purview. Not only are we working on working with our AWS environment, but we also are involved in how can we make the site more reliable or performance so that, so that creators fans have a good experience. So we work with our content delivery numbers or caching strategies for caching caching assets. We work inside the application itself for doing performance performance, a hassle. This is also in proving observability with distributed tracing and metrics on a lot of that stuff, but also on the build and deploy side, if we can, if we can get that deploy time faster, like give engineers faster feedback on features that they're working on or bug fixes and also being secure and knowing that the, the code that they're working on it gets delivered reliably. >>Yeah. I think I, you have the continuous delivery is always the, the, the killer killer workflow as both the Spinnaker question here. Well, how has Spinnaker, well, what, how, how does Spinnaker being an open source project help you guys? I mean, obviously open source code is great. How has that been significant and beneficial for both armory and Patrion? >>Yeah, I'll take the first stab at this one. And it starts at the beginning. Spinnaker was created by Netflix and since Netflix open source that four or five years ago, there have been countless and significant contributions from many other companies, including armory, including AWS and those contributions collectively push the industry forward and allow the, the companies that, you know, that use open-source Spinnaker or armory, they can now benefit from all of the collective effort together. So just that community aspect working together is huge. Absolutely huge. And, you know, open source, I guess on the go-to-market side is a big driver for us. You know, there's many, many companies using open-source Spinnaker in production that are not our customers yet. And we, we survey them. We want to know how they're using open-source Spinnaker so that we can then improve open-source Spinnaker, but also build features that are critical for large companies to run at scale, deploy at scale, deploy with velocity and with reliability. >>Yeah. What's your take on, on the benefits of Spinnaker being open source? >>A lot of what Ben, it's been really beneficial to be able to like, be able to go in and look at the source code for components. I've been wondering something like, why is this thing working like this? Or how did they solve this? It's also been useful for, I can go ask the community for, for advice on things. If armory doesn't has the, it doesn't have the time or bandwidth to work on some things I've been able to ask the special interest groups in the source community. Like, can we, can we help improve this or something like that. And I've also been able to commit simple bug fixes for features that I've, that I've needed. I was like, well, I don't need to, I don't need to go engage are very on this. I can just like, I can just write up a simple patch on and have that out for review. >>You know, that's the beautiful thing about open sources. You get the source code and that's, and some people just think it's so easy, Ben, you know, just, Hey, just give me the open source. I'll code it. I got an unlimited resource team. Not, not always the case. So I gotta ask you guys on Patrion. Why use a company like armory, if you have the open source code and armory, why did you build a business on the open source project? Like Spinnaker? >>Yeah. Like I see. Absolutely. Yeah. Like I, like I said earlier, the atrium, the Patrion SRE team was wasn't is fairly small. There's two people. Now we're six. People are still people down. We're six people now. So being sure we could run a Spinnaker on our own if we, if we wanted to. And, but then we'd have no time to do anything else basically. And that's not the best use of our, of our creators money. Our fans, the fans being the creators artists. We have obviously take a percentage on top of that. And we, we need to spend our, that money well, and having armory who's dedicated to the Spinnaker is dedicated, involved the open source project. But also there are experts on this Sunday. It was something that would take me like a week of stumbling around trying to find documentation on how to set this thing up. They done this like 15, 20 times and they can just go, oh yeah, this is what we do for this. And let me go fix it for you >>At score. You know, you've got a teammate. I think that's where, what you're getting at. I got to ask you what other things is that free you up? Because this is the classic business model of life. You know, you have a partner you're moving fast, it slows you down to get into it. Sure. You can do it yourself, but why it's faster to go with it, go together with a partner and a wing man as we will. What things did does that free you up to work on as an SRE? >>Oh, that's freed me up to work on a bigger parts of our build and deploy pipeline. It's freed me up to work on moving from a usage based deploys onto a containerization strategy. It's freed me up to work on more broader observability issues instead of just being laser-focused on running an operating spending. >>Yeah. And that really kind of highlights. I'm glad you said that because it highlights what's going on. You had a lot of speed and velocity. You've got scale, you've got security and you've got new challenges you got to fix in and move fast. It's a whole new world. So again, this is why I love cloud native. Right? So you got open source, you got scale and you guys are applying directly to the, to the infrastructure of the business. So Ben, I got to ask you armory. Co-founder why did you guys build your business on an open source project? Like Spinnaker? What was the mindset? How did you attack this? What did you guys do? Take us through that piece because this is truly a great entrepreneurial story about open source. >>Yeah. Yeah. I'll give you the abridged version, which is that my co-founders and I, we solved the same problem, which is CD at a previous company, but we did it kind of the old fashioned way we home role. We handled it ourselves. We built it on top of Jenkins and it was great for that company, but, and that was kind of the inspiration for us to then ask questions. Hey, is this bigger? We, when at the time we found that Spinnaker had just been, you know, dog food inside of Netflix and they were open sourcing it. And we thought it was a great opportunity for us to partner. But the bigger reason is that Spinnaker is a platform that deploys to other platforms like AWS and Kubernetes and the sheer amount of surface area that's required to build a great product is enormous. And I actually believe that the only way to be successful in this space is to be open source, to have a community of large companies and passionate developers that contribute the roads if you will, to deploy into various targets. >>And so that's the reason, number one for it being open source and us wanting to build our business on top of open source. And then the second reason is because we focus almost exclusively on solving enterprise scale problems. We have a platform that needs to be extensible and open source is by definition extensible. So our customers, I mean, Ian just had a great example, right? Like he needed to fix something he was able to do so solve it in open source. And then, you know, shortly thereafter that that fix in mainline gets into the armory official build and then he can consume his fix. So we see a lot of that from our other customers. And then even, you know, take a very, very large company. They may have custom off that they need to integrate with, but that doesn't, that's not in open-source Spinnaker, but they can go and build that themselves. >>Yeah, it's real. It really is the new modern way to develop. And I, you know, last 80 with startup showcase last season, Emily Freeman gave a talk on, you know, you know, retiring, I call it killing the software, SDLC, the lifecycle of how software was developed in the past. And I got to ask you guys, and, and this cube conversation is that this is kind of like the, the kind of the big wave we're on now is cloud scale, open source, cloud, native data security, all being built in on this in the pipelines to your point is SRS enabling a new infrastructure and a new environment for people to build essentially SAS. So I got to ask you guys as, and you mentioned it Ben, the old way you hand rolled something, Netflix, open source, something, you got to look at Lyft with Envoy. I mean, large-scale comes, are donating their stuff into open source and people getting on top of it and building it. So the world's changed. So we've got to ask you, what's the difference between standing up a SAS application today versus say five to eight years ago, because we all see salesforce.com. You know, they're out there, they built their own data center. Cloud skills changed the dynamics of how software is being built. And with open-source accelerating every quarter, you're seeing more growth in software. How has building a platform for applications changed and how has that changed? How people build SAS applications, Ben, what's your take on this? It's kind of a thought exercise here. >>Yeah. I mean, I wouldn't even call it a thought exercise. We're seeing it firsthand from our customers. And then I'll, you know, I'll, I'll give my answer and you can weigh in on like practical, like what you're actually doing at Patrion with SAS, but the, the costs and the kind of entry fee, if you will, for building a SAS application has tremendously dropped. You don't need to buy servers and put them inside data centers anymore. You just spin up a VM or Kubernetes cluster with AWS. AWS has led the way in public cloud to make this incredible easy. And the tool sets being built around cloud native, like armory and like many other companies in the space are making it even easier. So we're just seeing the proliferation of, of software being developed and, and hopefully, you know, armory is playing a role in, in making it easier and better. >>So before we get to Unum for a second, I just want to just double down on it because there's great conversation that implies that there's going to be a new migration of apps everywhere, right. As tsunami of clutter good or bad, is that good or bad or is it all open source? Is it all good then? >>Absolutely good. For sure. There will be, you know, good stuff developed and not so good stuff developed, but survival of the fittest will hopefully promote those, the best apps with the highest value to the end user and, and society at large and push us all forward. So, >>And what's your take, obviously, Kubernetes, you seeing things like observability talking about how we're managing stateful and services that are being deployed and tear down in real time, automated, all new things are developing. How does building a true scalable SAS application change today versus say five, eight years ago? >>I mean, like you said, there's a, there's a lot, there's a lot of new, both open source. So SAS products available that you can use to build a scale stuff. Like if you're going to need that to build like secure authentication, instead of having to roll that out and you could go with something like Okta raw zero, you can just pull that off the shelf stuff for like managing push notifications before that was like something really hard to really hard to do. Then Firebase came on the scene and also for manic state and application and stuff like that. And also for like being, being able to deliver before >>You had Jenkins, maybe even for that, you didn't really have anything Jenkins came along. And then now you have open-source products like Spinnaker that you can use to deliver. And then you have companies built around that, that you can just go and say, Hey, can you please help us deliver this? Like you just help us, enable us to be able to build, build our products so that we can focus on delivering value to our creators and fans instead of having to focus on, on other things. >>So bill it builds faster. You can compose stuff faster. You don't have to roll your own code. You can just roll your own modules basically, and then exactly what prietary on top of it. Absolutely. Yeah. And that's why commercial open source is booming. Guys. Thank you so much, Ben, congratulations on armory and great to have you on from Patrion well-known success. So we'll accompany you congratulate. If we don't know patriarch, check it out, they have changed the game on creators and leading the industry. Ben. Great, great shot with armory and Spinnaker. Thanks for coming on. Thank you >>So much. Thank you >>So much. Okay. I'm Sean Ferrer here with the cube conversation with Palo Alto. Thanks for watching.

>>Welcome back to the cubes coverage of ADA reinvent 2021 here, the cube coverage. I'm Judd for a, your host we're on the ground with two sets on the floor, real event. Of course, it's hybrid. It's online as well. You can check it out there. All the on-demand replays are there. We're here with Sandy Carter, worldwide vice president, public sector partners and programs. And we've got Fred Swanick, her founder, and chief curator of the room. We're talking about getting the best talent programming and in the cloud, doing great things, innovation all happening, Sandy. Great to see you. Thanks for coming on the cube, but appreciate it. Thanks for halfway to see. Okay. So tell us about the room. What is the room what's going on? >>Um, well, I mentioned in the room is to help the world's most extraordinary do us to fulfill their potential. So, um, it's a community of exceptional talent that we are building throughout the world, um, and connecting this talent to each other and connecting them to the organizations that are looking for people who can really move the needle for those organizations. >>So what kind of results are you guys seeing right now? Give us some stats. >>Well, it's a, it's a relatively new concept. So we're about 5,000 members so far, um, from 77 different countries. Um, and this is, you know, we're talking about sort of the top two to 3% of talent in different fields. Um, and, um, as we go forward, you know, we're really looking, seeing this as an opportunity to curate, um, exceptional talent. Um, and it feels like software engineering, data science, UX, UI design, cloud computing, um, and, uh, it really helped to, um, identify diverse talent as well from pockets that have typically been untapped for technology. Okay. >>I want to ask you kind of, what's the, how you read the tea leaves. How do I spot the talent, but first talk about the relationship with Amazon. What's the program together? How you guys working together? It's a great mission. I mean, we need more people anyway, coding everywhere, globally. What's the AWS connection. >>So Fred and I met and, uh, he had this, I mean the brilliant concept of the room. And so, uh, obviously you need to run that on the cloud. And so he's got organizations he's working at connecting them through the room and kind of that piece that he was needing was the technology. So we stepped in to help him with the technology piece because he's got all the subject matter expertise to train 3 million Africans, um, coming up on tech, we also were able to provide him some of the classwork as well for the cloud computing models. So some of those certs and things that we want to get out into the marketplace as well, we're also helping Fred with that as well. So >>I mean, want to, just to add onto that, you know, one of the things that's unique about the room is that we're trying to really build a long-term relationship with talent. So imagine joining the room as a 20 year old and being part of it until you're 60. So you're going to have a lot of that. You collect on someone as they progress through different stages of their career and the ability for us to leverage that data, um, and continuously learn about someone's, you know, skills and values and use, um, predictive algorithms to be able to match them to the right opportunities at the right time of their lives. And this is where the machine learning comes in and the, you know, the data lake that we're building to build to really store this massive data that we're going to be building on the top talent to the world. >>You know, that's a really good point. It's a list that's like big trend in tech where it's, it's still it's over the life's life of the horizon of the person. And it's also blends community, exactly nurturing, identifying, and assisting. But at the same day, not just giving people the answer, they got to grow on their own, but some people grow differently. So again, progressions are nonlinear sometimes and creativity can come out of nowhere. Got it. Uh, which brings me up to my number one question, because this always was on my mind is how do you spot talent? What's the secret sauce? >>Well, there is no real secret source because every person is unique. So what we look for are people who have an extra dose of five things, courage, passion, resilience, imagination, and good values, right? And this is what we're looking for. And you will someone who is unusually driven to achieve great things. Um, so of course, you know, you look at it from a combination of their, their training, you know, what they, what they've learned, but also what they've actually done in the workplace and feedback that you get from previous employers and data that we collect through our own interactions with this person. Um, and so we screened them through, you know, with the town that we had, didn't fly, we take them through really rigorous selection process. So, um, it takes, uh, for example, people go through an online assessments and then they go through an in-person interview and then we'll take them through a one to three month bootcamp to really identify, you know, people who are exceptional and of course get data from different sources about the person as well. >>Sandy, how do you see this collaboration helping, uh, your other clients? I mean, obviously talent, cross pollinates, um, learnings, what's your, you see this level of >>It has, uh, you know, AWS grows, obviously we're going to need more talent, especially in Africa because we're growing so rapidly there and there's going to be so much talent available in Africa here in just a few short years. Most of the tech talent will be in Africa. I think that that's really essential, but also as looking after my partners, I had Fred today on the keynote explaining to all my partners around the world, 55,000 streaming folks, how they can also leverage the room to fill some of their roles as well. Because if you think about it, you know, we heard from Presidio there's 3 million open cyber security roles. Um, you know, we're training 20 of mine million cloud folks because we have a gap. We see a gap around the world. And part of my responsibility with partners is making sure that they can get access to the right skills. And we're counting on the room and what Fred has produced to produce some of those great skills. You have AI, AML and dev ops. Tell us some of the areas you haven't. >>You know, we're looking at, uh, business intelligence, data science, um, full-stack software engineering, cybersecurity, um, you know, IOT talent. So fields that, um, the world needs a lot more talented. And I think today, a lot of technology, um, talent is moving from one place to another and what we need is new supply. And so what the room is doing is not only a community of top 10, but we're actually producing and training a lot more new talent. And that was going to hopefully, uh, remove a key bottleneck that a lot of companies are facing today as they try to undergo the digital trends. >>Well, maybe you can add some hosts on there. We need some cube hosts, come on, always looking for more talent on the set. You could be there. >>Yeah. The other interesting thing, John, Fred and I on stage today, he was talking about how easy to the first narrative written for easy to was written by a gentleman out of South Africa. So think about that right. ECE to talent. And he was talking about Ian Musk is based, you know, south African, right? So think about all the great talent that exists. There. There you go. There you go. So how do you get access to that talent? And that's why we're so excited to partner with Fred. Not only is he wicked impressive when a time's most influential people, but his mission, his life purpose has really been to develop this great talent. And for us, that gets us really excited because we, yeah, >>I think there's plenty of opportunities to around new business models in the U S for instance, um, my friends started upstart, which they were betting on people almost like a stock market. You know, almost like currency will fund you and you pay us back. And there's all kinds of gamification techniques that you can start to weave into the system. Exactly. As you get the flywheel going, exactly, you can look at it holistically and say, Hey, how do we get more people in and harvest the value of knowledge? >>That's exactly. I mean, one of the elements of the technology platform that we developed to the Amazon with AWS is the room intelligence platform. And in there is something called legacy points. So every time you, as a member of the room, give someone else an opportunity. You invest in their venture, you hire them, you mentor them, you get points and you can leverage those points for some really cool experiences, right? So you want to game-ify um, this community that is, uh, you know, essentially crowdsourcing opportunities. And you're not only getting things from the room, but you're also giving to others to enable everyone to grow. >>Yeah, what's the coolest thing you've seen. And this is a great initiative. First of all, it's a great model. I think it's, this is the future. Cause I'm a big believer that communities groups, as we get into this hybrid world is going to open up the virtualization. What the virtual world has shown us is virtualization, which is a cloud technology when Amazon started with Zen, which is virtualization technology, but virtualization, conceptually is replicating things. So if you think hybrid world, you can blend the connect people together. So now you have this social construct, this connective tissue between relationships, and it's always evolving, you know, this and you've been involved in community from, from, from the early days when you have that social evolution, it's not software as a mechanism. It's a human thing. Exactly. It's organism, it evolves. And so if you can get the software to think like that and the group to drive the behavior, it's not community software. >>Exactly. I mean, we say that the room is not an online community. It's really an offline community powered by technology. So our vision is to actually have physical rooms in different cities around the world, whether it's talent gathers, but imagine showing up at a, at a room space and we've got the technology to know what your interests are. We know that you're working on a new venture and there's this, there's a venture capitalists in that area, investing that venture, we can connect you right then that space powered by the, >>And then you can have watch parties. For instance, there's an event going on in us. You can do some watch parties and time shifted and then re replicated online and create a localization, but yet have that connection in >>Present. Exactly, exactly. Exactly. So what are the >>Learnings, what's your big learning share with the audience? What you've learned, because this is really kind of on the front edge of the new kind of innovation we're seeing, being enabled with software. >>I mean, one thing we're learning is that, uh, talent is truly, uh, evenly distribute around the world, but what is not as opportunity. And so, um, there's some truly exceptional talent that is hidden and on tap today. And if we can, you know, and, and today with the COVID pandemic companies or around the world, a lot more open to hiring more talent. So there's a huge opportunity to access new talent from, from sources that haven't been tapped before. Well, but also learnings the power of blending, the online and offline world. So, um, you know, the room is, as I mentioned, brings people together, normally in line, but also offline. And so when you're able to meet talent and actually see someone's personality and get a sense of the culture fit the 360 degree for your foot, some of that, you can't just get on a LinkedIn. Yes. That I built it to make a decision, to hire someone who is much better. And finally, we're also learning about the importance of long-term relationships. One of my motives in the room is relationships not transactions where, um, you actually get to meet someone in an environment where they're not pretending in an interview and you get to really see who they are and build relationships with them before you need to hide them. And these are some really unique ways that we think we can redefine how talent finds opportunity in the 21st. So >>You can put a cube in every room, we pick >>You up because, >>And the cube, what we do here is that when people collaborate, whether they're doing an interview together, riffing and sharing content is creating knowledge, but that shared experience creates a bonding. So when you have that kind of mindset and this room concept where it's not just resume, get a job, see you later, it's learning, having peers and colleagues and people around you, and then seeing them in a journey, multiple laps around the track of humans >>And going through a career, not just a job. >>Yes, exactly. And then, and then celebrating the ups and downs in learning. It's not always roses, as you know, it's always pain before you accelerate. >>Exactly. And you never quite arrive at your destination. You're always growing, and this is where technology can really play. >>Okay. So super exciting. Where's this go next, Sandy. And next couple of minutes left in. >>So, um, one of the things that we've envisioned, so this is not done yet, but, um, Fred and I imagined like, what if you could have an Alexa set up and you could say, Hey, you know, Alexa, what should be my next job? Or how should I go train? Or I'm really interested in being on a Ted talk. What could I do having an Alexa skill might be a really cool thing to do. And with the great funding that Fred Scott and you should talk about the $400 million to that, he's already raised $400 million. I mean, there, I think the sky's the limit on platforms. Like >>That's a nice chunk of change. There it is. We've got some fat financing as they say, >>But, well, it's a big mission. So to request significant resources, >>Who's backing you guys. What's the, who's the, where's the money coming from? >>It's coming from, um, the MasterCard foundation. They, our biggest funder, um, as well as, um, some philanthropists, um, and essentially these are people who truly see the potential, uh, to unlock, um, opportunity for millions of people global >>For Glen, a global scale. The vision has global >>Executive starting in Africa, but truly global. Our vision is eventually to have a community of about 10 to 20 million of the most extraordinary doers in the world, in this community, and to connect them to opportunity >>Angela and diverse John. I mean, this is the other thing that gets me excited because innovation comes from diversity of thought and given the community, we'll have so many diverse individuals in it that are going to get trained and mentored to create something that is amazing for their career as well. That really gets me excited too, as well as Amazon website, >>Smart people, and yet identifying the fresh voices and the fresh minds that come with it, all that that comes together, >>The social capital that they need to really accelerate their impact. >>Then you read the room and then you get wherever you need. Thanks so much. Congratulations on your great mission. Love the room. Um, you need to be the in Cuban, every room, you gotta get those fresh voices out there. See any graduates on a great project, super exciting. And SageMaker, AI's all part of, it's all kind of, it's a cool wave. It's fun. Can I join? Can I play? I tell you I need a room. >>I think he's top talent. >>Thanks so much for coming. I really appreciate your insight. Great stuff here, bringing you all the action and knowledge and insight here at re-invent with the cube two sets on the floor. It's a hybrid event. We're in person in Las Vegas for a real event. I'm John ferry with the cube, the leader in global tech coverage. Thanks for watching.

