>> Narrator: TheCUBE's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (light airy music) >> Hi, everybody, welcome back to the Fira in Barcelona. My name is Dave Vellante. I'm here with my co-host Dave Nicholson. Lisa Martin is in the house. John Furrier is pounding the news from our Palo Alto studio. We are super excited to be talking about cloud at the edge, what that means. Charles Tsai is here. He's the Senior Director of product management at Dell Technologies and Peter Fetterolf is the Chief Technology Officer at ACG Business Analytics, a firm that goes deep into the TCO and the telco space, among other things. Gents, welcome to theCUBE. Thanks for coming on. Thank you. >> Good to be here. >> Yeah, good to be here. >> So I've been in search all week of the elusive next wave of monetization for the telcos. We know they make great money on connectivity, they're really good at that. But they're all talking about how they can't let this happen again. Meaning we can't let the over the top vendors yet again, basically steal our cookies. So we're going to not mess it up this time. We're going to win in the monetization. Charles, where are those monetization opportunities? Obviously at the edge, the telco cloud at the edge. What is that all about and where's the money? >> Well, Dave, I think from a Dell's perspective, what we want to be able to enable operators is a solution that enable them to roll out services much quicker, right? We know there's a lot of innovation around IoT, MEG and so on and so forth, but they continue to rely on traditional technology and way of operations is going to take them years to enable new services. So what Dell is doing is now, creating the entire vertical stack from the hardware through CAST and automation that enable them, not only to push out services very quickly, but operating them using cloud principles. >> So it's when you say the entire vertical stack, it's the integrated hardware components with like, for example, Red Hat on top- >> Right. >> Or a Wind River? >> That's correct. >> Okay, and then open API, so the developers can create workloads, I presume data companies. We just had a data conversation 'cause that was part of the original stack- >> That's correct. >> So through an open ecosystem, you can actually sort of recreate that value, correct? >> That's correct. >> Okay. >> So one thing Dell is doing, is we are offering an infrastructure block where we are taking over the overhead of certifying every release coming from the Red Hat or the Wind River of the world, right? We want telcos to spend their resources on what is going to generate them revenue. Not the overhead of creating this cloud stack. >> Dave, I remember when we went through this in the enterprise and you had companies like, you know, IBM with the AS400 and the mainframe saying it's easier to manage, which it was, but it's still, you know, it was subsumed by the open systems trend. >> Yeah, yeah. And I think that's an important thing to probe on, is this idea of what is, what exactly does it mean to be cloud at the edge in the telecom space? Because it's a much used term. >> Yeah. >> When we talk about cloud and edge, in sort of generalized IT, but what specifically does it mean? >> Yeah, so when we talk about telco cloud, first of all it's kind of different from what you're thinking about public cloud today. And there's a couple differences. One, if you look at the big hyperscaler public cloud today, they tend to be centralized in huge data centers. Okay, telco cloud, there are big data centers, but then there's also regional data centers. There are edge data centers, which are your typical like access central offices that have turned data centers, and then now even cell sites are becoming mini data centers. So it's distributed. I mean like you could have like, even in a country like say Germany, you'd have 30,000 soul sites, each one of them being a data center. So it's a very different model. Now the other thing I want to go back to the question of monetization, okay? So how do you do monetization? The only way to do that, is to be able to offer new services, like Charles said. How do you offer new services? You have to have an open ecosystem that's going to be very, very flexible. And if we look at where telcos are coming from today, they tend to be very inflexible 'cause they're all kind of single vendor solutions. And even as we've moved to virtualization, you know, if you look at packet core for instance, a lot of them are these vertical stacks of say a Nokia or Ericson or Huawei where you know, you can't really put any other vendors or any other solutions into that. So basically the idea is this kind of horizontal architecture, right? Where now across, not just my central data centers, but across my edge data centers, which would be traditionally my access COs, as well as my cell sites. I have an open environment. And we're kind of starting with, you know, packet core obviously with, and UPFs being distributed, but now open ran or virtual ran, where I can have CUs and DUs and I can split CUs, they could be at the soul site, they could be in edge data centers. But then moving forward, we're going to have like MEG, which are, you know, which are new kinds of services, you know, could be, you know, remote cars it could be gaming, it could be the Metaverse. And these are going to be a multi-vendor environment. So one of the things you need to do is you need to have you know, this cloud layer, and that's what Charles was talking about with the infrastructure blocks is helping the service providers do that, but they still own their infrastructure. >> Yeah, so it's still not clear to me how the service providers win that game but we can maybe come back to that because I want to dig into TCO a little bit. >> Sure. >> Because I have a lot of friends at Dell. I don't have a lot of friends at HPE. I've always been critical when they take an X86 server put a name on it that implies edge and they throw it over the fence to the edge, that's not going to work, okay? We're now seeing, you know we were just at the Dell booth yesterday, you did the booth crawl, which was awesome. Purpose-built servers for this environment. >> Charles: That's right. >> So there's two factors here that I want to explore in TCO. One is, how those next gen servers compare to the previous gen, especially in terms of power consumption but other factors and then how these sort of open ran, open ecosystem stacks compared to proprietary stacks. Peter, can you help us understand those? >> Yeah, sure. And Charles can comment on this as well. But I mean there, there's a couple areas. One is just moving the next generation. So especially on the Intel side, moving from Ice Lake to the Sapphire Rapids is a big deal, especially when it comes to the DU. And you know, with the radios, right? There's the radio unit, the RU, and then there's the DU the distributed unit, and the CU. The DU is really like part of the radio, but it's virtualized. When we moved from Ice lake to Sapphire Rapids, which is third generation intel to fourth generation intel, we're literally almost doubling the performance in the DU. And that's really important 'cause it means like almost half the number of servers and we're talking like 30, 40, 50,000 servers in some cases. So, you know, being able to divide that by two, that's really big, right? In terms of not only the the cost but all the TCO and the OpEx. Now