>>Well, welcome back to the cubes coverage of AWS reinvent 2021. We're here joined by Ian buck, general manager and vice president of accelerated computing at Nvidia I'm. John Ford, your host of the QB. And thanks for coming on. So in video, obviously, great brand congratulates on all your continued success. Everyone who has does anything in graphics knows the GPU's are hot and you guys get great brand great success in the company, but AI and machine learning was seeing the trend significantly being powered by the GPU's and other systems. So it's a key part of everything. So what's the trends that you're seeing, uh, in ML and AI, that's accelerating computing to the cloud. Yeah, >>I mean, AI is kind of drape bragging breakthroughs innovations across so many segments, so many different use cases. We see it showing up with things like credit card, fraud prevention and product and content recommendations. Really it's the new engine behind search engines is AI. Uh, people are applying AI to things like, um, meeting transcriptions, uh, virtual calls like this using AI to actually capture what was said. Um, and that gets applied in person to person interactions. We also see it in intelligence systems assistance for a contact center, automation or chat bots, uh, medical imaging, um, and intelligence stores and warehouses and everywhere. It's really, it's really amazing what AI has been demonstrated, what it can do. And, uh, it's new use cases are showing up all the time. >>Yeah. I'd love to get your thoughts on, on how the world's evolved just in the past few years, along with cloud, and certainly the pandemics proven it. You had this whole kind of full stack mindset initially, and now you're seeing more of a horizontal scale, but yet enabling this vertical specialization in applications. I mean, you mentioned some of those apps, the new enablers, this kind of the horizontal play with enablement for specialization, with data, this is a huge shift that's going on. It's been happening. What's your reaction to that? >>Yeah, it's the innovations on two fronts. There's a horizontal front, which is basically the different kinds of neural networks or AIS as well as machine learning techniques that are, um, just being invented by researchers for, uh, and the community at large, including Amazon. Um, you know, it started with these convolutional neural networks, which are great for image processing, but as it expanded more recently into, uh, recurrent neural networks, transformer models, which are great for language and language and understanding, and then the new hot topic graph neural networks, where the actual graph now is trained as a, as a neural network, you have this underpinning of great AI technologies that are being adventure around the world in videos role is try to productize that and provide a platform for people to do that innovation and then take the next step and innovate vertically. Um, take it, take it and apply it to two particular field, um, like medical, like healthcare and medical imaging applying AI, so that radiologists can have an AI assistant with them and highlight different parts of the scan. >>Then maybe troublesome worrying, or requires more investigation, um, using it for robotics, building virtual worlds, where robots can be trained in a virtual environment, their AI being constantly trained, reinforced, and learn how to do certain activities and techniques. So that the first time it's ever downloaded into a real robot, it works right out of the box, um, to do, to activate that we co we are creating different vertical solutions, vertical stacks for products that talk the languages of those businesses, of those users, uh, in medical imaging, it's processing medical data, which is obviously a very complicated large format data, often three-dimensional boxes in robotics. It's building combining both our graphics and simulation technologies, along with the, you know, the AI training capabilities and different capabilities in order to run in real time. Those are, >>Yeah. I mean, it's just so cutting edge. It's so relevant. I mean, I think one of the things you mentioned about the neural networks, specifically, the graph neural networks, I mean, we saw, I mean, just to go back to the late two thousands, you know, how unstructured data or object store created, a lot of people realize that the value out of that now you've got graph graph value, you got graph network effect, you've got all kinds of new patterns. You guys have this notion of graph neural networks. Um, that's, that's, that's out there. What is, what is a graph neural network and what does it actually mean for deep learning and an AI perspective? >>Yeah, we have a graph is exactly what it sounds like. You have points that are connected to each other, that established relationships and the example of amazon.com. You might have buyers, distributors, sellers, um, and all of them are buying or recommending or selling different products. And they're represented in a graph if I buy something from you and from you, I'm connected to those end points and likewise more deeply across a supply chain or warehouse or other buyers and sellers across the network. What's new right now is that those connections now can be treated and trained like a neural network, understanding the relationship. How strong is that connection between that buyer and seller or that distributor and supplier, and then build up a network that figure out and understand patterns across them. For example, what products I may like. Cause I have this connection in my graph, what other products may meet those requirements, or also identifying things like fraud when, when patterns and buying patterns don't match, what a graph neural networks should say would be the typical kind of graph connectivity, the different kind of weights and connections between the two captured by the frequency half I buy things or how I rate them or give them stars as she used cases, uh, this application graph neural networks, which is basically capturing the connections of all things with all people, especially in the world of e-commerce, it's very exciting to a new application, but applying AI to optimizing business, to reducing fraud and letting us, you know, get access to the products that we want, the products that they have, our recommendations be things that, that excited us and want us to buy things >>Great setup for the real conversation that's going on here at re-invent, which is new kinds of workloads are changing. The game. People are refactoring their business with not just replatform, but actually using this to identify value and see cloud scale allows you to have the compute power to, you know, look at a note on an arc and actually code that. It's all, it's all science, all computer science, all at scale. So with that, that brings up the whole AWS relationship. Can you tell us how you're working with AWS before? >>Yeah. 80 of us has been a great partner and one of the first cloud providers to ever provide GPS the cloud, uh, we most more recently we've announced two new instances, uh, the instance, which is based on the RA 10 G GPU, which has it was supports the Nvidia RTX technology or rendering technology, uh, for real-time Ray tracing and graphics and game streaming is their highest performance graphics, enhanced replicate without allows for those high performance graphics applications to be directly hosted in the cloud. And of course runs everything else as well, including our AI has access to our AI technology runs all of our AI stacks. We also announced with AWS, the G 5g instance, this is exciting because it's the first, uh, graviton or ARM-based processor connected to a GPU and successful in the cloud. Um, this makes, uh, the focus here is Android gaming and machine learning and France. And we're excited to see the advancements that Amazon is making and AWS is making with arm and the cloud. And we're glad to be part of that journey. >>Well, congratulations. I remember I was just watching my interview with James Hamilton from AWS 2013 and 2014. He was getting, he was teasing this out, that they're going to build their own, get in there and build their own connections, take that latency down and do other things. This is kind of the harvest of all that. As you start looking at these new new interfaces and the new servers, new technology that you guys are doing, you're enabling applications. What does, what do you see this enabling as this, as this new capability comes out, new speed, more, more performance, but also now it's enabling more capabilities so that new workloads can be realized. What would you say to folks who want to ask that question? >>Well, so first off I think arm is here to stay and you can see the growth and explosion of my arm, uh, led of course, by grab a tiny to be. I spend many others, uh, and by bringing all of NVIDIA's rendering graphics, machine learning and AI technologies to arm, we can help bring that innovation. That arm allows that open innovation because there's an open architecture to the entire ecosystem. Uh, we can help bring it forward, uh, to the state of the art in AI machine learning, the graphics. Um, we all have our software that we released is both supportive, both on x86 and an army equally, um, and including all of our AI stacks. So most notably for inference the deployment of AI models. We have our, the Nvidia Triton inference server. Uh, this is the, our inference serving software where after he was trained to model, he wanted to play it at scale on any CPU or GPU instance, um, for that matter. So we support both CPS and GPS with Triton. Um, it's natively integrated with SageMaker and provides the benefit of all those performance optimizations all the time. Uh, things like, uh, features like dynamic batching. It supports all the different AI frameworks from PI torch to TensorFlow, even a generalized Python code. Um, we're activating how activating the arm ecosystem as well as bringing all those AI new AI use cases and all those different performance levels, uh, with our partnership with AWS and all the different clouds. >>And you got to making it really easy for people to use, use the technology that brings up the next kind of question I want to ask you. I mean, a lot of people are really going in jumping in the big time into this. They're adopting AI. Either they're moving in from prototype to production. There's always some gaps, whether it's knowledge, skills, gaps, or whatever, but people are accelerating into the AI and leaning into it hard. What advancements have is Nvidia made to make it more accessible, um, for people to move faster through the, through the system, through the process? >>Yeah, it's one of the biggest challenges. The other promise of AI, all the publications that are coming all the way research now, how can you make it more accessible or easier to use by more people rather than just being an AI researcher, which is, uh, uh, obviously a very challenging and interesting field, but not one that's directly in the business. Nvidia is trying to write a full stack approach to AI. So as we make, uh, discover or see these AI technologies come available, we produce SDKs to help activate them or connect them with developers around the world. Uh, we have over 150 different STKs at this point, certain industries from gaming to design, to life sciences, to earth scientist. We even have stuff to help simulate quantum computing. Um, and of course all the, all the work we're doing with AI, 5g and robotics. So, uh, we actually just introduced about 65 new updates just this past month on all those SDKs. Uh, some of the newer stuff that's really exciting is the large language models. Uh, people are building some amazing AI. That's capable of understanding the Corpus of like human understanding, these language models that are trained on literally the continent of the internet to provide general purpose or open domain chatbots. So the customer is going to have a new kind of experience with a computer or the cloud. Uh, we're offering large language, uh, those large language models, as well as AI frameworks to help companies take advantage of this new kind of technology. >>You know, each and every time I do an interview with Nvidia or talk about Nvidia my kids and their friends, they first thing they said, you get me a good graphics card. Hey, I want the best thing in their rig. Obviously the gaming market's hot and known for that, but I mean, but there's a huge software team behind Nvidia. This is a well-known your CEO is always talking about on his keynotes, you're in the software business. And then you had, do have hardware. You were integrating with graviton and other things. So, but it's a software practices, software. This is all about software. Could you share kind of more about how Nvidia culture and their cloud culture and specifically around the scale? I mean, you, you hit every, every use case. So what's the software culture there at Nvidia, >>And it is actually a bigger, we have more software people than hardware people, people don't often realize this. Uh, and in fact that it's because of we create, uh, the, the, it just starts with the chip, obviously building great Silicon is necessary to provide that level of innovation, but as it expanded dramatically from then, from there, uh, not just the Silicon and the GPU, but the server designs themselves, we actually do entire server designs ourselves to help build out this infrastructure. We consume it and use it ourselves and build our own supercomputers to use AI, to improve our products. And then all that software that we build on top, we make it available. As I mentioned before, uh, as containers on our, uh, NGC container store container registry, which is accessible for me to bus, um, to connect to those vertical markets, instead of just opening up the hardware and none of the ecosystem in develop on it, they can with a low-level and programmatic stacks that we provide with Kuda. We believe that those vertical stacks are the ways we can help accelerate and advance AI. And that's why we make as well, >>Ram a little software is so much easier. I want to get that plug for, I think it's worth noting that you guys are, are heavy hardcore, especially on the AI side. And it's worth calling out, uh, getting back to the customers who are bridging that gap and getting out there, what are the metrics they should consider as they're deploying AI? What are success metrics? What does success look like? Can you share any insight into what they should be thinking about and looking at how they're doing? >>Yeah. Um, for training, it's all about time to solution. Um, it's not the hardware that that's the cost, it's the opportunity that AI can provide your business and many, and the productivity of those data scientists, which are developing, which are not easy to come by. So, uh, what we hear from customers is they need a fast time to solution to allow people to prototype very quickly, to train a model to convergence, to get into production quickly, and of course, move on to the next or continue to refine it often. So in training is time to solution for inference. It's about our, your ability to deploy at scale. Often people need to have real time requirements. They want to run in a certain amount of latency, a certain amount of time. And typically most companies don't have a single AI model. They have a collection of them. They want, they want to run for a single service or across multiple services. That's where you can aggregate some of your infrastructure leveraging the trading infant server. I mentioned before can actually run multiple models on a single GPU saving costs, optimizing for efficiency yet still meeting the requirements for latency and the real time experience so that your customers have a good, a good interaction with the AI. >>Awesome. Great. Let's get into, uh, the customer examples. You guys have obviously great customers. Can you share some of the use cases, examples with customers, notable customers? >>Yeah. I want one great part about working in videos as a technology company. You see, you get to engage with such amazing customers across many verticals. Uh, some of the ones that are pretty exciting right now, Netflix is using the G4 instances to CLA um, to do a video effects and animation content. And, you know, from anywhere in the world, in the cloud, uh, as a cloud creation content platform, uh, we work in the energy field that Siemens energy is actually using AI combined with, um, uh, simulation to do predictive maintenance on their energy plants, um, and, and, uh, doing preventing or optimizing onsite inspection activities and eliminating downtime, which is saving a lot of money for the engine industry. Uh, we have worked with Oxford university, uh, which is Oxford university actually has over two, over 20 million artifacts and specimens and collections across its gardens and museums and libraries. They're actually using convenient GPS and Amazon to do enhance image recognition, to classify all these things, which would take literally years with, um, uh, going through manually each of these artifacts using AI, we can click and quickly catalog all of them and connect them with their users. Um, great stories across graphics, about cross industries across research that, uh, it's just so exciting to see what people are doing with our technology together with, >>And thank you so much for coming on the cube. I really appreciate Greg, a lot of great content there. We probably going to go another hour, all the great stuff going on in the video, any closing remarks you want to share as we wrap this last minute up >>Now, the, um, really what Nvidia is about as accelerating cloud computing, whether it be AI, machine learning, graphics, or headphones, community simulation, and AWS was one of the first with this in the beginning, and they continue to bring out great instances to help connect, uh, the cloud and accelerated computing with all the different opportunities integrations with with SageMaker really Ks and ECS. Uh, the new instances with G five and G 5g, very excited to see all the work that we're doing together. >>Ian buck, general manager, and vice president of accelerated computing. I mean, how can you not love that title? We want more, more power, more faster, come on. More computing. No, one's going to complain with more computing know, thanks for coming on. Thank you. Appreciate it. I'm John Farrell hosted the cube. You're watching Amazon coverage reinvent 2021. Thanks for watching.

(bright music) >> Well, welcome back to theCUBE's coverage of AWS re:Invent 2021. We're here joined by Ian Buck, general manager and vice president of Accelerated Computing at NVIDIA. I'm John Furrrier, host of theCUBE. Ian, thanks for coming on. >> Oh, thanks for having me. >> So NVIDIA, obviously, great brand. Congratulations on all your continued success. Everyone who does anything in graphics knows that GPU's are hot, and you guys have a great brand, great success in the company. But AI and machine learning, we're seeing the trend significantly being powered by the GPU's and other systems. So it's a key part of everything. So what's the trends that you're seeing in ML and AI that's accelerating computing to the cloud? >> Yeah. I mean, AI is kind of driving breakthroughs and innovations across so many segments, so many different use cases. We see it showing up with things like credit card fraud prevention, and product and content recommendations. Really, it's the new engine behind search engines, is AI. People are applying AI to things like meeting transcriptions, virtual calls like this, using AI to actually capture what was said. And that gets applied in person-to-person interactions. We also see it in intelligence assistance for contact center automation, or chat bots, medical imaging, and intelligence stores, and warehouses, and everywhere. It's really amazing what AI has been demonstrating, what it can do, and its new use cases are showing up all the time. >> You know, Ian, I'd love to get your thoughts on how the world's evolved, just in the past few years alone, with cloud. And certainly, the pandemic's proven it. You had this whole kind of fullstack mindset, initially, and now you're seeing more of a horizontal scale, but yet, enabling this vertical specialization in applications. I mean, you mentioned some of those apps. The new enablers, this kind of, the horizontal play with enablement for, you know, specialization with data, this is a huge shift that's going on. It's been happening. What's your reaction to that? >> Yeah. The innovation's on two fronts. There's a horizontal front, which is basically the different kinds of neural networks or AIs, as well as machine learning techniques, that are just being invented by researchers and the community at large, including Amazon. You know, it started with these convolutional neural networks, which are great for image processing, but has expanded more recently into recurrent neural networks, transformer models, which are great for language and language and understanding, and then the new hot topic, graph neural networks, where the actual graph now is trained as a neural network. You have this underpinning of great AI technologies that are being invented around the world. NVIDIA's role is to try to productize that and provide a platform for people to do that innovation. And then, take the next step and innovate vertically. Take it and apply it to a particular field, like medical, like healthcare and medical imaging, applying AI so that radiologists can have an AI assistant with them and highlight different parts of the scan that may be troublesome or worrying, or require some more investigation. Using it for robotics, building virtual worlds where robots can be trained in a virtual environment, their AI being constantly trained and reinforced, and learn how to do certain activities and techniques. So that the first time it's ever downloaded into a real robot, it works right out of the box. To activate that, we are creating different vertical solutions, vertical stacks, vertical products, that talk the languages of those businesses, of those users. In medical imaging, it's processing medical data, which is obviously a very complicated, large format data, often three-dimensional voxels. In robotics, it's building, combining both our graphics and simulation technologies, along with the AI training capabilities and difference capabilities, in order to run in real time. Those are just two simple- >> Yeah, no. I mean, it's just so cutting-edge, it's so relevant. I mean, I think one of the things you mentioned about the neural networks, specifically, the graph neural networks, I mean, we saw, I mean, just go back to the late 2000s, how unstructured data, or object storage created, a lot of people realized a lot of value out of that. Now you got graph value, you got network effect, you got all kinds of new patterns. You guys have this notion of graph neural networks that's out there. What is a graph neural network, and what does it actually mean from a deep learning and an AI perspective? >> Yeah. I mean, a graph is exactly what it sounds like. You have points that are connected to each other, that establish relationships. In the example of Amazon.com, you might have buyers, distributors, sellers, and all of them are buying, or recommending, or selling different products. And they're represented in a graph. If I buy something from you and from you, I'm connected to those endpoints, and likewise, more deeply across a supply chain, or warehouse, or other buyers and sellers across the network. What's new right now is, that those connections now can be treated and trained like a neural network, understanding the relationship, how strong is that connection between that buyer and seller, or the distributor and supplier, and then build up a network to figure out and understand patterns across them. For example, what products I may like, 'cause I have this connection in my graph, what other products may meet those requirements? Or, also, identifying things like fraud, When patterns and buying patterns don't match what a graph neural networks should say would be the typical kind of graph connectivity, the different kind of weights and connections between the two, captured by the frequency of how often I buy things, or how I rate them or give them stars, or other such use cases. This application, graph neural networks, which is basically capturing the connections of all things with all people, especially in the world of e-commerce, is very exciting to a new application of applying AI to optimizing business, to reducing fraud, and letting us, you know, get access to the products that we want. They have our recommendations be things that excite us and want us to buy things, and buy more. >> That's a great setup for the real conversation that's going on here at re:Invent, which is new kinds of workloads are changing the game, people are refactoring their business with, not just re-platforming, but actually using this to identify value. And also, your cloud scale allows you to have the compute power to, you know, look at a note in an arc and actually code that. It's all science, it's all computer science, all at scale. So with that, that brings up the whole AWS relationship. Can you tell us how you're working with AWS, specifically? >> Yeah, AWS have been a great partner, and one of the first cloud providers to ever provide GPUs to the cloud. More recently, we've announced two new instances, the G5 instance, which is based on our A10G GPU, which supports the NVIDIA RTX technology, our rendering technology, for real-time ray tracing in graphics and game streaming. This is our highest performance graphics enhanced application, allows for those high-performance graphics applications to be directly hosted in the cloud. And, of course, runs everything else as well. It has access to our AI technology and runs all of our AI stacks. We also announced, with AWS, the G5 G instance. This is exciting because it's the first Graviton or Arm-based processor connected to a GPU and successful in the cloud. The focus here is Android gaming and machine learning inference. And we're excited to see the advancements that Amazon is making and AWS is making, with Arm in the cloud. And we're glad to be part of that journey. >> Well, congratulations. I remember, I was just watching my interview with James Hamilton from AWS 2013 and 2014. He was teasing this out, that they're going to build their own, get in there, and build their own connections to take that latency down and do other things. This is kind of the harvest of all that. As you start looking at these new interfaces, and the new servers, new technology that you guys are doing, you're enabling applications. What do you see this enabling? As this new capability comes out, new speed, more performance, but also, now it's enabling more capabilities so that new workloads can be realized. What would you say to folks who want to ask that question? >> Well, so first off, I think Arm is here to stay. We can see the growth and explosion of Arm, led of course, by Graviton and AWS, but many others. And by bringing all of NVIDIA's rendering graphics, machine learning and AI technologies to Arm, we can help bring that innovation that Arm allows, that open innovation, because there's an open architecture, to the entire ecosystem. We can help bring it forward to the state of the art in AI machine learning and graphics. All of our software that we release is both supportive, both on x86 and on Arm equally, and including all of our AI stacks. So most notably, for inference, the deployment of AI models, we have the NVIDIA Triton inference server. This is our inference serving software, where after you've trained a model, you want to deploy it at scale on any CPU, or GPU instance, for that matter. So we support both CPUs and GPUs with Triton. It's natively integrated with SageMaker and provides the benefit of all those performance optimizations. Features like dynamic batching, it supports all the different AI frameworks, from PyTorch to TensorFlow, even a generalized Python code. We're activating, and help activating, the Arm ecosystem, as well as bringing all those new AI use cases, and all those different performance levels with our partnership with AWS and all the different cloud instances. >> And you guys are making it really easy for people to use use the technology. That brings up the next, kind of, question I wanted to ask you. I mean, a lot of people are really going in, jumping in big-time into this. They're adopting AI, either they're moving it from prototype to production. There's always some gaps, whether it's, you know, knowledge, skills gaps, or whatever. But people are accelerating into the AI and leaning into it hard. What advancements has NVIDIA made to make it more accessible for people to move faster through the system, through the process? >> Yeah. It's one of the biggest challenges. You know, the promise of AI, all the publications that are coming out, all the great research, you know, how can you make it more accessible or easier to use by more people? Rather than just being an AI researcher, which is obviously a very challenging and interesting field, but not one that's directly connected to the business. NVIDIA is trying to provide a fullstack approach to AI. So as we discover or see these AI technologies become available, we produce SDKs to help activate them or connect them with developers around the world. We have over 150 different SDKs at this point, serving industries from gaming, to design, to life sciences, to earth sciences. We even have stuff to help simulate quantum computing. And of course, all the work we're doing with AI, 5G, and robotics. So we actually just introduced about 65 new updates, just this past month, on all those SDKs. Some of the newer stuff that's really exciting is the large language models. People are building some amazing AI that's capable of understanding the corpus of, like, human understanding. These language models that are trained on literally the content of the internet to provide general purpose or open-domain chatbots, so the customer is going to have a new kind of experience with the computer or the cloud. We're offering those large language models, as well as AI frameworks, to help companies take advantage of this new kind of technology. >> You know, Ian, every time I do an interview with NVIDIA or talk about NVIDIA, my kids and friends, first thing they say is, "Can you get me a good graphics card?" They all want the best thing in their rig. Obviously the gaming market's hot and known for that. But there's a huge software team behind NVIDIA. This is well-known. Your CEO is always talking about it on his keynotes. You're in the software business. And you do have hardware, you are integrating with Graviton and other things. But it's a software practice. This is software. This is all about software. >> Right. >> Can you share, kind of, more about how NVIDIA culture and their cloud culture, and specifically around the scale, I mean, you hit every use case. So what's the software culture there at NVIDIA? >> Yeah, NVIDIA's actually a bigger, we have more software people than hardware people. But people don't often realize this. And in fact, that it's because of, it just starts with the chip, and obviously, building great silicon is necessary to provide that level of innovation. But it's expanded dramatically from there. Not just the silicon and the GPU, but the server designs themselves. We actually do entire server designs ourselves, to help build out this infrastructure. We consume it and use it ourselves, and build our own supercomputers to use AI to improve our products. And then, all that software that we build on top, we make it available, as I mentioned before, as containers on our NGC container store, container registry, which is accessible from AWS, to connect to those vertical markets. Instead of just opening up the hardware and letting the ecosystem develop on it, they can, with the low-level and programmatic stacks that we provide with CUDA. We believe that those vertical stacks are the ways we can help accelerate and advance AI. And that's why we make them so available. >> And programmable software is so much easier. I want to get that plug in for, I think it's worth noting that you guys are heavy hardcore, especially on the AI side, and it's worth calling out. Getting back to the customers who are bridging that gap and getting out there, what are the metrics they should consider as they're deploying AI? What are success metrics? What does success look like? Can you share any insight into what they should be thinking about, and looking at how they're doing? >> Yeah. For training, it's all about time-to-solution. It's not the hardware that's the cost, it's the opportunity that AI can provide to your business, and the productivity of those data scientists which are developing them, which are not easy to come by. So what we hear from customers is they need a fast time-to-solution to allow people to prototype very quickly, to train a model to convergence, to get into production quickly, and of course, move on to the next or continue to refine it. >> John Furrier: Often. >> So in training, it's time-to-solution. For inference, it's about your ability to deploy at scale. Often people need to have real-time requirements. They want to run in a certain amount of latency, in a certain amount of time. And typically, most companies don't have a single AI model. They have a collection of them they want to run for a single service or across multiple services. That's where you can aggregate some of your infrastructure. Leveraging the Triton inference server, I mentioned before, can actually run multiple models on a single GPU saving costs, optimizing for efficiency, yet still meeting the requirements for latency and the real-time experience, so that our customers have a good interaction with the AI. >> Awesome. Great. Let's get into the customer examples. You guys have, obviously, great customers. Can you share some of the use cases examples with customers, notable customers? >> Yeah. One great part about working at NVIDIA is, as technology company, you get to engage with such amazing customers across many verticals. Some of the ones that are pretty exciting right now, Netflix is using the G4 instances to do a video effects and animation content from anywhere in the world, in the cloud, as a cloud creation content platform. We work in the energy field. Siemens energy is actually using AI combined with simulation to do predictive maintenance on their energy plants, preventing, or optimizing, onsite inspection activities and eliminating downtime, which is saving a lot of money for the energy industry. We have worked with Oxford University. Oxford University actually has over 20 million artifacts and specimens and collections, across its gardens and museums and libraries. They're actually using NVIDIA GPU's and Amazon to do enhanced image recognition to classify all these things, which would take literally years going through manually, each of these artifacts. Using AI, we can quickly catalog all of them and connect them with their users. Great stories across graphics, across industries, across research, that it's just so exciting to see what people are doing with our technology, together with Amazon. >> Ian, thank you so much for coming on theCUBE. I really appreciate it. A lot of great content there. We probably could go another hour. All the great stuff going on at NVIDIA. Any closing remarks you want to share, as we wrap this last minute up? >> You know, really what NVIDIA's about, is accelerating cloud computing. Whether it be AI, machine learning, graphics, or high-performance computing and simulation. And AWS was one of the first with this, in the beginning, and they continue to bring out great instances to help connect the cloud and accelerated computing with all the different opportunities. The integrations with EC2, with SageMaker, with EKS, and ECS. The new instances with G5 and G5 G. Very excited to see all the work that we're doing together. >> Ian Buck, general manager and vice president of Accelerated Computing. I mean, how can you not love that title? We want more power, more faster, come on. More computing. No one's going to complain with more computing. Ian, thanks for coming on. >> Thank you. >> Appreciate it. I'm John Furrier, host of theCUBE. You're watching Amazon coverage re:Invent 2021. Thanks for watching. (bright music)

>>As we've been reporting, the pandemic has called CSOs to really shift their spending priorities towards securing remote workers. Almost overnight. Zero trust has gone from buzzword to mandate. What's more as we wrote in our recent cybersecurity breaking analysis, not only Maseca pro secured increasingly distributed workforce, but now they have to be wary of software updates in the digital supply chain, including the very patches designed to protect them against cyber attacks. Hello everyone. And welcome to this Q conversation. My name is Dave Vellante and I'm pleased to welcome Derek manky. Who's chief security insights, and global threat alliances for four guard labs with fresh data from its global threat landscape report. Derek. Welcome. Great to see you. >>Thanks so much for, for the invitation to speak. It's always a pleasure. Multicover yeah, >>You're welcome. So first I wonder if you could explain for the audience, what is for guard labs and what's its relationship to fortunate? >>Right. So 40 grand labs is, is our global sockets, our global threat intelligence operation center. It never sleeps, and this is the beat. Um, you know, it's, it's been here since inception at port in it. So it's it's 20, 21 years in the making, since Fortinet was founded, uh, we have built this in-house, uh, so we don't go yum technology. We built everything from the ground up, including creating our own training programs for our, our analysts. We're following malware, following exploits. We even have a unique program that I created back in 2006 to ethical hacking program. And it's a zero-day research. So we try to meet the hackers, the bad guys to their game. And we of course do that responsibly to work with vendors, to close schools and create virtual patches. Um, and, but, you know, so it's, it's everything from, uh, customer protection first and foremost, to following, uh, the threat landscape and cyber. It's very important to understand who they are, what they're doing, who they're, uh, what they're targeting, what tools are they using? >>Yeah, that's great. Some serious DNA and skills in that group. And it's, it's critical because like you said, you can, you can minimize the spread of those malware very, very quickly. So what, what now you have, uh, the global threat landscape report. We're going to talk about that, but what exactly is that? >>Right? So this a global threat landscape report, it's a summary of, uh, all, all the data that we collect over a period of time. So we released this, that biannually two times a year. Um, cyber crime is changing very fast, as you can imagine. So, uh, while we do release security blogs, and, uh, what we call threat signals for breaking security events, we have a lot of other vehicles to release threat intelligence, but this threat landscape report is truly global. It looks at all of our global data. So we have over 5 million censorship worldwide in 40 guard labs, we're processing. I know it seems like a very large amount, but North of a hundred billion, uh, threat events in just one day. And we have to take the task of taking all of that data and put that onto scale for half a year and compile that into something, um, that is, uh, the, you know, that that's digestible. That's a, a very tough task, as you can imagine, so that, you know, we have to work with a huge technologies back to machine learning and artificial intelligence automation. And of course our analyst view to do that. >>Yeah. So this year, of course, there's like the every year is a battle, but this year was an extra battle. Can you explain what you saw in terms of the hacker dynamics over the past? Let's say 12 months. I know you do this twice a year, but what trends did