another area that's really important, when I was talking moving from these vertical silos to the horizontal, the issue with the vertical silos is, you can't place any other workloads into those silos. So it's kind of inefficient, right? Whereas when we have the horizontal architecture, now you can place workloads wherever you want, which basically also means less servers but also more flexibility, more service agility. And then, you know, I think Charles can comment more, specifically on the XR8000, some things Dell's doing, 'cause it's really exciting relative to- >> Sure. >> What's happening in there. >> So, you know, when we start looking at putting compute at the edge, right? We recognize the first thing we have to do is understand the environment we are going into. So we spend with a lot of time with telcos going to the south side, going to the edge data center, looking at operation, how do the engineer today deal with maintenance replacement at those locations? Then based on understanding the operation constraints at those sites, we create innovation and take a traditional server, remodel it to make sure that we minimize the disruption to the operations, right? Just because we are helping them going from appliances to open compute, we do not want to disrupt what is have been a very efficient operation on the remote sites. So we created a lot of new ideas and develop them on general compute, where we believe we can save a lot of headache and disruptions and still provide the same level of availability, resiliency, and redundancy on an open compute platform. >> So when we talk about open, we don't mean generic? Fair? See what I mean? >> Open is more from the software workload perspective, right? A Dell server can run any type of workload that customer intend. >> But it's engineered for this? >> Environment. >> Environment. >> That's correct. >> And so what are some of the environmental issues that are dealt with in the telecom space that are different than the average data center? >> The most basic one, is in most of the traditional cell tower, they are deployed within cabinets instead of racks. So they are depth constraints that you just have no access to the rear of the chassis. So that means on a server, is everything you need to access, need to be in the front, nothing should be in the back. Then you need to consider how labor union come into play, right? There's a lot of constraint on who can go to a cell tower and touch power, who can go there and touch compute, right? So we minimize all that disruption through a modular design and make it very efficient. >> So when we took a look at XR8000, literally right here, sitting on the desk. >> Uh-huh. >> Took it apart, don't panic, just pulled out some sleds and things. >> Right, right. >> One of the interesting demonstrations was how it compared to the size of a shoe. Now apparently you hired someone at Dell specifically because they wear a size 14 shoe, (Charles laughs) so it was even more dramatic. >> That's right. >> But when you see it, and I would suggest that viewers go back and take a look at that segment, specifically on the hardware. You can see exactly what you just referenced. This idea that everything is accessible from the front. Yeah. >> So I want to dig in a couple things. So I want to push back a little bit on what you were saying about the horizontal 'cause there's the benefit, if you've got the horizontal infrastructure, you can run a lot more workloads. But I compare it to the enterprise 'cause I, that was the argument, I've made that argument with converged infrastructure versus say an Oracle vertical stack, but it turned out that actually Oracle ran Oracle better, okay? Is there an analog in telco or is this new open architecture going to be able to not only service the wide range of emerging apps but also be as resilient as the proprietary infrastructure? >> Yeah and you know, before I answer that, I also want to say that we've been writing a number of white papers. So we have actually three white papers we've just done with Dell looking at infrastructure blocks and looking at vertical versus horizontal and also looking at moving from the previous generation hardware to the next generation hardware. So all those details, you can find the white papers, and you can find them either in the Dell website or at the ACG research website >> ACGresearch.com? >> ACG research. Yeah, if you just search ACG research, you'll find- >> Yeah. >> Lots of white papers on TCO. So you know, what I want to say, relative to the vertical versus horizontal. Yeah, obviously in the vertical side, some of those things will run well, I mean it won't have issues. However, that being said, as we move to cloud native, you know, it's very high performance, okay? In terms of the stack, whether it be a Red Hat or a VMware or other cloud layers, that's really become much more mature. It now it's all CNF base, which is really containerized, very high performance. And so I don't think really performance is an issue. However, my feeling is that, if you want to offer new services and generate new revenue, you're not going to do it in vertical stacks, period. You're going to be able to do a packet core, you'll be able to do a ran over here. But now what if I want to offer a gaming service? What if I want to do metaverse? What if I want to do, you have to have an environment that's a multi-vendor environment that supports an ecosystem. Even in the RAN, when we look at the RIC, and the xApps and the rApps, these are multi-vendor environments that's going to create a lot of flexibility and you can't do that if you're restricted to, I can only have one vendor running on this hardware. >> Yeah, we're seeing these vendors work together and create RICs. That's obviously a key point, but what I'm hearing is that there may be trade offs, but the incremental value is going to overwhelm that. Second question I have, Peter is, TCO, I've been hearing a lot about 30%, you know, where's that 30% come from? Is it Op, is it from an OpEx standpoint? Is it labor, is it power? Is it, you mentioned, you know, cutting the number of servers in half. If I can unpack the granularity of that TCO, where's the benefit coming from? >> Yeah, the answer is yes. (Peter and Charles laugh) >> Okay, we'll do. >> Yeah, so- >> One side that, in terms of, where is the big bang for the bucks? >> So I mean, so you really need to look at the white paper to see details, but definitely power, definitely labor, definitely reducing the number of servers, you know, reducing the CapEx. The other thing is, is as you move to this really next generation horizontal telco cloud, there's the whole automation and orchestration, that is a key component as well. And it's enabled by what Dell is doing. It's enabled by the, because the thing is you're not going to have end-to-end automation if you have all this legacy stuff there or if you have these vertical stacks where you can't integrate. I mean you can automate that part and then you have separate automation here, you separate. you need to have integrated automation and orchestration across the whole thing. >> One other point I would add also, right, on the hardware perspective, right? With the customized hardware, what we allow operator to do is, take out the existing appliance and push a edge optimized server without reworking the entire infrastructure. There is a significant saving where you don't have to rethink