you see evolving throughout the year and what have you seen with the way that attackers have exploited this expanded attack surface outside of corporate network? >>Yeah, it was quite interesting last year. It certainly was not normal. Like we all say, um, and that was no exception for cybersecurity. You know, if we look at cyber criminals and how they pivoted and adapted to the scrap threat landscape, cyber cyber criminals are always trying to take advantage of the weakest link of the chain. They're trying to always prey off here and ride waves of global trends and themes. We've seen this before in, uh, natural disasters as an example, you know, um, trying to do charity kind of scams and campaigns. And they're usually limited to a region where that incident happened and they usually live about two to three weeks, maybe a month at the most. And then they'll move on to the next to the next trip. That's braking, of course, because COVID is so global and dominant. Um, we saw attacks coming in from, uh, well over 40 different languages as an example, um, in regions all across the world that wasn't lasting two to three weeks and it lasted for the better part of a year. >>And of course, what they're, they're using this as a vehicle, right? Not preying on the fear. They're doing everything from initial lockdown, uh, fishing. We were as COVID-19 movers to, um, uh, lay off notices then to phase one, reopenings all the way up to fast forward to where we are today with vaccine rollover development. So there's always that new flavor and theme that they were rolling out, but because it was so successful for them, they were able to, they didn't have to innovate too much, right. They didn't have to expand and shifted to new to new trends. And themes are really developed on new rats families as an example, or a new sophisticated malware. That was the first half of the year and the second half of the year. Um, of course people started to experience COVID fatigue, right? Um, people started to become, we did a lot of education around this. >>People started to become more aware of this threat. And so, um, cyber criminals have started to, um, as we expected, started to become more sophisticated with their attacks. We saw an expansion in different ransomware families. We saw more of a shift of focus on, on, um, uh, you know, targeting the digital supply chain as an example. And so that, that was, that was really towards Q4. Uh, so it, it was a long lived lead year with success on the Google themes, um, targeting healthcare as an example, a lot of, um, a lot of the organizations that were, you know, really in a vulnerable position, I would say >>So, okay. I want to clarify something because my assumption was that they actually did really increase the sophistication, but it sounds like that was kind of a first half trends. Not only did they have to adapt and not have to, but they adapt it to these new vulnerabilities. Uh, my sense was that when you talk about the digital supply chain, that that was a fairly sophisticated attack. Am I, am I getting that right? That they did their sort of their, their, their increased sophistication in the first half, and then they sort of deployed it, did it, uh, w what actually happened there from your data? >>Well, if we look at, so generally there's two types of attacks that we look at, we look at the, uh, the premeditated sophisticated attacks that can have, um, you know, a lot of ramp up work on their end, a lot of time developing the, the, the, the weaponization phase. So developing, uh, the exploits of the sophisticated malware that they're gonna use for the campaign reconnaissance, understanding the targets, where platforms are developed, um, the blueprinting that DNA of, of, of the supply chain, those take time. Um, in fact years, even if we look back to, um, uh, 10 plus years ago with the Stuxnet attacks, as an example that was on, uh, nuclear centrifuges, um, and that, that had four different zero-day weapons at the time. That was very sophisticated, that took over two years to develop as an example. So some of these can take years of time to develop, but they're, they're, uh, very specific in terms of the targets are going to go after obviously the ROI from their end. >>Uh, the other type of attack that we see is as ongoing, um, these broad, wide sweeping attacks, and the reality for those ones is they don't unfortunately need to be too sophisticated. And those ones were the ones I was talking about that were really just playing on the cool, the deem, and they still do today with the vaccine road and development. Uh, but, but it's really because they're just playing on, on, um, you know, social engineering, um, using, uh, topical themes. And in fact, the weapons they're using these vulnerabilities are from our research data. And this was highlighted actually the first pop landscape before last year, uh, on average were two to three years old. So we're not talking about fresh vulnerabilities. You've got to patch right away. I mean, these are things that should have been patched two years ago, but they're still unfortunately having success with that. >>So you mentioned stuck next Stuxnet as the former sort of example, of one of the types of attacks that you see. And I always felt like that was a watershed moment. One of the most sophisticated, if not the most sophisticated attack that we'd ever seen. When I talk to CSOs about the recent government hack, they, they, they suggest I infer maybe they don't suggest it. I infer that it was of similar sophistication. It was maybe thousands of people working on this for years and years and years. Is that, is that accurate or not necessarily? >>Yeah, there's definitely a, there's definitely some comparisons there. Uh, you know, one of the largest things is, uh, both attacks used digital circuits certificate personation, so they're digitally signed. So, you know, of course that whole technology using cryptography is designed by design, uh, to say that, you know, this piece of software installed in your system, hassles certificate is coming from the source. It's legitimate. Of course, if that's compromised, that's all out of the window. And, um, yeah, this is what we saw in both attacks. In fact, you know, stocks in that they also had digitally designed, uh, certificates that were compromised. So when it gets to that level of students or, uh, sophistication, that means definitely that there's a target that there has been usually months of, of, uh, homework done by cyber criminals, for reconnaissance to be able to weaponize that. >>W w what did you see with respect to ransomware? What were the trends there over the past 12 months? I've heard some data and it's pretty scary, but what did you see? >>Yeah, so we're actually, ransomware is always the thorn in our side, and it's going to continue to be so, um, you know, in fact, uh, ransomware is not a new itself. It was actually first created in 1989, and they demanded ransom payments through snail mail. This was to appeal a box, obviously that, that, that didn't take off. Wasn't a successful on the internet was porn at the time. But if you look at it now, of course, over the last 10 years, really, that's where it ran. The ransomware model has been, uh, you know, lucrative, right? I mean, it's been, um, using, uh, by force encrypting data on systems, so that users had to, if they were forced to pay the ransom because they wanted access to their data back data was the target currency for ransomware. That's shifted now. And that's actually been a big pivotal over the last year or so, because again, before it was this let's cast a wide net, in fact, as many people as we can random, um, and try to see if we can hold some of their data for ransom. >>Some people that data may be valuable, it may not be valuable. Um, and that model still exists. Uh, and we see that, but really the big shift that we saw last year and the threat landscape before it was a shift to targeted rats. So again, the sophistication is starting to rise because they're not just going out to random data. They're going out to data that they know is valuable to large organizations, and they're taking that a step further now. So there's various ransomware families. We saw that have now reverted to extortion and blackmail, right? So they're taking that data, encrypting it and saying, unless you pay us as large sum of money, we're going to release this to the public or sell it to a buyer on the dark web. And of course you can imagine the amount of, um, you know, damages that can happen from that. The other thing we're seeing is, is a target of going to revenue services, right? So if they can cripple networks, it's essentially a denial of service. They know that the company is going to be bleeding, you know, X, millions of dollars a day, so they can demand Y million dollars of ransom payments, and that's effectively what's happening. So it's, again, becoming more targeted, uh, and more sophisticated. And unfortunately the ransom is going up. >>So they go to where the money is. And of course your job is to, it's a lower the ROI for them, a constant challenge. Um, we talked about some of the attack vectors, uh, that you saw this year that, that cyber criminals are targeting. I wonder if, if, you know, given the work from home, if things like IOT devices and cameras and, you know, thermostats, uh, with 75% of the work force at home, is this infrastructure more vulnerable? I guess, of course it is. But what did you see there in terms of attacks on those devices? >>Yeah, so, uh, um, uh, you know, unfortunately the attack surface as we call it, uh, so the amount of target points is expanding. It's not shifting, it's expanding. We still see, um, I saw, I mentioned earlier vulnerabilities from two years ago that are being used in some cases, you know, over the holidays where e-commerce means we saw e-commerce heavily under attack in e-commerce has spikes since last summer, right. It's been a huge amount of traffic increase everybody's shopping from home. And, uh, those vulnerabilities going after a shopping cart, plugins, as an example, are five to six years old. So we still have this theme of old vulnerabilities are still new in a sense being attacked, but we're also now seeing this complication of, yeah, as you said, IOT, uh, B roll out everywhere, the really quick shift to work from home. Uh, we really have to treat this as if you guys, as the, uh, distributed branch model for enterprise, right. >>And it's really now the secure branch. How do we take, um, um, you know, any of these devices on, on those networks and secure them, uh, because yeah, if you look at the, what we highlighted in our landscape report and the top 10 attacks that we're seeing, so hacking attacks hacking in tabs, this is who our IPS triggers. You know, we're seeing attempts to go after IOT devices. Uh, right now they're mostly, uh, favoring, uh, well in terms of targets, um, consumer grade routers. Uh, but they're also looking at, um, uh, DVR devices as an example for, uh, you know, home entertainment systems, uh, network attached storage as well, and IP security cameras, um, some of the newer devices, uh, what, the quote unquote smart devices that are now on, you know, virtual assistance and home networks. Uh, we actually released a predictions piece at the end of last year as well. So this is what we call the new intelligent edge. And that's what I think is we're really going to see this year in terms of what's ahead. Um, cause we always have to look ahead and prepare for that. But yeah, right now, unfortunately, the story is, all of this is still happening. IOT is being targeted. Of course they're being targeted because they're easy targets. Um, it's like for cybercriminals, it's like shooting fish in a barrel. There's not just one, but there's multiple vulnerabilities, security holes associated with these devices, easy entry points into networks. >>I mean, it's, um, I mean, attackers they're, they're highly capable. They're organized, they're well-funded they move fast, they're they're agile, uh, and they follow the money. As we were saying, uh, you, you mentioned, you know, co vaccines and, you know, big pharma healthcare, uh, where >>Did you see advanced, persistent >>Threat groups really targeting? Were there any patterns that emerged in terms of other industry types or organizations being targeted? >>Yeah. So just to be clear again, when we talk about AP teams, um, uh, advanced, specific correct group, the groups themselves they're targeting, these are usually the more sophisticated groups, of course. So going back to that theme, these are usually the target, the, um, the premeditated targeted attacks usually points to nation state. Um, sometimes of course there's overlap. They can be affiliated with cyber crime, cyber crime, uh, uh, groups are typically, um, looking at some other targets for ROI, uh, bio there's there's a blend, right? So as an example, if we're looking at the, uh, apt groups I had last year, absolutely. Number one I would say would be healthcare. Healthcare was one of those, and it's, it's, it's, uh, you know, very unfortunate, but obviously with the shift that was happening at a pop up medical facilities, there's a big, a rush to change networks, uh, for a good cause of course, but with that game, um, you know, uh, security holes and concerns the targets and, and that's what we saw IPT groups targeting was going after those and, and ransomware and the cyber crime shrine followed as well. Right? Because if you can follow, uh, those critical networks and crippled them on from cybercriminals point of view, you can, you can expect them to pay the ransom because they think that they need to buy in order to, um, get those systems back online. Uh, in fact, last year or two, unfortunately we saw the first, um, uh, death that was caused because of a denial of service attack in healthcare, right. Facilities were weren't available because of the cyber attack. Patients had to be diverted and didn't make it on the way. >>All right. Jericho, sufficiently bummed out. So maybe in the time remaining, we can talk about remediation strategies. You know, we know there's no silver bullet in security. Uh, but what approaches are you recommending for organizations? How are you consulting with folks? >>Sure. Yeah. So a couple of things, um, good news is there's a lot that we can do about this, right? And, um, and, and basic measures go a long way. So a couple of things just to get out of the way I call it housekeeping, cyber hygiene, but it's always worth reminding. So when we talk about keeping security patches up to date, we always have to talk about that because that is reality as et cetera, these, these vulnerabilities that are still being successful are five to six years old in some cases, the majority two years old. Um, so being able to do that, manage that from an organization's point of view, really treat the new work from home. I don't like to call it a work from home. So the reality is it's work from anywhere a lot of the times for some people. So really treat that as, as the, um, as a secure branch, uh, methodology, doing things like segmentations on network, secure wifi access, multi-factor authentication is a huge muscle, right? >>So using multi-factor authentication because passwords are dead, um, using things like, uh, XDR. So Xers is a combination of detection and response for end points. This is a mass centralized management thing, right? So, uh, endpoint detection and response, as an example, those are all, uh, you know, good security things. So of course having security inspection, that that's what we do. So good threat intelligence baked into your security solution. That's supported by labs angles. So, uh, that's, uh, you know, uh, antivirus, intrusion prevention, web filtering, sandbox, and so forth, but then it gets that that's the security stack beyond that it gets into the end user, right? Everybody has a responsibility. This is that supply chain. We talked about. The supply chain is, is, is a target for attackers attackers have their own supply chain as well. And we're also part of that supply chain, right? The end users where we're constantly fished for social engineering. So using phishing campaigns against employees to better do training and awareness is always recommended to, um, so that's what we can do, obviously that's, what's recommended to secure, uh, via the endpoints in the secure branch there's things we're also doing in the industry, um, to fight back against that with prime as well. >>Well, I, I want to actually talk about that and talk about ecosystems and collaboration, because while you have competitors, you all want the same thing. You, SecOps teams are like superheroes in my book. I mean, they're trying to save the world from the bad guys. And I remember I was talking to Robert Gates on the cube a couple of years ago, a former defense secretary. And I said, yeah, but don't, we have like the best security people and can't we go on the offensive and weaponize that ourselves. Of course, there's examples of that. Us. Government's pretty good at it, even though they won't admit it. But his answer to me was, yeah, we gotta be careful because we have a lot more to lose than many countries. So I thought that was pretty interesting, but how do you collaborate with whether it's the U S government or other governments or other other competitors even, or your ecosystem? Maybe you could talk about that a little bit. >>Yeah. Th th this is what, this is what makes me tick. I love working with industry. I've actually built programs for 15 years of collaboration in the industry. Um, so, you know, we, we need, I always say we can't win this war alone. You actually hit on this point earlier, you talked about following and trying to disrupt the ROI of cybercriminals. Absolutely. That is our target, right. We're always looking at how we can disrupt their business model. Uh, and, and in order, there's obviously a lot of different ways to do that, right? So a couple of things we do is resiliency. That's what we just talked about increasing the security stack so that they go knocking on someone else's door. But beyond that, uh, it comes down to private, private sector collaborations. So, uh, we, we, uh, co-founder of the cyber threat Alliance in 2014 as an example, this was our fierce competitors coming in to work with us to share intelligence, because like you said, um, competitors in the space, but we need to work together to do the better fight. >>And so this is a Venn diagram. What's compared notes, let's team up, uh, when there's a breaking attack and make sure that we have the intelligence so that we can still remain competitive on the technology stack to gradation the solutions themselves. Uh, but let's, let's level the playing field here because cybercriminals moved out, uh, you know, um, uh, that, that there's no borders and they move with great agility. So, uh, that's one thing we do in the private private sector. Uh, there's also, uh, public private sector relationships, right? So we're working with Interpol as an example, Interfor project gateway, and that's when we find attribution. So it's not just the, what are these people doing like infrastructure, but who, who are they, where are they operating? What, what events tools are they creating? We've actually worked on cases that are led down to, um, uh, warrants and arrests, you know, and in some cases, one case with a $60 million business email compromise fraud scam, the great news is if you look at the industry as a whole, uh, over the last three to four months has been for take downs, a motet net Walker, uh, um, there's also IE Gregor, uh, recently as well too. >>And, and Ian Gregor they're actually going in and arresting the affiliates. So not just the CEO or the King, kind of these organizations, but the people who are distributing the ransomware themselves. And that was a unprecedented step, really important. So you really start to paint a picture of this, again, supply chain, this ecosystem of cyber criminals and how we can hit them, where it hurts on all angles. I've most recently, um, I've been heavily involved with the world economic forum. Uh, so I'm, co-author of a report from last year of the partnership on cyber crime. And, uh, this is really not just the pro uh, private, private sector, but the private and public sector working together. We know a lot about cybercriminals. We can't arrest them. Uh, we can't take servers offline from the data centers, but working together, we can have that whole, you know, that holistic effect. >>Great. Thank you for that, Derek. What if people want, want to go deeper? Uh, I know you guys mentioned that you do blogs, but are there other resources that, that they can tap? Yeah, absolutely. So, >>Uh, everything you can see is on our threat research blog on, uh, so 40 net blog, it's under expired research. We also put out, uh, playbooks, w we're doing blah, this is more for the, um, the heroes as he called them the security operation centers. Uh, we're doing playbooks on the aggressors. And so this is a playbook on the offense, on the offense. What are they up to? How are they doing that? That's on 40 guard.com. Uh, we also release, uh, threat signals there. So, um, we typically release, uh, about 50 of those a year, and those are all, um, our, our insights and views into specific attacks that are now >>Well, Derek Mackie, thanks so much for joining us today. And thanks for the work that you and your teams do. Very important. >>Thanks. It's yeah, it's a pleasure. And, uh, rest assured we will still be there 24 seven, three 65. >>Good to know. Good to know. And thank you for watching everybody. This is Dave Volante for the cube. We'll see you next time.

>>As we've been reporting, the pandemic has called CSOs to really shift their spending priorities towards securing remote workers. Almost overnight. Zero trust has gone from buzzword to mandate. What's more as we wrote in our recent cybersecurity breaking analysis, not only Maseca pro secured increasingly distributed workforce, but now they have to be wary of software updates in the digital supply chain, including the very patches designed to protect them against cyber attacks. Hello everyone. And welcome to this Q conversation. My name is Dave Vellante and I'm pleased to welcome Derek manky. Who's chief security insights, and global threat alliances for four guard labs with fresh data from its global threat landscape report. Derek. Welcome. Great to see you. >>Thanks so much for, for the invitation to speak. It's always a pleasure. Multicover yeah, >>You're welcome. So first I wonder if you could explain for the audience, what is for guard labs and what's its relationship to fortunate? >>Right. So 40 grand labs is, is our global sockets, our global threat intelligence operation center. It never sleeps, and this is the beat. Um, you know, it's, it's been here since inception at port in it. So it's it's 20, 21 years in the making, since Fortinet was founded, uh, we have built this in-house, uh, so we don't go yum technology. We built everything from the ground up, including creating our own training programs for our, our analysts. We're following malware, following exploits. We even have a unique program that I created back in 2006 to ethical hacking program. And it's a zero-day research. So we try to meet the hackers, the bad guys to their game. And we of course do that responsibly to work with vendors, to close schools and create virtual patches. Um, and, but, you know, so it's, it's everything from, uh, customer protection first and foremost, to following, uh, the threat landscape and cyber. It's very important to understand who they are, what they're doing, who they're, uh, what they're targeting, what tools are they using? >>Yeah, that's great. Some serious DNA and skills in that group. And it's, it's critical because like you said, you can, you can minimize the spread of those malware very, very quickly. So what, what now you have, uh, the global threat landscape report. We're going to talk about that, but what exactly is that? >>Right? So this a global threat landscape report, it's a summary of, uh, all, all the data that we collect over a period of time. So we released this, that biannually two times a year. Um, cyber crime is changing very fast, as you can imagine. So, uh, while we do release security blogs, and, uh, what we call threat signals for breaking security events, we have a lot of other vehicles to release threat intelligence, but this threat landscape report is truly global. It looks at all of our global data. So we have over 5 million censorship worldwide in 40 guard labs, we're processing. I know it seems like a very large amount, but North of a hundred billion, uh, threat events in just one day. And we have to take the task of taking all of that data and put that onto scale for half a year and compile that into something, um, that is, uh, the, you know, that that's digestible. That's a, a very tough task, as you can imagine, so that, you know, we have to work with a huge technologies back to machine learning and artificial intelligence automation. And of course our analyst view to do that. >>Yeah. So this year, of course, there's like the every year is a battle, but this year was an extra battle. Can you explain what you saw in terms of the hacker dynamics over the past? Let's say 12 months. I know you do this twice a year, but what trends did you see evolving throughout the year and what have you seen with the way that attackers have exploited this expanded attack surface outside of corporate network? >>Yeah, it was quite interesting last year. It certainly was not normal. Like we all say, um, and that was no exception for cybersecurity. You know, if we look at cyber criminals and how they pivoted and adapted to the scrap threat landscape, cyber cyber criminals are always trying to take advantage of the weakest link of the chain. They're trying to always prey off here and ride waves of global trends and themes. We've seen this before in, uh, natural disasters as an example, you know, um, trying to do charity kind of scams and campaigns. And they're usually limited to a region where that incident happened and they usually live about two to three weeks, maybe a month at the most. And then they'll move on to the next to the next trip. That's braking, of course, because COVID is so global and dominant. Um, we saw attacks coming in from, uh, well over 40 different languages as an example, um, in regions all across the world that wasn't lasting two to three weeks and it lasted for the better part of a year. >>And of course, what they're, they're using this as a vehicle, right? Not preying on the fear. They're doing everything from initial lockdown, uh, fishing. We were as COVID-19 movers to, um, uh, lay off notices then to phase one, reopenings all the way up to fast forward to where we are today with vaccine rollover development. So there's always that new flavor and theme that they were rolling out, but because it was so successful for them, they were able to, they didn't have to innovate too much, right. They didn't have to expand and shifted to new to new trends. And themes are really developed on new rats families as an example, or a new sophisticated malware. That was the first half of the year and the second half of the year. Um, of course people started to experience COVID fatigue, right? Um, people started to become, we did a lot of education around this. >>People started to become more aware of this threat. And so, um, cyber criminals have started to, um, as we expected, started to become more sophisticated with their attacks. We saw an expansion in different ransomware families. We saw more of a shift of focus on, on, um, uh, you know, targeting the digital supply chain as an example. And so that, that was, that was really towards Q4. Uh, so it, it was a long lived lead year with success on the Google themes, um, targeting healthcare as an example, a lot of, um, a lot of the organizations that were, you know, really in a vulnerable position, I would say >>So, okay. I want to clarify something because my assumption was that they actually did really increase the sophistication, but it sounds like that was kind of a first half trends. Not only did they have to adapt and not have to, but they adapt it to these new vulnerabilities. Uh, my sense was that when you talk about the digital supply chain, that that was a fairly sophisticated attack. Am I, am I getting that right? That they did their sort of their, their, their increased sophistication in the first half, and then they sort of deployed it, did it, uh, w what actually happened there from your data? >>Well, if we look at, so generally there's two types of attacks that we look at, we look at the, uh, the premeditated sophisticated attacks that can have, um, you know, a lot of ramp up work on their end, a lot of time developing the, the, the, the weaponization phase. So developing, uh, the exploits of the sophisticated malware that they're gonna use for the campaign reconnaissance, understanding the targets, where platforms are developed, um, the blueprinting that DNA of, of, of the supply chain, those take time. Um, in fact years, even if we look back to, um, uh, 10 plus years ago with the Stuxnet attacks, as an example that was on, uh, nuclear centrifuges, um, and that, that had four different zero-day weapons at the time. That was very sophisticated, that took over two years to develop as an example. So some of these can take years of time to develop, but they're, they're, uh, very specific in terms of the targets are going to go after obviously the ROI from their end. >>Uh, the other type of attack that we see is as ongoing, um, these broad, wide sweeping attacks, and the reality for those ones is they don't unfortunately need to be too sophisticated. And those ones were the ones I was talking about that were really just playing on the cool, the deem, and they still do today with the vaccine road and development. Uh, but, but it's really because they're just playing on, on, um, you know, social engineering, um, using, uh, topical themes. And in fact, the weapons they're using these vulnerabilities are from our research data. And this was highlighted actually the first pop landscape before last year, uh, on average were two to three years old. So we're not talking about fresh vulnerabilities. You've got to patch right away. I mean, these are things that should have been patched two years ago, but they're still unfortunately having success with that. >>So you mentioned stuck next Stuxnet as the former sort of example, of one of the types of attacks that you see. And I always felt like that was a watershed moment. One of the most sophisticated, if not the most sophisticated attack that we'd ever seen. When I talk to CSOs about the recent government hack, they, they, they suggest I infer maybe they don't suggest it. I infer that it was of similar sophistication. It was maybe thousands of people working on this for years and years and years. Is that, is that accurate or not necessarily? >>Yeah, there's definitely a, there's definitely some comparisons there. Uh, you know, one of the largest things is, uh, both attacks used digital circuits certificate personation, so they're digitally signed. So, you know, of course that whole technology using cryptography is designed by design, uh, to say that, you know, this piece of software installed in your system, hassles certificate is coming from the source. It's legitimate. Of course, if that's compromised, that's all out of the window. And, um, yeah, this is what we saw in both attacks. In fact, you know, stocks in that they also had digitally designed, uh, certificates that were compromised. So when it gets to that level of students or, uh, sophistication, that means definitely that there's a target that there has been usually months of, of, uh, homework done by cyber criminals, for reconnaissance to be able to weaponize that. >>W w what did you see with respect to ransomware? What were the trends there over the past 12 months? I've heard some data and it's pretty scary, but what did you see? >>Yeah, so we're actually, ransomware is always the thorn in our side, and it's going to continue to be so, um, you know, in fact, uh, ransomware is not a new itself. It was actually first created in 1989, and they demanded ransom payments through snail mail. This was to appeal a box, obviously that, that, that didn't take off. Wasn't a successful on the internet was porn at the time. But if you look at it now, of course, over the last 10 years, really, that's where it ran. The ransomware model has been, uh, you know, lucrative, right? I mean, it's been, um, using, uh, by force encrypting data on systems, so that users had to, if they were forced to pay the ransom because they wanted access to their data back data was the target currency for ransomware. That's shifted now. And that's actually been a big pivotal over the last year or so, because again, before it was this let's cast a wide net, in fact, as many people as we can random, um, and try to see if we can hold some of their data for ransom. >>Some people that data may be valuable, it may not be valuable. Um, and that model still exists. Uh, and we see that, but really the big shift that we saw last year and the threat landscape before it was a shift to targeted rats. So again, the sophistication is starting to rise because they're not just going out to random data. They're going out to data that they know is valuable to large organizations, and they're taking that a step further now. So there's various ransomware families. We saw that have now reverted to extortion and blackmail, right? So they're taking that data, encrypting it and saying, unless you pay us as large sum of money, we're going to release this to the public or sell it to a buyer on the dark web. And of course you can imagine the amount of, um, you know, damages that can happen from that. The other thing we're seeing is, is a target of going to revenue services, right? So if they can cripple networks, it's essentially a denial of service. They know that the company is going to be bleeding, you know, X, millions of dollars a day, so they can demand Y million dollars of ransom payments, and that's effectively what's happening. So it's, again, becoming more targeted, uh, and more sophisticated. And unfortunately the ransom is going up. >>So they go to where the money is. And of course your job is to, it's a lower the ROI for them, a constant challenge. Um, we talked about some of the attack vectors, uh, that you saw this year that, that cyber criminals are targeting. I wonder if, if, you know, given the work from home, if things like IOT devices and cameras and, you know, thermostats, uh, with 75% of the work force at home, is this infrastructure more vulnerable? I guess, of course it is. But what did you see there in terms of attacks on those devices? >>Yeah, so, uh, um, uh, you know, unfortunately the attack surface as we call it, uh, so the amount of target points is expanding. It's not shifting, it's expanding. We still see, um, I saw, I mentioned earlier vulnerabilities from two years ago that are being used in some cases, you know, over the holidays where e-commerce means we saw e-commerce heavily under attack in e-commerce has spikes since last summer, right. It's been a huge amount of traffic increase everybody's shopping from home. And, uh, those vulnerabilities going after a shopping cart, plugins, as an example, are five to six years old. So we still have this theme of old vulnerabilities are still new in a sense being attacked, but we're also now seeing this complication of, yeah, as you said, IOT, uh, B roll out everywhere, the really quick shift to work from home. Uh, we really have to treat this as if you guys, as the, uh, distributed branch model for enterprise, right. >>And it's really now the secure branch. How do we take, um, um, you know, any of these devices on, on those networks and secure them, uh, because yeah, if you look at the, what we highlighted in our landscape report and the top 10 attacks that we're seeing, so hacking attacks hacking in tabs, this is who our IPS triggers. You know, we're seeing attempts to go after IOT devices. Uh, right now they're mostly, uh, favoring, uh, well in terms of targets, um, consumer grade routers. Uh, but they're also looking at, um, uh, DVR devices as an example for, uh, you know, home entertainment systems, uh, network attached storage as well, and IP security cameras, um, some of the newer devices, uh, what, the quote unquote smart devices that are now on, you know, virtual assistance and home networks. Uh, we actually released a predictions piece at the end of last year as well. So this is what we call the new intelligent edge. And that's what I think is we're really going to see this year in terms of what's ahead. Um, cause we always have to look ahead and prepare for that. But yeah, right now, unfortunately, the story is, all of this is still happening. IOT is being targeted. Of course they're being targeted because they're easy targets. Um, it's like for cybercriminals, it's like shooting fish in a barrel. There's not just one, but there's multiple vulnerabilities, security holes associated with these devices, easy entry points into networks. >>I mean, it's, um, I mean, attackers they're, they're highly capable. They're organized, they're well-funded they move fast, they're they're agile, uh, and they follow the money. As we were saying, uh, you, you mentioned, you know, co vaccines and, you know, big pharma healthcare, uh, where >>Did you see advanced, persistent >>Threat groups really targeting? Were there any patterns that emerged in terms of other industry types or organizations being targeted? >>Yeah. So just to be clear again, when we talk about AP teams, um, uh, advanced, specific correct group, the groups themselves they're targeting, these are usually the more sophisticated groups, of course. So going back to that theme, these are usually the target, the, um, the premeditated targeted attacks usually points to nation state. Um, sometimes of course there's overlap. They can be affiliated with cyber crime, cyber crime, uh, uh, groups are typically, um, looking at some other targets for ROI, uh, bio there's there's a blend, right? So as an example, if we're looking at the, uh, apt groups I had last year, absolutely. Number one I would say would be healthcare. Healthcare was one of those, and it's, it's, it's, uh, you know, very unfortunate, but obviously with the shift that was happening at a pop up medical facilities, there's a big, a rush to change networks, uh, for a good cause of course, but with that game, um, you know, uh, security holes and concerns the targets and, and that's what we saw IPT groups targeting was going after those and, and ransomware and the cyber crime shrine followed as well. Right? Because if you can follow, uh, those critical networks and crippled them on from cybercriminals point of view, you can, you can expect them to pay the ransom because they think that they need to buy in order to, um, get those systems back online. Uh, in fact, last year or two, unfortunately we saw the first, um, uh, death that was caused because of a denial of service attack in healthcare, right. Facilities were weren't available because of the cyber attack. Patients had to be diverted and didn't make it on the way. >>All right. Jericho, sufficiently bummed out. So maybe in the time remaining, we can talk about remediation strategies. You know, we know there's no silver bullet in security. Uh, but what approaches are you recommending for organizations? How are you consulting with folks? >>Sure. Yeah. So a couple of things, um, good news is there's a lot that we can do about this, right? And, um, and, and basic measures go a long way. So a couple of things just to get out of the way I call it housekeeping, cyber hygiene, but it's always worth reminding. So when we talk about keeping security patches up to date, we always have to talk about that because that is reality as et cetera, these, these vulnerabilities that are still being successful are five to six years old in some cases, the majority two years old. Um, so being able to do that, manage that from an organization's point of view, really treat the new work from home. I don't like to call it a work from home. So the reality is it's work from anywhere a lot of the times for some people. So really treat that as, as the, um, as a secure branch, uh, methodology, doing things like segmentations on network, secure wifi access, multi-factor authentication is a huge muscle, right? >>So using multi-factor authentication because passwords are dead, um, using things like, uh, XDR. So Xers is a combination of detection and response for end points. This is a mass centralized management thing, right? So, uh, endpoint detection and response, as an example, those are all, uh, you know, good security things. So of course having security inspection, that that's what we do. So good threat intelligence baked into your security solution. That's supported by labs angles. So, uh, that's, uh, you know, uh, antivirus, intrusion prevention, web filtering, sandbox, and so forth, but then it gets that that's the security stack beyond that it gets into the end user, right? Everybody has a responsibility. This is that supply chain. We talked about. The supply chain is, is, is a target for attackers attackers have their own supply chain as well. And we're also part of that supply chain, right? The end users where we're constantly fished for social engineering. So using phishing campaigns against employees to better do training and awareness is always recommended to, um, so that's what we can do, obviously that's, what's recommended to secure, uh, via the endpoints in the secure branch there's things we're also doing in the industry, um, to fight back against that with prime as well. >>Well, I, I want to actually talk about that and talk about ecosystems and collaboration, because while you have competitors, you all want the same thing. You, SecOps teams are like superheroes in my book. I mean, they're trying to save the world from the bad guys. And I remember I was talking to Robert Gates on the cube a couple of years ago, a former defense secretary. And I said, yeah, but don't, we have like the best security people and can't we go on the offensive and weaponize that ourselves. Of course, there's examples of that. Us. Government's pretty good at it, even though they won't admit it. But his answer to me was, yeah, we gotta be careful because we have a lot more to lose than many countries. So I thought that was pretty interesting, but how do you collaborate with whether it's the U S government or other governments or other other competitors even, or your ecosystem? Maybe you could talk about that a little bit. >>Yeah. Th th this is what, this is what makes me tick. I love working with industry. I've actually built programs for 15 years of collaboration in the industry. Um, so, you know, we, we need, I always say we can't win this war alone. You actually hit on this point earlier, you talked about following and trying to disrupt the ROI of cybercriminals. Absolutely. That is our target, right. We're always looking at how we can disrupt their business model. Uh, and, and in order, there's obviously a lot of different ways to do that, right? So a couple of things we do is resiliency. That's what we just talked about increasing the security stack so that they go knocking on someone else's door. But beyond that, uh, it comes down to private, private sector collaborations. So, uh, we, we, uh, co-founder of the cyber threat Alliance in 2014 as an example, this was our fierce competitors coming in to work with us to share intelligence, because like you said, um, competitors in the space, but we need to work together to do the better fight. >>And so this is a Venn diagram. What's compared notes, let's team up, uh, when there's a breaking attack and make sure that we have the intelligence so that we can still remain competitive on the technology stack to gradation the solutions themselves. Uh, but let's, let's level the playing field here because cybercriminals moved out, uh, you know, um, uh, that, that there's no borders and they move with great agility. So, uh, that's one thing we do in the private private sector. Uh, there's also, uh, public private sector relationships, right? So we're working with Interpol as an example, Interfor project gateway, and that's when we find attribution. So it's not just the, what are these people doing like infrastructure, but who, who are they, where are they operating? What, what events tools are they creating? We've actually worked on cases that are led down to, um, uh, warrants and arrests, you know, and in some cases, one case with a $60 million business email compromise fraud scam, the great news is if you look at the industry as a whole, uh, over the last three to four months has been for take downs, a motet net Walker, uh, um, there's also IE Gregor, uh, recently as well too. >>And, and Ian Gregor they're actually going in and arresting the affiliates. So not just the CEO or the King, kind of these organizations, but the people who are distributing the ransomware themselves. And that was a unprecedented step, really important. So you really start to paint a picture of this, again, supply chain, this ecosystem of cyber criminals and how we can hit them, where it hurts on all angles. I've most recently, um, I've been heavily involved with the world economic forum. Uh, so I'm, co-author of a report from last year of the partnership on cyber crime. And, uh, this is really not just the pro uh, private, private sector, but the private and public sector working together. We know a lot about cybercriminals. We can't arrest them. Uh, we can't take servers offline from the data centers, but working together, we can have that whole, you know, that holistic effect. >>Great. Thank you for that, Derek. What if people want, want to go deeper? Uh, I know you guys mentioned that you do blogs, but are there other resources that, that they can tap? Yeah, absolutely. So, >>Uh, everything you can see is on our threat research blog on, uh, so 40 net blog, it's under expired research. We also put out, uh, playbooks, w we're doing blah, this is more for the, um, the heroes as he called them the security operation centers. Uh, we're doing playbooks on the aggressors. And so this is a playbook on the offense, on the offense. What are they up to? How are they doing that? That's on 40 guard.com. Uh, we also release, uh, threat signals there. So, um, we typically release, uh, about 50 of those a year, and those are all, um, our, our insights and views into specific attacks that are now >>Well, Derek Mackie, thanks so much for joining us today. And thanks for the work that you and your teams do. Very important. >>Thanks. It's yeah, it's a pleasure. And, uh, rest assured we will still be there 24 seven, three 65. >>Good to know. Good to know. And thank you for watching everybody. This is Dave Volante for the cube. We'll see you next time.