about what is my power infrastructure, right? What is my security infrastructure? The server is designed to leverage the existing, what is already there. >> How should telco, Charles, plan for this transformation? Are there specific best practices that you would recommend in terms of the operational model? >> Great question. I think first thing is do an inventory of what you have. Understand what your constraints are and then come to Dell, we will love to consult with you, based on our experience on the best practices. We know how to minimize additional changes. We know how to help your support engineer, understand how to shift appliance based operation to a cloud-based operation. >> Is that a service you offer? Is that a pre-sales freebie? What is maybe both? >> It's both. >> Yeah. >> It's both. >> Yeah. >> Guys- >> Just really quickly. >> We're going to wrap. >> The, yeah. Dave loves the TCO discussion. I'm always thinking in terms of, well how do you measure TCO when you're comparing something where you can't do something to an environment where you're going to be able to do something new? And I know that that's always the challenge in any kind of emerging market where things are changing, any? >> Well, I mean we also look at, not only TCO, but we look at overall business case. So there's basically service at GLD and revenue and then there's faster time to revenues. Well, and actually ACG, we actually have a platform called the BAE or Business Analytics Engine that's a very sophisticated simulation cloud-based platform, where we can actually look at revenue month by month. And we look at what's the impact of accelerating revenue by three months. By four months. >> So you're looking into- >> By six months- >> So you're forward looking. You're just not consistently- >> So we're not just looking at TCO, we're looking at the overall business case benefit. >> Yeah, exactly right. There's the TCO, which is the hard dollars. >> Right. >> CFO wants to see that, he or she needs to see that. But you got to, you can convince that individual, that there's a business case around it. >> Peter: Yeah. >> And then you're going to sign up for that number. >> Peter: Yeah. >> And they're going to be held to it. That's the story the world wants. >> At the end of the day, telcos have to be offered new services 'cause look at all the money that's been spent. >> Dave: Yeah, that's right. >> On investment on 5G and everything else. >> 0.5 trillion over the next seven years. All right, guys, we got to go. Sorry to cut you off. >> Okay, thank you very much. >> But we're wall to wall here. All right, thanks so much for coming on. >> Dave: Fantastic. >> All right, Dave Vellante, for Dave Nicholson. Lisa Martin's in the house. John Furrier in Palo Alto Studios. Keep it right there. MWC 23 live from the Fira in Barcelona. (light airy music)

>>And good afternoon. Welcome back here on the Cub as to continue our coverage at aws Reinvent 22, win the Venetian here in Las Vegas, day two, it's Wednesday. Thanks. Still rolling. Quite a along. We have another segment for you as part of the Global Startup program, which is under the AWS Startup Showcase. I'm joined now by Vink at Viera, who is the CEO and co-founder of R Set. And good to see you, >>Sir. Thanks for having me here. Yeah, >>No, a real pleasure. Looking forward to it. So first off, for some of, for yours who might not be familiar with Roxette, I know you've been on the cube a little bit, so you're, you're an alum, but, but why don't you set the stage a little bit for Rock set and you know, where you're engaged with in terms of, with aws? >>Definitely. Rock Set is a realtime analytics database that is built for the cloud. You know, we make realtime applications possible in the cloud. You know, realtime applications need high concurrency, low latency query processing data needs to be fresh, your analytic needs to be fast. And, you know, we built on aws and that's why we are here. We are very, very proud partners of aws. We are in the AWS Accelerate program, and also we are in the startup program of aws. We are strategic ISV partner. And so yeah, we make real time analytics possible without all the cost and complexity barriers that are usually associated with it. And very, very happy to be part of this movement from batch to real time that is happening in the world. >>Right. Which is certainly an exciting trend. Right. I know great news for you, you made news yesterday, had an announcement involved with the intel with aws, who wants to share some of that >>With us too? Definitely. So, you know, one, one question that I always ask people is like, you know, if you go perspective that I share is like, if you go ask a hundred people, do you want fast analytics on fresh data or slow analytics on stale data? You know, a hundred out of a hundred would say fast and fresh, right? Sure. So then the question is, why hasn't this happened already? Why is this still a new trend that is emerging as opposed to something that everybody's taking for granted? It really comes down to compute efficiency, right? I think, you know, at the end of the day, real time analytics was always in using, you know, technologies that are, let's say 10 years ago using let's say processors that were available 10 years ago to, you know, three cloud, you know, days. There was a lot of complexity barriers associated with realtime analytics and also a lot of cost and, and performance barriers associated with it. >>And so Rox said from the, you know, from the very beginning, has been obsessing about building the most compute efficient realtime database in the world. And, you know, AWS on one hand, you know, allows us to make a consumption based pricing model. So you only pay for what you use. Sure. And that shatters all the cost barriers. But in terms of computer efficiency, what we announced yesterday is the Intel's third generation Zon scalable processors, it's code named Intel Ice Lake. When we port it over Rock said to that architecture, taking advantage of some of the instructions sets that Intel has, we got an 84% performance boost, 84, 84, 84. >>It's, it's incredible, right? >>It's, it's an incredible charts, it's an incredible milestone. It reduces the barrier even more in terms of cost and, you know, and, and pushes the efficiency and sets a, a really new record for how efficient realtime, you know, data processing can be in the cloud. And, and it's very, very exciting news. And so we used to benchmark ourselves against some of our other, you know, realtime, you know, did up providers and we were already faster and now we've set a, a much, much higher bar for other people to follow. >>Yep. And, and so what is, or what was it about real time that, that, you know, was such a barrier because, and now you've got the speed of, of course, obviously, and maybe that's what it was, but I think cost is probably part of that too, right? That's all part of that equation. I mean, real time, so elusive. >>Yeah. So real time has this inherent pattern that your data never stops coming. And when your data never stops coming, and you can now actually do analytics on that. Now, initially people start with saying, oh, I just want a real time dashboard. And then very quickly they realize, well, the dashboard is actually in real time. I'm not gonna be staring at the 24 7. Can you tap on my shoulder when something is off, something needs to be looked at. So in which case you're