>>From around the globe presenting the convergence of file and object brought to you by pure storage. Okay. >>We're back with the convergence of file and object and a power panel. This is a special content program made possible by pure storage. And co-created with the cube. Now in this series, what we're doing is we're exploring the coming together of file and object storage, trying to understand the trends that are driving this convergence, the architectural considerations that users should be aware of and which use cases make the most sense for so-called unified fast file in object storage. And with me are three great guests to unpack these issues. Garrett bell center is the data center solutions architect he's with CDW. Scott Sinclair is a senior analyst at enterprise strategy group. He's got deep experience on enterprise storage and brings that independent analyst perspective. And Matt Burr is back with us, gentlemen, welcome to the program. >>Thank you. >>Hey Scott, let me, let me start with you, uh, and get your perspective on what's going on in the market with, with object to cloud huge amount of unstructured data out there. It lives in files. Give us your independent view of the trends that you're seeing out there. >>Well, Dave, you know where to start, I mean, surprise, surprise data's growing. Um, but one of the big things that we've seen is that we've been talking about data growth for what decades now, but what's really fascinating is or changed is because of the digital economy, digital business, digital transformation, whatever you call it. Now, people are not just storing data. They actually have to use it. And so we see this in trends like analytics and artificial intelligence. And what that does is it's just increasing the demand for not only consolidation of massive amounts of storage that we've seen for awhile, but also the demand for incredibly low latency access to that storage. And I think that's one of the things that we're seeing, that's driving this need for convergence, as you put it of having multiple protocols can Solidated onto one platform, but also the need for high performance access to that data. >>Thank you for that. A great setup. I got, like I wrote down three topics that we're going to unpack as a result of that. So Garrett, let me, let me go to you. Maybe you can give us the perspective of what you see with customers is, is this, is this like a push where customers are saying, Hey, listen, I need to converge my file and object. Or is it more a story where they're saying, Garrett, I have this problem. And then you see unified file and object as a solution. >>Yeah, I think, I think for us, it's, you know, taking that consultative approach with our customers and really kind of hearing pain around some of the pipelines, the way that they're going to market with data today and kind of what are the problems that they're seeing. We're also seeing a lot of the change driven by the software vendors as well. So really being able to support a dis-aggregated design where you're not having to upgrade and maintain everything as a single block has been a place where we've seen a lot of customers pivot to where they have more flexibility as they need to maintain larger volumes of data and higher performance data, having the ability to do that separate from compute and cash. And some of those other layers are, is really critical. >>So, Matt, I wonder if you could follow up on that. So, so Gary was talking about this dis-aggregated design, so I like it, you know, distributed cloud, et cetera, but then we're talking about bringing things together in one place, right? So square that circle. How does this fit in with this hyper distributed cloud edge that's getting built out? >>Yeah. You know, I mean, I could give you the easy answer on that, but I can also pass it back to Garrett in the sense that, you know, Garrett, maybe it's important to talk about, um, elastic and Splunk and some of the things that you're seeing in, in that world and, and how that, I think the answer today, the question I think you can give, you can give a pretty qualified answer relative to what your customers are seeing. >>Oh, that'd be great, please. >>Yeah, absolutely. No, no problem at all. So, you know, I think with, um, Splunk kind of moving from its traditional design and classic design, whatever you want to, you want to call it up into smart store? Um, that was kind of one of the first that we saw kind of make that move towards kind of separating object out. And I think, you know, a lot of that comes from their own move to the cloud and updating their code to basically take advantage of object object in the cloud. Um, but we're starting to see, you know, with like Vertica Ian, for example, um, elastic other folks taking that same type of approach where in the past we were building out many to use servers. We were jamming them full of, uh, you know, SSDs and then DME drives. Um, that was great, but it doesn't really scale. >>And it kind of gets into that same problem that we see with hyperconvergence a little bit where it's, you know, you're all, you're always adding something maybe that you didn't want to add. Um, so I think it, you know, again, being driven by software is really kind of where we're seeing the world open up there. Um, but that whole idea of just having that as a hub and a central place where you can then leverage that out to other applications, whether that's out to the edge for machine learning or AI applications to take advantage of it. I think that's where that convergence really comes back in. Um, but I think like Scott mentioned earlier, it's really folks are now doing things with the data where before I think they were really storing and trying to figure out what are we going to actually do with it when we need to do something with it? So this is making it possible. >>Yeah. And Dave, if I could just sort of tack onto the end of the Garrett's answer there, you know, in particular verdict with beyond mode, the ability to leverage sharted sub clusters, give you, um, you know, sort of an advantage in terms of being able to isolate performance, hotspots you an advantage to that as being able to do that on a flash blade, for example. So, um, sharted, sub clusters allow you to sort of say, I am, you know, I am going to give prioritization to, you know, this particular element of my application in my dataset, but I can still share those, share that data across those, across those sub clusters. So, um, you know, as you see, you know, Vertica with the non-motor, >>You see Splunk advanced with, with smart store, um, you know, these are all sort of advancements that are, you know, it's a chicken and the egg thing. Um, they need faster storage, they need, you know, sort of a consolidated data storage data set. Um, and, and that's what sort of allows these things to drive forward. Yes, >>The verdict eon mode, there was a no, no, it's the ability to separate compute and storage and scale independently. I think, I think Vertica, if they're, if they're not the only one, they're one of the only ones I think they might even be the only one that does that in the cloud and on prem and that sort of plays into this distributed nature of this hyper distributed cloud. I sometimes call it and I'm interested in the, in the data pipeline. And I wonder Scott, if we can talk a little bit about that maybe where unified object and file fund. I mean, I'm envisioning this, this distributed mesh and then, you know, UFO is sort of a note on that, that I can tap when I need it. But, but Scott, what are you seeing as the state of infrastructure as it relates to the data pipeline and the trends there? >>Yeah, absolutely. Dave, so w when I think data pipeline, I immediately gravitate to analytics or, or machine learning initiatives. Right. And so one of the big things we see, and this is, it's an interesting trend. It seems, you know, we continue to see increased investment in AI, increase interest and people think, and as companies get started, they think, okay, well, what does that mean? Well, I gotta go hire a data scientist. Okay. Well that data scientist probably needs some infrastructure. And what they end, what often happens in these environments is where it ends up being a bespoke environment or a one-off environment. And then over time organizations run into challenges. And one of the big challenges is the data science team or people whose jobs are outside of it, spend way too much time trying to get the infrastructure, um, to, to keep up with their demands and predominantly around data performance. So one of the, one of the ways organizations that especially have artificial intelligence workloads in production, and we found this in our research have started mitigating that is by deploying flash all across the data pipe. We have. Yeah, >>We have data on this. Sorry to interrupt, but Pat, if you could bring up that, that chart, that would be great. Um, so take us through this, uh, Scott and, and share with us what we're looking at here. >>Yeah, absolutely. So, so Dave, I'm glad you brought this up. So we did this study. Um, I want to say late last year, uh, one of the things we looked at was across artificial intelligence environments. Now, one thing that you're not seeing on this slide is we went through and we asked all around the data pipeline and we saw flash everywhere. But I thought this was really telling because this is around data lakes. And when many people think about the idea of a data Lake, they think about it as a repository. It's a place where you keep maybe cold data. And what we see here is especially within production environments, a pervasive use of flash stores. So I think that 69% of organizations are saying their data Lake is mostly flash or all flash. And I think we had 0% that don't have any flash in that environment. So organizations are out that thing that flashes in essential technology to allow them to harness the value of their data. >>So Garrett, and then Matt, I wonder if you could chime in as well. We talk about digital transformation and I, I sometimes call it, you know, the COVID forced March to digital transformation. And, and I'm curious as to your perspective on things like machine learning and the adoption, um, and Scott, you may have a perspective on this as well. You know, we had to pivot, he had to get laptops. We had to secure the end points, you know, VDI, those became super high priorities. What happened to, you know, injecting AI into my applications and, and machine learning. Did that go in the back burner? Was that accelerated along with the need to digitally transform, uh, Garrett, I wonder if you could share with us what you saw with, with customers last year? >>Yeah. I mean, I think we definitely saw an acceleration. Um, I think folks are in, in my market are, are still kind of figuring out how they inject that into more of a widely distributed business use case. Um, but again, this data hub and allowing folks to now take advantage of this data that they've had in these data lakes for a long time. I agree with Scott. I mean, many of the data lakes that we have were somewhat flashing, accelerated, but they were typically really made up of large capacity, uh, slower spinning nearline drives, um, accelerated with some flash, but I'm really starting to see folks now look at some of those older Hadoop implementations and really leveraging new ways to look at how they consume data. And many of those redesigned customers are coming to us, wanting to look at all flash solutions. So we're definitely seeing it. And we're seeing an acceleration towards folks trying to figure out how to actually use it in more of a business sense now, or before I feel it goes a little bit more skunkworks kind of people dealing with, uh, you know, in a much smaller situation, maybe in the executive offices trying to do some testing and things. >>Scott you're nodding away. Anything you can add in here. >>Yeah. So, well, first off, it's great to get that confirmation that the stuff we're seeing in our research, Garrett seeing, you know, out in the field and in the real world, um, but you know, as it relates to really the past year, it's been really fascinating. So one of the things we, we studied at ESG is it buying intentions. What are things, what are initiatives that companies plan to invest in? And at the beginning of 2020, we saw heavy interest in machine learning initiatives. Then you transition to the middle of 2020 in the midst of COVID. Uh, some organizations continued on that path, but a lot of them had the pivot, right? How do we get laptops, everyone? How do we continue business in this new world? Well, now as we enter into 2021, and hopefully we're coming out of this, uh, you know, the, the pandemic era, um, we're getting into a world where organizations are pivoting back towards these strategic investments around how do I maximize the usage of data and actually accelerating those because they've seen the importance of, of digital business initiatives over the past >>Year. >>Yeah, Matt, I mean, when we exited 2019, we saw a narrowing of experimentation in our premise was, you know, that that organizations are going to start now operationalizing all their digital transformation experiments. And, and then we had a 10 month Petri dish on, on digital. So what are you, what are you seeing in this regard? >>It's 10 months, Petri dish is an interesting way to interesting way to describe it. Um, you know, we, we saw another, there's another, there's another candidate for pivot in there around ransomware as well. Right. Um, you know, security entered into the mix, uh, which took people's attention away from some of this as well. I mean, look, I I'd like to bring this up just a level or two, um, because what we're actually talking about here is progress, right? And, and progress is an, is an inevitability. Um, you know, whether it's whether, whether you believe that it's by 20, 25 or you, or you think it's 20, 35 or 2050, it doesn't matter. We're on a forced March to the eradication of desk. And that is happening in many ways. Uh, you know, in many ways, um, due to some of the things that Garrett was referring to and what Scott was referring to in terms of what our customer's demands for, how they're going to actually leverage the data that they have. >>And that brings me to kind of my final point on this, which is we see customers in three phases. There's the first phase where they say, Hey, I have this large data store, and I know there's value in there. I don't know how to get to it. Or I have this large data store and I've started a project to get value out of it. And we failed. Those could be customers that, um, you know, marched down the dupe, the Hadoop path early on. And they, they, they got some value out of it. Um, but they realized that, you know, HDFS, wasn't going to be a modern protocol going forward for any number of reasons. You know, the first being, Hey, if I have gold dot master, how do I know that I have gold dot four is consistent with my gold dot master? So data consistency matters. >>And then you have the sort of third group that says, I have these large datasets. I know how to extract value from them. And I'm already on to the Vertica is the elastics, you know, the Splunks et cetera. Um, I think those folks are the folks that, that latter group are the folks that kept their, their, their projects going because they were already extracting value from them. The first two groups we were seeing, sort of saying the second half of this year is when we're going to begin really being picking up on these, on these types of initiatives again. >>Well, thank you, Matt, by the way, for, for hitting the escape key, because I think value from data really is what this is all about. And there are some real blockers there that I kind of want to talk about. You've mentioned HDFS. I mean, we were very excited, of course, in the early days of a dupes, many of the concepts were profound, but at the end of the day, it was too complicated. We've got these hyper specialized roles that are, that are serving the business, but it still takes too long. It's, it's too hard to get value from data. And one of the blockers is infrastructure that the complexity of that infrastructure really needs to be abstracted taken up a level. We're starting to see this in, in cloud where you're seeing some of those abstraction layers being built from some of the cloud vendors, but more importantly, a lot of the vendors like pure, Hey, we can do that heavy lifting for you. Uh, and we, you know, we have expertise in engineering to do cloud native. So I'm wondering what you guys see. Maybe Garrett, you could start us off and the other salmon as some of the blockers, uh, to getting value from data and how we're going to address those in the coming decade. >>Yeah. I mean, I think part of it we're solving here obviously with, with pure bringing, uh, you know, flash to a market that traditionally was utilizing a much slower media. Um, you know, the other thing that I, that I see that's very nice with flash blade for example, is the ability to kind of do things, you know, once you get it set up a blade at a time. I mean, a lot of the things that we see from just kind of more of a simplistic approach to this, like a lot of these teams don't have big budgets and being able to kind of break them down into almost a blade type chunk, I think has really kind of allowed folks to get more projects and, and things off the ground because they don't have to buy a full expensive system to run these projects. Um, so that's helped a lot. >>I think the wider use cases have helped a lot. So, um, Matt mentioned ransomware, um, you know, using safe mode as a, as a place to help with ransomware has been a really big growth spot for us. We've got a lot of customers, very interested and excited about that. Um, and the other thing that I would say is bringing dev ops into data is another thing that we're seeing. So kind of that push towards data ops and really kind of using automation and infrastructure as code as a way to now kind of drive things through the system. The way that we've seen with automation through dev ops is, is really an area we're seeing a ton of growth with from a services perspective, >>Guys, any other thoughts on that? I mean, we're, I I'll, I'll tee it up there. I, we are seeing some bleeding edge, which is somewhat counterintuitive, especially from a cost standpoint, organizational changes at some, some companies, uh, think of some of the, the, the, the internet companies that do, uh, music, uh, for instance, and adding podcasts, et cetera. And those are different data products. We're seeing them actually reorganize their data architectures to make them more distributed, uh, and actually put the domain heads, the business heads in charge of the data and the data pipeline. And that is maybe less efficient, but, but it's, again, some of these bleeding edge. What else are you guys seeing out there that might be some harbinger of the next decade? >>Uh, I'll go first. Um, you know, I think specific to, um, the, the construct that you threw out, Dave, one of the things that we're seeing is, um, you know, the, the, the application owner, maybe it's the dev ops person, but it's, you know, maybe it's, it's, it's, it's the application owner through the dev ops person. They're, they're becoming more technical in their understanding of how infrastructure, um, interfaces with their, with their application. I think, um, you know, what, what we're seeing on the flash blade side is we're having a lot more conversations with application people than, um, just it people. It doesn't mean that the, it people aren't there, the it, people are still there for sure if they have to deliver the service, et cetera. Um, but you know, the days of, of it, you know, building up a catalog of services and a business owner subscribing to one of those services, you know, picking, you know, whatever sort of fits their need. >>Um, I don't think that constant, I think that's the construct that changes going forward. The application owner is becoming much more prescriptive about what they want the infrastructure to fit, how they want the infrastructure to fit into their application. Um, and that's a big change. And for, for, um, you know, certainly folks like, like Garrett and CDW, um, you know, they do a good job with this being able to sort of get to the application owner and bring those two sides together. There's a tremendous amount of value there, uh, for us to spend a little bit of a, of a retooling we've traditionally sold to