constantly also asking the question, is everything okay? Is everything all right? Do I need to, is is that something that I need to be, you know, double clicking on and, and following up on? So essentially very quickly in real time analytics, what happens is your queries never stop. The questions that you're asking on your data never stops. And it's often a program asking the question to detect anomalies and things like that. >>And your data never stops coming. And so compute is running 24 7. If you look at traditional data warehouses and data lakes, they're not really optimized for these kinds of workloads. They're optimized to store massive volumes of data and in a storage efficient format. And when an analyst comes and asks a question to generate a report, you can spin up a whole bunch of compute, generate the report and tear it all down when you're done. Well, that is not compute running 24 7 to continuously, you know, you know, keep ingesting the data or continuously keep answering questions. So the compute efficiency that is needed is, is much, much, much higher. Right? And that is why, you know, Rox was born. So from the very beginning, we're only built, you know, for these use cases, we have a, an extremely powerful SQL engine that can give you full feature SQL analytics in a very, very compute efficient way in the cloud. >>Right. So, so let's talk about the leap that you've made, say in the last two years and, and, and what's been the spur of that? What has been allowed you to, to create this, you know, obviously a, a different kind of an array for your customers from which to choose, but, but what's been the spark you think >>We touched upon this a little earlier, right? This spark is really, you know, the world going from batch to real time. So if you look at mainstream adoption of technologies like Apache, Kafka and Confluent doing a really good job at that. In, in, in growing that community and, and use cases, now businesses are now acquiring business data, really important business data in real time. Now they want to operationalize it, right? So, you know, extract based static reports and bi you know, business intelligence is getting replaced in all modern enterprises with what we call operational intelligence, right? Don't tell me what happened last quarter and how to plan this quarter better. Tell me what's happening today, what's happening right now. And it's, it's your business operations using data to make day to day decisions better that either grows your top line, compresses your bottom line, eliminates risk that are inherently creeping up in your business. >>Sure. You know, eliminate potential churn from a customer or fraud, you know, deduction and, and getting on top of, you know, that, you know, a minute into this, into, into an outage as opposed to an hour into the outage. Right? And so essentially I think businesses are now realizing that operational intelligence and operational analytics really, you know, allows them to leverage data and especially real time data to make their, you know, to grow their businesses faster and more efficiently. And especially in this kind of macro environment that is, you know, more important to have better unit economics in your business than ever before. Sure. And so that is really, I think that is the real market movement happening. And, and we are here to just serve that market. We are making it much, much easier for companies that have already adopted, you know, streaming technologies like Kafka and, and, and knows Canis MSK and all these technologies. Now businesses are acquiring these data in real time now. They can also get realtime analytics on the other end of it. Sure. >>You know, you just touched on this and, and I'd like to hear your thoughts about this, about, about the economic environment because it does drive decisions, right? And it does motivate people to look for efficiencies and maybe costs, you know, right. Cutting costs. What are you seeing right now in terms of that, that kind of looming influence, right? That the economy can have in terms of driving decisions about where investments are being made and what expectations are in terms of delivering value, more value for the buck? >>Exactly. I think we see across the board, all of our customers come back and tell us, we don't want to manage data infrastructure and we don't want to do kind of DIY open source clusters. We don't wanna manage and scale and build giant data ops and DevOps teams to manage that, because that is not really, you know, in their business. You know, we have car rental companies want to be better at car rentals, we want airlines to be a better airline, and they don't, don't want their, you know, a massive investment in DevOps and data ops, which is not really their core business. And they really want to leverage, you know, you know, fully managed and, you know, cloud offerings like Rock said, you know, built on aws, massively scalable in the cloud with zero operational overhead, very, very easy to get started and scale. >>And so that completely removes all the operational overhead. And so they can invest the resources they have, the manpower, they have, the calories that they have on actually growing their businesses because that is what really gonna allow them to have better unit economics, right? So everybody that is on my payroll is helping me grow my top line or shrink my bottom line, eliminate risk in my business and, and, and, and churn and, and fraud and other, and eliminate all those risks that are inherent in my business. So, so that is where I think a lot of the investments going. So gone are the days where, you know, you're gonna have these in like five to 10% team managing a very hard to operate, you know, open source data management clusters on EC two nodes in, in AWS and, and kind of DIYing it their way because those 10 people, you know, if all they do is just operational maintenance of infrastructure, which is a means to an end, you're way better off, you know, using a cloud, you know, a bond in the cloud built for the cloud solution like rock and eliminate all that cost and, and replace that with an operationally much, much simpler, you know, system to op, you know, to to work with such as, such as rock. >>So that is really the big trend that we are seeing why, you know, not only real time is going more and more mainstream cloud native solutions or the real future even when it comes to real time because the complexity barrier needs to be shattered and only cloud native solutions can actually, >>You get the two Cs cost and complexity, right. That you, you need to address. Exactly. Yeah, for sure. You know, what is it about building trust with your, with your clients, with your partners? Because you, you're talking about this cloud environment that, that everyone is talking about, right? Not everyone's made that commitment. There are still some foot draggers out there. How are you going about establishing confidence and establishing trust and, and, and providing them with really concrete examples of the values and the benefits that you can provide, you know, with, with these opportunities? >>So, you know, I grew up, so there's a few ways to to, to answer this question. I'll, I'll, I'll come, I'll cover all the angles. So in, in order to establish trust, you have to create value. They, you know, your customer has to see that with you. They were able to solve the problem faster, better, cheaper, and they're able to, you know, have a, the business impact they were looking for, which is why they started the project in the first place. And so establishing that and proving that, I think there's no equivalence to that. And, you know, I grew up at, at, you know, at Facebook back in the day, you know, I was managing online data infrastructure, okay. For Facebook from 2007 and 2015. And internally we always had this kind of culture of all the product teams building on top of the infrastructure that my team was responsible for. >>And so they were not ever, there was never a, a customer vendor relationship internally within Facebook that we're all like, we're all part of the same team. We're partnering here to have you, you know, to help you have a successful product launch. There's a very similar DNA that, that exists in Rock said, when our customers work with us and they come to us and we are there to make them successful, our consumption based pricing model also forces us to say they're not gonna really use Rock said and consume more. I mean, we don't make money until they consume, right? And so their success is very much integral part of our, our success. And so that I think is one really important angle on, you know, give us a shot, come and do an evaluation, and we will work with you to build the most efficient way to solve your problem. >>And then when you succeed, we succeed. So that I think is a very important aspect. The second one is AWS partnership. You know, we are an ISV partner, you know, AWS a lot of the time. That really helps us establish trust. And a lot of the time, one of the, the, the people that they look up to, when a customer comes in saying, Hey, what is, who is Rock? Said? You know, who are your friends? Yeah. Who are your friends? And then, you know, and then the AWS will go like, oh, you know, we'll tell you, you know, all these other successful case studies that R has, you know, you know, built up on, you know, the world's largest insurance provider, Europe's largest insurance provider. We have customers like, you know, JetBlue Airlines to Klarna, which is a big bator company. And so, so all these case studies help and, and, and, and platform and partners like AWS helps us, helps you amplify that, that, you know, and, and, and, and, and give more credibility. And last but not least, compliance matters. You know, being Soto type two compliant is, is a really important part of establishing trust. We are hip hop compliant now so that, you know, we can, you know, pi I phi data handling that. And so I think that will continue to be a part, a big part of our focus in improving the security, you know, functionality and, and capabilities that R set has in the cloud, and also compliance and, and the set of com, you know, you know, standards that we are gonna be compliant against. >>Well, I'm glad you hit on the AWS too, cause I did wanna bring that up. I, I appreciate that and I know they appreciate the relationship as well. Thanks for the time here. It's been a pleasure. Awesome. Learning about Rockette and what you're up to. Thank you. >>You bet. >>It's a pleasure. Thank you. Vi ka. All right. You are watching the cube coverage here at AWS Reinvent 22. And on the cube, of course, the leader, the leader in high tech coverage.

>>This past May, The Cube in collaboration with Influx data shared with you the latest innovations in Time series databases. We talked at length about why a purpose built time series database for many use cases, was a superior alternative to general purpose databases trying to do the same thing. Now, you may, you may remember the time series data is any data that's stamped in time, and if it's stamped, it can be analyzed historically. And when we introduced the concept to the community, we talked about how in theory, those time slices could be taken, you know, every hour, every minute, every second, you know, down to the millisecond and how the world was moving toward realtime or near realtime data analysis to support physical infrastructure like sensors and other devices and IOT equipment. A time series databases have had to evolve to efficiently support realtime data in emerging use cases in iot T and other use cases. >>And to do that, new architectural innovations have to be brought to bear. As is often the case, open source software is the linchpin to those innovations. Hello and welcome to Evolving Influx DB into the smart Data platform, made possible by influx data and produced by the Cube. My name is Dave Valante and I'll be your host today. Now in this program we're going to dig pretty deep into what's happening with Time series data generally, and specifically how Influx DB is evolving to support new workloads and demands and data, and specifically around data analytics use cases in real time. Now, first we're gonna hear from Brian Gilmore, who is the director of IOT and emerging technologies at Influx Data. And we're gonna talk about the continued evolution of Influx DB and the new capabilities enabled by open source generally and specific tools. And in this program you're gonna hear a lot about things like Rust, implementation of Apache Arrow, the use of par k and tooling such as data fusion, which powering a new engine for Influx db. >>Now, these innovations, they evolve the idea of time series analysis by dramatically increasing the granularity of time series data by compressing the historical time slices, if you will, from, for example, minutes down to milliseconds. And at the same time, enabling real time analytics with an architecture that can process data much faster and much more efficiently. Now, after Brian, we're gonna hear from Anna East Dos Georgio, who is a developer advocate at In Flux Data. And we're gonna get into the why of these open source capabilities and how they contribute to the evolution of the Influx DB platform. And then we're gonna close the program with Tim Yokum, he's the director of engineering at Influx Data, and he's gonna explain how the Influx DB community actually evolved the data engine in mid-flight and which decisions went into the innovations that are coming to the market. Thank you for being here. We hope you enjoy the program. Let's get started. Okay, we're kicking things off with Brian Gilmore. He's the director of i t and emerging Technology at Influx State of Bryan. Welcome to the program. Thanks for coming on. >>Thanks Dave. Great to be here. I appreciate the time. >>Hey, explain why Influx db, you know, needs a new engine. Was there something wrong with the current engine? What's going on there? >>No, no, not at all. I mean, I think it's, for us, it's been about staying ahead of the market. I think, you know, if we think about what our customers are coming to us sort of with now, you know, related to requests like sql, you know, query support, things like that, we have to figure out a way to, to execute those for them in a way that will scale long term. And then we also, we wanna make sure we're innovating, we're sort of staying ahead of the market as well and sort of anticipating those future needs. So, you know, this is really a, a transparent change for our customers. I mean, I think we'll be adding new capabilities over time that sort of leverage this new engine, but you know, initially the customers who are using us are gonna see just great improvements in performance, you know, especially those that are working at the top end of the, of the workload