the it side of the house. And, um, you know, we've had to teach ourselves how to go talk the language of, of applications. So, um, you know, I think you pointed out a good, a good, a good construct there, and you know, that that application owner tank playing a much bigger role in what they're expecting from the performance of it, infrastructure I think is, is, is a key, is a key change. >>Interesting. I mean, that definitely is a trend. That's puts you guys closer to the business where the infrastructure team is serving the business, as opposed to sometimes I talked to data experts and they're frustrated, uh, especially data owners or data, product builders who are frustrated that they feel like they have to beg, beg the, the data pipeline team to get, you know, new data sources or get data out. How about the edge? Um, you know, maybe Scott, you can kick us off. I mean, we're seeing, you know, the emergence of, of edge use cases, AI inferencing at the edge, lot of data at the edge. W what are you seeing there and how does this unified object I'll bring us back to that in file fit. >>Wow. Dave, how much time do we have, um, tell me, first of all, Scott, why don't you, why don't you just tell everybody what the edge is? Yeah. You got it all figured out. How much time do you have end of the day. And that's, that's a great question, right? Is if you take a step back and I think it comes back to Dave, something you mentioned it's about extracting value from data. And what that means is when you extract value from data, what it does is as Matt pointed out the, the influencers or the users of data, the application owners, they have more power because they're driving revenue now. And so what that means is from an it standpoint, it's not just, Hey, here are the services you get, use them or lose them, or, you know, don't throw a fit. It is no, I have to, I have to adapt. I have to follow what my application owners me. Now, when you bring that back to the edge, what it means is, is that data is not localized to the data center. I mean, we just went through a nearly 12 month period where >>The entire workforce for most of the companies in this country had went distributed and business continued. So if business is distributed, data is distributed. And that means, that means in the data center, that means at the edge, that means that the cloud, and that means in all other places and tons of places. And what it also means is you have to be able to extract and utilize data anywhere it may be. And I think that's something that we're going to continue to and continue to see. And I think it comes back to, you know, if you think about key characteristics, we've talked about, um, things like performance and scale for years, but we need to start rethinking it because on one hand, we need to get performance everywhere. But also in terms of scale, and this ties back to some of the other initiatives and getting value from data, it's something I call the, the massive success problem. One of the things we see, especially with, with workloads like machine learning is businesses find success with them. And as soon as they do they say, well, I need about 20 of these projects now will all of a sudden that overburdens it organizations, especially across, across core and edge and cloud environments. And so when you look at environments ability to meet performance and scale demands, wherever it needs to be is something that's really important. You know, >>Dave, I'd like to, um, just sort of tie together sort of two things that, um, I think that I heard from Scott and Garrett that I think are important and it's around this concept of scale. Um, you know, some of us are old enough to remember the day when kind of a 10 terabyte blast radius was too big of a blast radius for people to take on, or a terabyte of storage was considered to be, um, you know, uh, uh, an exemplary budget environment. Right. Um, now we sort of think as terabytes, kind of like we used to think of as gigabytes in some ways, um, petabyte, like you don't have to explain to anybody what a petabyte is anymore. Um, and you know, what's on the horizon and it's not far are our exabyte type dataset workloads. Um, and you start to think about what could be in that exabyte of data. >>We've talked about how you extract that value. And we've talked about sort of, um, how you start, but if the scale is big, not everybody's going to start at a petabyte or an exabyte to Garrett's point, the ability to start small and grow into these products, or excuse me, these projects, I think is a, is a really, um, fundamental concept here because you're not going to just go buy five. I'm going to go kick off a five petabyte project, whether you do that on disk or flash, it's going to be expensive, right. But if you could start at a couple of hundred terabytes, not just as a proof of concept, but as something that, you know, you could get predictable value out of that, then you could say, Hey, this either scales linearly, or non-linearly in a way that I can then go map my investments to how I can go dig deeper into this. That's how all of these things are going to, that's how these successful projects are going to start, because the people that are starting with these very large, you know, sort of, um, expansive, you know, Greenfield projects at multi petabyte scale, it's gonna be hard to realize near-term value. Excellent. Uh, >>We we're, we gotta wrap, but, but Garrett, I wonder if you could close it, when you look forward, you talk to customers, do you see this unification of file and object? Is it, is this an evolutionary trend? Is it something that is, that is, that is, that is going to be a lever that customers use. How do you see it evolving over the next two, three years and beyond? >>Yeah, I mean, I think from our perspective, I mean, just from what we're seeing from the numbers within the market, the amount of growth that's happening with unstructured data is really just starting to finally really kind of hit this data delusion or whatever you want to call it that we've been talking about for so many years. Um, it really does seem to now be becoming true, um, as we start to see things scale out and really folks settle into, okay, I'm going to use the cloud to start and maybe train my models, but now I'm going to get it back on prem because of latency or security or whatever the, the, the, um, decision points are there. Um, this is something that is not going to slow down. And I think, you know, folks like pure having the ability to have the tools that they give us, um, do use and bring to market with our customers are, are really key and critical for us. So I see it as a huge growth area and a big focus for us moving forward, >>Guys, great job unpacking a topic that, you know, it's covered a little bit, but I think we, we covered some ground. That is a, that is new. And so thank you so much for those insights and that data really appreciate your time. >>Thanks, Dave. Thanks. Yeah. Thanks, Dave. >>Okay. And thank you for watching the convergence of file and object. Keep it right there. Bright, bright back after the short break.

>>From the cubes studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is a cute conversation. >>The external storage array business, as we know it has changed forever. You know, you can see that in the survey data that we do and the financial information from the largest public storage companies. And it's not just because of COVID, although that's clearly a factor which has accelerated the shifts that we see in the market, specifically, those CIO is, are rationalizing their infrastructure portfolios by consolidating workloads to simplify, reduce costs and minimize vendor sprawl. So they can shift resources toward digital initiatives that include cloud containers, machine intelligence, and automation all while reducing their risks. Hello everyone. This is Dave Vellante and welcome to this cube conversation where we're going to discuss strategies related to workload consolidation at petabyte scale. And with me is Dr. Rico. He's the vice president office of the CTO at INFINIDAT welcome back to the cube doc, always a pleasure to see you >>And great to be here. Always a pleasure to work with you, Dave. >>So doc, I just published a piece over the weekend and I pointed out that of the largest storage companies, only one showed revenue growth last quarter, and that was on a significantly reduced compare with last year. So my first question to you is, is INFINIDAT growing its business. >>Oh, absolutely. It's been a very interesting year all across as you can quite imagine. Um, but you know, our footprint is such that with our elastic pricing models and the, and the fact that we've got excess capacity, uh, in almost every single system that's out there, we were really given our customers a, an opportunity to take advantage of that, to increase their capacity levels while maintaining the same levels of performance and availability, but not have to have anybody on premises during this crazy, you know, COVID struck era. >>Yeah. So you're bringing that cloud model to the, to the data center, which has obviously been a challenge. I mean, you mentioned the subscription sort of like pricing, we're going to get into the cloud more, but I wonder if we could step back a little bit and look at some of the macro trends that you're seeing in the market and specifically as it relates to on-prem storage strategies that CEO's are taking. >>Yeah. You know, it's been interesting, we've seen over the course of the past five years or so, certainly a big uptick in people looking at next generation or what they believe in perceived to be next generation storage platforms, which are really just evolutions of media. They're not really taking advantage of any new innovations in storage and, you know, not withstanding our own products, which are all software driven. We've talked about that before, but what what's really happened in this past year, as you, as you said, CEOs and CTOs, they're always looking for that, that next point of leverage advantage. And they're looking for more agility in application deployment, they're looking in a way to rapidly respond to business requirements. So they're looking very much at those cloud-like requirements. They're looking at those capabilities to containerize applications. They're looking at how they can, um, you know, shift out virtual machines if they're not in a directly in a container, uh, and how the storage by the way, can, can have the same advantage and in order to do so, they really need to look at storage consolidation. You know, I think Dave, to, to sum it up from the storage perspective, you know, I love Ken Steinhardt was recently on a video and, you know, he was, he was challenged that, you know, people aren't looking at spinning rust, riff, you know, a derogatory wave of referring a disc and, and Ken, so rightly and accurately responded. Yeah. But people weren't really looking for QLC either. You know, what they're looking for is performance, scale availability and certainly cost effectiveness and price. >>Yeah. It was like, I set up front dock. I mean, if you're a C level executive today, you don't want to worry about your storage infrastructure. You've got bigger problems to worry about. You just want it to work. And so when you talk about consolidating workloads, people often talk about the so-called blast radiation. In other words, people who run data centers, they understand that things fail. And sometimes something as simple, it might be a power supply can have a catastrophic downstream effect on application availability. So my question is, how do you think about architecting systems? So as to minimize the effects of component failures on the business? >>Yeah. You know, it's a very interesting term, Dave blast radius, right? We've, we've heard this referred to storage over the last several decades. In fact, when it really should refer to the data center and the application infrastructure. Uh, but you know, if we're talking about just the storage footprint itself, one of the things that we really need to look, look at is the resilience and the reliability of the architecture. And when you look at something that is maybe dual controller single or double power supply, there are issues and concerns that take in, in, into, into play. And what we've done is we've designed something that's really triple redundant, which is typically only been applied to the very high end of the market before. And we do it in a very active, active, active manner. And naturally we have suggestions for best practices for deployment within a data center as well, you know, multiple sources of power coming into the array and things of that nature. But everything needs to be this active, active, active type of architecture in order to bring those reliability levels up to the point where as long as it's a component failure within the array, it's not going to cause an outage or data on availability event. >>Yeah. So imagine a heat map when people talk about the blast radius. So imagine the heat map is green. There's a big, you know, there's a yellow area and there's a, there's a red area. And what you're saying is, as far as the array goes, you're essentially eliminating the red area. Now, if you take it to the broader installation, you know, that red area, you have to deal with it in different ways, remote replication, then you can at the sink and in a sink. Uh, but, but essentially what I'm hearing you say, doc, is, is you're squeezing that red area out. So, so your customers could sleep at night. >>That absolutely sleep at night is so appropriate. And in fact, we've got a large portion of our customer base is, or they're running mission critical businesses. You know, we have some of the most mission critical companies in our, in our logo portfolio, in the world. We also have, by the way, some very significant service provider businesses who were we're providing, you know, mission critical capabilities to their customers in turn, and they need to sleep at night. And it it's, you know, availability is only one factor. Certainly manageability is another cause you know, not meeting a service level is just like data unavailability in some respects. So making manageability is automatic as it can be making sure that the, that the system is not only self-healing, but can re respond to variations in workload appropriately is very, very critically important as well. >>Yeah. So that, that you mentioned mission critical workloads, and those are the, those are the workloads that let's face it. They're not moving into the cloud, certainly not in any, any big way, you know, why would they generally are CIO CTO is they're putting a brick wall around that saying, Hey, it works. We don't want to migrate that piece, but I want to talk more about how your customers are thinking about workload consolidation and rationalizing their storage portfolios. What are those conversations like? Where do they start and what are some of the outcomes that you're seeing with your customers? >>Yeah, I think the funny thing about that point Dave, is that customers are really starting to think about a cloud in an entirely different way. You know, at one point cloud meant public cloud and men, this entity, uh, outside the walls of the data center and people were starting to use services without realizing that that was another type of cloud. And then they were starting to build their own versions of cloud. You know, we were referring to them as private clouds, but they were, you know, really spread beyond the walls of a single data center. So now it's a very hybrid world and there's lots of different ways to look at it, hybrid cloud multi-cloud, whatever moniker you want to put on it. It really comes down to a consistency in how you manage that infrastructure, how you interface with that infrastructure and then understanding what the practicality is of putting workloads in different places. >>And practicality means not only the, you know, the latency of access of the data, but the costs associated with it. And of course the other aspects that we talked about, like what the, the availability metrics, and as you increase the availability and performance metrics, those costs go up. And that's one of the reasons why some of these larger mission critical data centers are really, you know, repatriating their, their mission, critical workloads, at least the highest, highest levels of them and others are looking at other models, for example, AWS outposts, um, which, you know, talked about quite a bit recently in AWS reinvent. >>Yeah. I just wrote, again, this weekend that you guys were one of the, uh, partners that was qualified now, uh, to run on AWS outpost, it's interesting as Amazon moves, it's, you know, it's, it's it's model to the edge, which includes the data center to them. They need partners that can, that really understand how to operate in an on-premise world, how to service those customers. And so that's great to see you guys as part of that. >>Yeah. Thank you. And, you know, it was actually a very seamless integration because of the power and capability of all of the different interface models that we have is they all are fully and tightly integrated and work seamlessly. So if you want to use a, you know, a CSI type model, uh, you know, do you interface with your storage again, uh, with, with INFINIDAT and, you know, we work with all of the different flavors so that the qualification process, the certification process and the documentation process was actually quite easy. And now we're able to provide, you know, people who have particularly larger workloads that capability in the AWS on premises type environment. >>Yeah. Now I implied upfront that that cloud computing was the main factor, if not the primary factor, really driving some of the changes that we're seeing in the marketplace. Now, of course, it's all, not all pink roses with the cloud. We've seen numerous public cloud outages this year, certainly from Microsoft. We saw the AWS Kinesis outage in November. Google just had a major outage this month. Gmail was down G suite was down for an extended period of time. And that disrupted businesses, we rely on that schools, for example. So it's always caveat emptor as we know, but, but talk to INFINIDAT cloud strategy, you mentioned hybrid, uh, particularly interested in, in how you're dealing with things like orchestration and containers and Kubernetes. >>Yeah, well, of course we have a very feature rich set of interfaces for containers, Kubernetes interfaces, you know, downloadable through native, uh, native. So they're, they're very easy to integrate with, you know, but our cloud strategy is that, you know, we are a software centric model and we, you know, all of the, all of the value and feature function that we provide is through the software. The hardware of infiniboxes really a reference architecture that we, uh, we deliver to make it easier for customers to enjoy say 100% availability model. But if, if you want to run something in a traditional on premises data center, you know, straighten InfiniBox is fine, but we also give you the flexibility of cloud-like consumption through our pricing models, our, our elastic pricing models. So you don't need to consume an entire InfiniBox day one. You can grow and shrink that environment with, uh, with an OPEX model, or you can, um, buy it as you consume it in a, in a cap ex model. >>And you can switch, uh, from OPEX over to CapEx if it becomes more cost effective for you in time, which I think is, is what a lot of people are looking for. If you're looking for that public cloud, we, you know, we have our new tricks cloud offering, which is now being delivered more through partners, but you know, some businesses and especially the, the mid tier, um, you know, the SMB all the way through the mid enterprise are also now looking to cloud service providers, many of which use InfiniBox as, as their backend. And now with AWS outposts, of course, you know, we can give you that on premises, uh, uh, experience of the public cloud, >>You guys were early on. And obviously in that, that subscription-based model, and now everyone's doing it. I noticed in the latest Gartner magic quadrant on, on storage arrays, which you guys were named a leader, uh, they, I think they had a stat in there and said, I, I forget what the exact timeframe was that 50% of customers would be using that type of model. And again, I guarantee you by whatever time frame, that was a hundred percent of the vendor community is going to be delivering that type of