scale, you know, the massive data volumes and things like that. >>Yeah, and we're gonna get into that today and the architecture and the like, but what was the catalyst for the enhancements? I mean, when and how did this all come about? >>Well, I mean, like three years ago we were primarily on premises, right? I mean, I think we had our open source, we had an enterprise product, you know, and, and sort of shifting that technology, especially the open source code base to a service basis where we were hosting it through, you know, multiple cloud providers. That was, that was, that was a long journey I guess, you know, phase one was, you know, we wanted to host enterprise for our customers, so we sort of created a service that we just managed and ran our enterprise product for them. You know, phase two of this cloud effort was to, to optimize for like multi-tenant, multi-cloud, be able to, to host it in a truly like sass manner where we could use, you know, some type of customer activity or consumption as the, the pricing vector, you know, And, and that was sort of the birth of the, of the real first influx DB cloud, you know, which has been really successful. >>We've seen, I think like 60,000 people sign up and we've got tons and tons of, of both enterprises as well as like new companies, developers, and of course a lot of home hobbyists and enthusiasts who are using out on a, on a daily basis, you know, and having that sort of big pool of, of very diverse and very customers to chat with as they're using the product, as they're giving us feedback, et cetera, has has, you know, pointed us in a really good direction in terms of making sure we're continuously improving that and then also making these big leaps as we're doing with this, with this new engine. >>Right. So you've called it a transparent change for customers, so I'm presuming it's non-disruptive, but I really wanna understand how much of a pivot this is and what, what does it take to make that shift from, you know, time series, you know, specialist to real time analytics and being able to support both? >>Yeah, I mean, it's much more of an evolution, I think, than like a shift or a pivot. You know, time series data is always gonna be fundamental and sort of the basis of the solutions that we offer our customers, and then also the ones that they're building on the sort of raw APIs of our platform themselves. You know, the time series market is one that we've worked diligently to lead. I mean, I think when it comes to like metrics, especially like sensor data and app and infrastructure metrics, if we're being honest though, I think our, our user base is well aware that the way we were architected was much more towards those sort of like backwards looking historical type analytics, which are key for troubleshooting and making sure you don't, you know, run into the same problem twice. But, you know, we had to ask ourselves like, what can we do to like better handle those queries from a performance and a, and a, you know, a time to response on the queries, and can we get that to the point where the results sets are coming back so quickly from the time of query that we can like limit that window down to minutes and then seconds. >>And now with this new engine, we're really starting to talk about a query window that could be like returning results in, in, you know, milliseconds of time since it hit the, the, the ingest queue. And that's, that's really getting to the point where as your data is available, you can use it and you can query it, you can visualize it, and you can do all those sort of magical things with it, you know? And I think getting all of that to a place where we're saying like, yes to the customer on, you know, all of the, the real time queries, the, the multiple language query support, but, you know, it was hard, but we're now at a spot where we can start introducing that to, you know, a a limited number of customers, strategic customers and strategic availability zones to start. But you know, everybody over time. >>So you're basically going from what happened to in, you can still do that obviously, but to what's happening now in the moment? >>Yeah, yeah. I mean if you think about time, it's always sort of past, right? I mean, like in the moment right now, whether you're talking about like a millisecond ago or a minute ago, you know, that's, that's pretty much right now, I think for most people, especially in these use cases where you have other sort of components of latency induced by the, by the underlying data collection, the architecture, the infrastructure, the, you know, the, the devices and you know, the sort of highly distributed nature of all of this. So yeah, I mean, getting, getting a customer or a user to be able to use the data as soon as it is available is what we're after here. >>I always thought, you know, real, I always thought of real time as before you lose the customer, but now in this context, maybe it's before the machine blows up. >>Yeah, it's, it's, I mean it is operationally or operational real time is different, you know, and that's one of the things that really triggered us to know that we were, we were heading in the right direction, is just how many sort of operational customers we have. You know, everything from like aerospace and defense. We've got companies monitoring satellites, we've got tons of industrial users, users using us as a processes storing on the plant floor, you know, and, and if we can satisfy their sort of demands for like real time historical perspective, that's awesome. I think what we're gonna do here is we're gonna start to like edge into the real time that they're used to in terms of, you know, the millisecond response times that they expect of their control systems, certainly not their, their historians and databases. >>I, is this available, these innovations to influx DB cloud customers only who can access this capability? >>Yeah. I mean commercially and today, yes. You know, I think we want to emphasize that's a, for now our goal is to get our latest and greatest and our best to everybody over time. Of course. You know, one of the things we had to do here was like we double down on sort of our, our commitment to open source and availability. So like anybody today can take a look at the, the libraries in on our GitHub and, you know, can ex inspect it and even can try to, you know, implement or execute some of it themselves in their own infrastructure. You know, we are, we're committed to bringing our sort of latest and greatest to our cloud customers first for a couple of reasons. Number one, you know, there are big workloads and they have high expectations of us. I think number two, it also gives us the opportunity to monitor a little bit more closely how it's working, how they're using it, like how the system itself is performing. >>And so just, you know, being careful, maybe a little cautious in terms of, of, of how big we go with this right away, just sort of both limits, you know, the risk of, of, you know, any issues that can come with new software rollouts. We haven't seen anything so far, but also it does give us the opportunity to have like meaningful conversations with a small group of users who are using the products, but once we get through that and they give us two thumbs up on it, it'll be like, open the gates and let everybody in. It's gonna be exciting time for the whole ecosystem. >>Yeah, that makes a lot of sense. And you can do some experimentation and, you know, using the cloud resources. Let's dig into some of the architectural and technical innovations that are gonna help deliver on this vision. What, what should we know there? >>Well, I mean, I think foundationally we built the, the new core on Rust. You know, this is a new very sort of popular systems language, you know, it's extremely efficient, but it's also built for speed and memory safety, which goes back to that us being able to like deliver it in a way that is, you know, something we can inspect very closely, but then also rely on the fact that it's going to behave well. And if it does find error conditions, I mean we, we've loved working with Go and, you know, a lot of our libraries will continue to, to be sort of implemented in Go, but you know, when it came to this particular new engine, you know, that power performance and stability rust was critical. On top of that, like, we've also integrated Apache Arrow and Apache Parque for persistence. I think for anybody who's really familiar with the nuts and bolts of our backend and our TSI and our, our time series merged Trees, this is a big break from that, you know, arrow on the sort of in MI side and then Par K in the on disk side. >>It, it allows us to, to present, you know, a unified set of APIs for those really fast real time inquiries that we talked about, as well as for very large, you know, historical sort of bulk data archives in that PARQUE format, which is also cool because there's an entire ecosystem sort of popping up around Parque in terms of the machine learning community, you know, and getting that all to work, we had to glue it together with aero flight. That's sort of what we're using as our, our RPC component. You know, it handles the orchestration and the, the transportation of the Coer data. Now we're moving to like a true Coer database model for this, this version of the engine, you know, and it removes a lot of overhead for us in terms of having to manage all that serialization, the deserialization, and, you know, to that again, like blurring that line between real time and historical data. It's, you know, it's, it's highly optimized for both streaming micro batch and then batches, but true streaming as well. >>Yeah. Again, I mean, it's funny you mentioned Rust. It is, it's been around for a long time, but it's popularity is, is you know, really starting to hit that steep part of the S-curve. And, and we're gonna dig into to more of that, but give us any, is there anything else that we should know about Bryan? Give us the last word? >>Well, I mean, I think first I'd like everybody sort of watching just to like take a look at what we're offering in terms of early access in beta programs. I mean, if, if, if you wanna participate or if you wanna work sort of in terms of early access with the, with the new engine, please reach out to the team. I'm sure you know, there's a lot of communications going out and you know, it'll be highly featured on our, our website, you know, but reach out to the team, believe it or not, like we have a lot more going on than just the new engine. And so there are also other programs, things we're, we're offering to customers in terms of the user interface, data collection and things like that. And, you know, if you're a customer of ours and you have a sales team, a commercial team that you work with, you can reach out to them and see what you can get access to because we can flip a lot of stuff on, especially in cloud through feature flags. >>But if there's something new that you wanna try out, we'd just love to hear from you. And then, you know, our goal would be that as we give you access to all of these new cool features that, you know, you would give us continuous feedback on these products and services, not only like what you need today, but then what you'll need tomorrow to, to sort of build the next versions of your business. Because you know, the whole database, the ecosystem as it expands out into to, you know, this vertically oriented stack of cloud services and enterprise databases and edge databases, you know, it's gonna be what we all make it together, not just, you know, those of us who were employed by Influx db. And then finally I would just say please, like watch in ICE in Tim's sessions, like these are two of our best and brightest, They're totally brilliant, completely pragmatic, and they are most of all customer obsessed, which is amazing. And there's no better takes, like honestly on the, the sort of technical details of this, then there's, especially when it comes to like the value that these investments will, will bring to our customers and our communities. So encourage you to, to, you know, pay more attention to them than you did to me, for sure. >>Brian Gilmore, great stuff. Really appreciate your time. Thank you. >>Yeah, thanks Dave. It was awesome. Look forward to it. >>Yeah, me too. Looking forward to see how the, the community actually applies these new innovations and goes, goes beyond just the historical into the real time really hot area. As Brian said in a moment, I'll be right back with Anna East dos Georgio to dig into the critical aspects of key open source components of the Influx DB engine, including Rust, Arrow, Parque, data fusion. Keep it right there. You don't wanna miss this >>Time series Data is everywhere. The number of sensors, systems and applications generating time series data increases every day. All these data sources producing so much data can cause analysis paralysis. Influx DB is an entire platform designed with everything you need to quickly build applications that generate value from time series data influx. DB Cloud is a serverless solution, which means you don't need to buy or manage your own servers. There's no need to worry about provisioning because you only pay for what you use. Influx DB Cloud is fully managed so you get the newest features and enhancements as they're added to the platform's code base. It also means you can spend time building solutions and delivering value to your users instead of wasting time and effort managing something else. Influx TVB Cloud offers a range of security features to protect your data, multiple layers of redundancy ensure you don't lose any data access controls ensure that only the people who should see your data can see it. >>And encryption protects your data at rest and in transit between any of our regions or cloud providers. InfluxDB uses a single API across the entire platform suite so you can build on open source, deploy to the cloud and then then easily query data in the cloud at the edge or on prem using the same scripts. And InfluxDB is schemaless automatically adjusting to changes in the shape of your data without requiring changes in your application. Logic. InfluxDB Cloud is production ready from day one. All it needs is your data and your imagination. Get started today@influxdata.com slash cloud. >>Okay, we're back. I'm Dave Valante with a Cube and you're watching evolving Influx DB into the smart data platform made possible by influx data. Anna ETOs Georgio is here, she's a developer advocate for influx data and we're gonna dig into the rationale and value contribution behind several open source technologies that Influx DB is leveraging to increase the granularity of time series analysis analysis and bring the world of data into real-time analytics and is welcome to the program. Thanks for coming