model. So, so congratulations on being named a leader, I will say this there's there's there's consolidation happening in the market. So this, to me, this bodes well, to the extent that you can guarantee high availability and consistent performance, uh, at, at scale, that bodes well for, for you guys in a consolidating market. And I know IDC just released a paper, it was called, uh, I got, uh, I got a copy here. >>It's called a checklist for, uh, storage, workload consolidation at petabyte scale. It was written by Eric Bergner, who I've known for a number of years. He's the VP of infrastructure. Uh, he knows his stuff and the paper is very detailed. So I'm not going to go through the checklist items, but I, but I think if you don't mind, doc, I think it's worth reading an excerpt from this. If I can, as part of his conclusions, when workload consolidation, it organizations should carefully consider their performance availability, functionality, and affordability requirements. Of course, few storage systems in the market will be able to cost effectively consolidate different types of workloads with different IO profiles onto a single system. But that is in INFINIDAT forte. They're very good at it. So that's a, that's quite a testimonial, you know, why is that your thoughts on what Eric wrote? >>Well, you know, first of all, thank you for the kudos on the Gartner MQ, you know, being a leader on the second year in a row for primary storage, only because that documents only existed for two years, but, uh, you know, we were also a leader in hybrid storage arrays before that. And, you know, we, we love Gardner. We think they're, they're, you know, um, uh, real critical, you know, reliable source for, for a lot of large companies and, and IDC, you know, Eric of course is, uh, he's a name in the industry. So we, you know, we very much appreciate when he writes something, you know, that positive about us. But to answer your question, Dave, you know, there's, there's a lot that goes on inside InfiniBox and is the neural cash capabilities, the deep learning engine that is able to understand the different types of workloads, how they operate, uh, how to provide, you know, predictable performance. >>And that I think is ultimately key to an application. It's not just high performance. It's, it's predictable performance is making sure the application knows what to expect. And of course it has to be performant. It can't just be slow, but predictable. It has to be fast and predictable providing a multi-tenant infrastructure that is, that is native to the architecture, uh, so that these workloads can coexist whether they're truly just workloads from multiple applications or workloads from different business units, or potentially, as we mentioned with cloud service providers, workloads from different customers, you know, they, they need to be segmented in such a way so that they can be managed, operating and provide that performance and availability, you know, at scale because that's where data centers go. That's where data centers are. >>Great. Well, so we'll bring that graphic back up just to show you, obviously, this is available on your website. Uh, you can go download this paper from Erik, uh, from IDC, www infinidat.com/ian/resource. I would definitely recommend you check it out. Uh, as I say, Ericsson, you know, I've been in the business a long, long time, so, so that's great, doc, we'll give you the last word. Anything we didn't cover any big takeaways you want to, you want to share with the audience? >>Yeah. You know, I think I'll go back to that point. You know, consolidation is absolutely key for, uh, not just simplicity of management, but capability for you respond quickly to changing business requirements and or new business requirements, and also do it in a way that is cost-effective, you know, just buying the new shiny object is it's expensive and it's very limited in, in shelf life. You're just going to be looking for the next one the next year. You want to provide something that is going to provide you that predictable capability over time, because frankly, I have never met a C X O of anything that wasn't trying to increase their profit. >>You know, that's a great point. And I just, I would add, I mean, the shiny new object thing. Look, if you're in an experimental mode and playing around with, you know, artificial intelligence or automation thinking, you know, areas that you really don't know a lot about, you know, what, check out the shiny new objects, but I would argue you're on-prem storage. You don't want to be messing around with that. That's, it's not a shiny new objects business. It's really about, you know, making sure that that base is stable. And as you say, predictable and reliable. So doc Terico thanks so much for coming back into cube. Great to see you. >>Great to see you, David, and look forward to next time. >>And thank you for watching everybody. This is Dave Volante and we'll see you next time on the queue.

>>Yeah. >>All right. Welcome back to our third session, which is all about administering analytics at Global Scale. We're gonna be discussing how you can implement security data compliance and governance across the globe at for large numbers of users to ensure thoughts. What is open for everyone across your organization? So coming right up is Cheryl Zang, who is a senior director of product management of Thought spot, and Kendrick. He threw the sports sports director of Systems Engineering. So, Cheryl and Kendrick, the floor is yours. >>Thank you, Tina, for the introduction. So let's talk about analytics scale on. Let's understand what that is. It's really three components. It's the access to not only data but its technology, and we start looking at the intersection of that is the value that you get as an organization. When you start thinking about analytics scale, a lot of times we think of analysts at scale and we look at the cloud as the A seven m for it, and that's a That's an accurate statement because people are moving towards the cloud for a variety of reasons. And if you think about what's been driving, it has been the applications like Salesforce, Forcados, Mongo, DB, among others. And it's actually part of where we're seeing our market go where 64% of the company's air planning to move their analytics to the cloud. And if you think of stock spotted specifically, we see that vast majority of our customers are already in the cloud with one of the Big Four Cloud Data warehouses, or they're evaluated. And what we found, though, is that even though companies are moving their analytics to the cloud, we have not solved. The problem of accessing the data is a matter of fact. Our customers. They're telling us that 10 to 25% of that data warehouse that they're leveraging, they've moved and I'm utilizing. And if you look at in General, Forrester says that 60 to 73% of data that you have is not being leveraged, and if we think about why you go through, you have this process of taking enterprise data, moving it into these cubes and aggregates and building these reports dashboards. And there's this bottleneck typically of that be I to and at the end of the day, the people that are getting that data on the right hand side or on Lee. Anywhere from 20 to 30% of the population when companies want to be data driven is 20 to 30% of the population. Really what you're looking for now it's something north of that. And if you think of Cloud data, warehouse is being the the process and you bring Cloud Data Warehouse and it's still within the same framework. You know? Why invest? Why invest and truly not fix the problem? And if you take that out and your leverage okay, you don't necessarily have the You could go directly against the warehouse, but you're still not solving the reports and dashboards. Why investing truly not scale? It's the three pillars. It's technology, it's data, and it's a accessibility. So if we look at analytics at scale, it truly is being able to get to that north of the 20 to 30% have that be I team become enablers, often organization. Have them be ableto work with the data in the Cloud Data warehouse and allow the cells marking finding supplies and then hr get direct access to that. Ask their own questions to be able to leverage that to be able to do that. You really have to look at your modern data architecture and figure out where you are in this maturity, and then they'll be able to build that out. So you look at this from the left to right and sources. It's ingestion transformation. It's the storage that the technology brains e. It's the data from a historical predictive perspective. And then it's the accessibility. So it's technology. It's data accessibility. And how do you build that? Well, if you look at for a thought to spot perspective, it truly is taking and driving and leveraging the cloud data warehouse architectures, interrogated, essay behind it. And then the accessibility is the search answers pen boards and embedded analytics. If you take that and extend it where you want to augment it, it's adding our partners from E T L R E L t. Perspective like al tricks talent Matile Ian Streaming data from data brings or if you wanna leverage your cloud, data warehouses of Data Lake and then leverage the Martin capability of your child data warehouse. The augmentation leveraging out through its data bricks and data robot. And that's where your data side of that pillar gets stronger, the technologies are enabling it. And then the accessibility from the output. This thought spot. Now, if you look at the hot spots, why and how do we make this technology accessible? What's the user experience we are? We allow an organization to go from 20 to 30% population, having access to data to what it means to be truly data driven by our users. That user experience is enabled by our ability to lead a person through the search process. There are search index and rankings. This is built for search for corporate data on top of the Cloud Data Warehouse. On top of the data that you need to be able to allow a person who doesn't understand analytics to get access to the data and the questions they need to answer, Arcuri Engine makes it simple for customers to take. Ask those questions and what you might think are not complex business questions. But they turn into complex queries in the back end that someone who typically needs to know that's that power user needs to know are very engine. Isolate that from an end user and allows them to ask that question and drive that query. And it's built on an architecture that allows us to change and adapt to the types of things. It's micro services architecture, that we've not only gone from a non grim system to our cloud offering, in a matter of of really true these 23 years. And it's amazing the reason why we can do that, do that and in a sense, future proof your investment. It's because of the way we've developed this. It's wild. First, it's Michael Services. It's able to drive. So what this architecture ER that we've talked about. We've seen different conversations of beyond its thought spot everywhere, which allows us to take that spot. One. Our ability to for search for search data for auto analyzed the Monitor with that govern security in the background and being able to leverage that not only internally but externally and then being able to take thought spot modeling language for that analysts and that person who just really good at creating and let them create these models that it could be deployed anywhere very, very quickly and then taking advantage off the Cloud Data warehouse or the technology that you have and really give you accessibility the technology that you need as well as the data that you need. That's what you need to be able to administer, uh, to take analytics at scale. So what I'm gonna do now is I'm gonna turn it over to Cheryl and she's gonna talk about administration in thought spot. Cheryl, >>thank you very much Can take. Today. I'm going to show you how you can administrator and manage South Spot for your organization >>covering >>streaming topics, the user management >>data management and >>also user adoption and performance monitoring. Let's jump into the demo. >>I think the Southport Application The Admin Council provides all the core functions needed for system level administration. Let's start with user management and authentication. With the user tab. You can add or delete a user, or you can modify the setting for an existing user. For example, user name, password email. Or you can add the user toe a different group with the group's tab. You can add or delete group, or you can manage the group setting. For example, Privileges associated with all the group members, for example, can administrate a soft spot can share data with all users or can manage data this can manage data privilege is very important. It grants a user the privileges to add data source added table and worksheet, manage data for different organizations or use cases without being an at me. There is also a field called Default Pin Board. You can select a set of PIN board that will be shown toe all of the users in that group on their homepage in terms off authentication. Currently, we support three different methods local active directory and samel By default. Local authentication is enabled and you can also choose to have several integration with an external identity provider. Currently, we support actor Ping Identity, Seaside Minor or a T. F. S. The third method is integration with active directory. You can configure integration with L DAP through active directory, allowing you to authenticate users against an elder up server. Once the users and groups are added to the system, we can share pin board wisdom or they can search to ask and answer their own questions. To create a searchable data, we first need to connect to our data warehouses with embraced. You can directly query the data as it exists in the data warehouse without having to move or transfer the data. In this page, you can add a connection to any off the six supported data warehouses. Today we will be focusing on the administrative aspect off the data management. So I will close the tap here and we will be using the connections that are already being set up. Under the Data Objects tab, we can see all of the tables from the connections. Sometimes there are a lot of tables, and it may be overwhelming for the administrator to manage the data as a best practice. We recommend using stickers toe organize your data sets here, we're going to select the Salesforce sticker. This will refined a list off tables coming from Salesforce only. This helps with data, lineage and the traceability because worksheets are curated data that's based on those tables. Let's take a look at this worksheet. Here we can see the joints between tables that created a schema. Once the data analyst created the table and worksheet, the data is searchable by end users. Let's go to search first, let's select the data source here. We can see all of the data that we have been granted access to see Let's choose the Salesforce sticker and we will see all of the tables and work ship that's available to us as a data source. Let's choose this worksheet as a data source. Now we're ready to search the search Insight can be saved either into a PIN board or an answer. Okay, it's important to know that the sticker actually persist with PIN board and answers. So when the user logging, they will be able to see all of the content that's available to them. Let's go to the Admin Council and check out the User Adoption Pin board. The User Adoption Pin board contains essential information about your soft spot users and their adoption off the platform. Here, you can see daily active user, weekly, active user and monthly active user. Count that in the last 30 days you can also see the total count off the pin board and answers that saved in the system. Here, you can see that unique count off users. Now. You can also find out the top 10 users in the last 30 days. The top 10 PIN board consumers and top 10 ad hoc searchers here, you can see that trending off weekly, active users, daily, active users and hourly active users over time. You can also get information about popular pin boards and user actions in the last one month. Now let's zoom in into this chart. With this chart, you can see weekly active users and how they're using soft spot. In this example, you can see 60% of the time people are doing at Hawk search. If you would like to see what people are searching, you can do a simple drill down on quarry tax. Here we can find out the most popular credit tax that's being used is number off the opportunities. At last, I would like to show you assistant performance Tracking PIN board that's available to the ad means this PIN board contains essential information about your soft spot. Instance performance You this pimple. To understand the query, Leighton see user traffic, how users are interacting with soft spot, most frequently loaded tables and so on. The last component toe scowling hundreds of users, is a great on boarding experience. A new feature we call Search Assist helps automate on boarding while ensuring new users have the foundation. They need to be successful on Day one, when new users logging for the first time, they're presented with personalized sample searches that are specific to their data set. In this example, someone in a sales organization would see questions like What were sales by product? Type in 2020. From there are guided step by step process helps introduce new users with search ensuring a successful on boarding experience. The search assist. The coach is a customized in product Walk through that uses your own data and your own business vocabulary to take your business users from unfamiliar to near fluent in minutes. Instead of showing the entire end user experience today, I will focus on the set up and administration side off the search assist. Search Assist is easy to set up at worksheet level with flexible options for multiple guided lessons. Using preview template, we help you create multiple learning path based on department or based on your business. Users needs to set up a learning path. You're simply feeling the template with relevant search examples while previewing what the end user will see and then increase the complexity with each additional question toe. Help your users progress >>in summary. It is easy to administrator user management, data management, management and the user adoption at scale Using soft spot Admin Council Back to you, Kendrick. >>Thank you, Cheryl. That was great. Appreciate the demo there. It's awesome. It's real life data, real life software. You know what? Enclosing here? I want to talk a little bit about what we've seen out in the marketplace and some of them when we're talking through prospects and customers, what they talk a little bit about. Well, I'm not quite area either. My data is not ready or I've got I don't have a file data warehouse. That's this process. In this thinking on, we have examples and three different examples. We have a company that actually had never I hadn't even thought about analytics at scale. We come in, we talked to them in less than a week. They're able to move their data thought spot and ask questions of the billion rose in less than a week now. We've also had customers that are early adoption. They're sticking their toes in the water around the technology, so they have a lot of data warehouse and they put some data at it, and with 11 minute within 11 minutes, we were able to search on a billion rows of their data. Now they're adding more data to combine to, to be able to work with. And then we have customers that are more mature in their process. Uh, they put large volumes of data within nine minutes. We're asking questions of their data, their business users air understanding. What's going on? A second question we get sometimes is my data is not clean. We'll talk Spot is very, very good at finding that type of data. If you take, you start moving and becomes an inner door process, and we can help with that again. Within a week, we could take data, get it into your system, start asking business questions of that and be ready to go. You know, I'm gonna turn it back to you and thank you for your time. >>Kendrick and Carol thank you for joining us today and bringing all of that amazing inside for our audience at home. Let's do a couple of stretches and then join us in a few minutes for our last session of the track. Insides for all about how Canadian Tire is delivering Korean making business outcomes would certainly not in a I. So you're there