on. >>Hi, thank you so much. It's a pleasure to be here. >>Oh, you're very welcome. Okay, so IX is being touted as this next gen open source core for Influx db. And my understanding is that it leverages in memory of course for speed. It's a kilo store, so it gives you a compression efficiency, it's gonna give you faster query speeds, you store files and object storage, so you got very cost effective approach. Are these the salient points on the platform? I know there are probably dozens of other features, but what are the high level value points that people should understand? >>Sure, that's a great question. So some of the main requirements that IOx is trying to achieve and some of the most impressive ones to me, the first one is that it aims to have no limits on cardinality and also allow you to write any kind of event data that you want, whether that's live tag or a field. It also wants to deliver the best in class performance on analytics queries. In addition to our already well served metrics queries, we also wanna have operator control over memory usage. So you should be able to define how much memory is used for buffering caching and query processing. Some other really important parts is the ability to have bulk data export and import super useful. Also broader ecosystem compatibility where possible we aim to use and embrace emerging standards in the data analytics ecosystem and have compatibility with things like sql, Python, and maybe even pandas in the future. >>Okay, so lot there. Now we talked to Brian about how you're using Rust and which is not a new programming language and of course we had some drama around Rust during the pandemic with the Mozilla layoffs, but the formation of the Rust Foundation really addressed any of those concerns. You got big guns like Amazon and Google and Microsoft throwing their collective weights behind it. It's really, the adoption is really starting to get steep on the S-curve. So lots of platforms, lots of adoption with rust, but why rust as an alternative to say c plus plus for example? >>Sure, that's a great question. So Russ was chosen because of his exceptional performance and reliability. So while Russ is synt tactically similar to c plus plus and it has similar performance, it also compiles to a native code like c plus plus. But unlike c plus plus, it also has much better memory safety. So memory safety is protection against bugs or security vulnerabilities that lead to excessive memory usage or memory leaks. And rust achieves this memory safety due to its like innovative type system. Additionally, it doesn't allow for dangling pointers. And dangling pointers are the main classes of errors that lead to exploitable security vulnerabilities in languages like c plus plus. So Russ like helps meet that requirement of having no limits on ality, for example, because it's, we're also using the Russ implementation of Apache Arrow and this control over memory and also Russ Russ's packaging system called crates IO offers everything that you need out of the box to have features like AY and a weight to fix race conditions, to protection against buffering overflows and to ensure thread safe async cashing structures as well. So essentially it's just like has all the control, all the fine grain control, you need to take advantage of memory and all your resources as well as possible so that you can handle those really, really high ity use cases. >>Yeah, and the more I learn about the, the new engine and, and the platform IOCs et cetera, you know, you, you see things like, you know, the old days not even to even today you do a lot of garbage collection in these, in these systems and there's an inverse, you know, impact relative to performance. So it looks like you really, you know, the community is modernizing the platform, but I wanna talk about Apache Arrow for a moment. It it's designed to address the constraints that are associated with analyzing large data sets. We, we know that, but please explain why, what, what is Arrow and and what does it bring to Influx db? >>Sure, yeah. So Arrow is a, a framework for defining in memory calmer data. And so much of the efficiency and performance of IOx comes from taking advantage of calmer data structures. And I will, if you don't mind, take a moment to kind of of illustrate why column or data structures are so valuable. Let's pretend that we are gathering field data about the temperature in our room and also maybe the temperature of our stove. And in our table we have those two temperature values as well as maybe a measurement value, timestamp value, maybe some other tag values that describe what room and what house, et cetera we're getting this data from. And so you can picture this table where we have like two rows with the two temperature values for both our room and the stove. Well usually our room temperature is regulated so those values don't change very often. >>So when you have calm oriented st calm oriented storage, essentially you take each row, each column and group it together. And so if that's the case and you're just taking temperature values from the room and a lot of those temperature values are the same, then you'll, you might be able to imagine how equal values will then enable each other and when they neighbor each other in the storage format, this provides a really perfect opportunity for cheap compression. And then this cheap compression enables high cardinality use cases. It also enables for faster scan rates. So if you wanna define like the men and max value of the temperature in the room across a thousand different points, you only have to get those a thousand different points in order to answer that question and you have those immediately available to you. But let's contrast this with a row oriented storage solution instead so that we can understand better the benefits of calmer oriented storage. >>So if you had a row oriented storage, you'd first have to look at every field like the temperature in, in the room and the temperature of the stove. You'd have to go across every tag value that maybe describes where the room is located or what model the stove is. And every timestamp you'd then have to pluck out that one temperature value that you want at that one time stamp and do that for every single row. So you're scanning across a ton more data and that's why Rowe Oriented doesn't provide the same efficiency as calmer and Apache Arrow is in memory calmer data, commoner data fit framework. So that's where a lot of the advantages come >>From. Okay. So you basically described like a traditional database, a row approach, but I've seen like a lot of traditional database say, okay, now we've got, we can handle colo format versus what you're talking about is really, you know, kind of native i, is it not as effective? Is the, is the foreman not as effective because it's largely a, a bolt on? Can you, can you like elucidate on that front? >>Yeah, it's, it's not as effective because you have more expensive compression and because you can't scan across the values as quickly. And so those are, that's pretty much the main reasons why, why RO row oriented storage isn't as efficient as calm, calmer oriented storage. Yeah. >>Got it. So let's talk about Arrow Data Fusion. What is data fusion? I know it's written in Rust, but what does it bring to the table here? >>Sure. So it's an extensible query execution framework and it uses Arrow as it's in memory format. So the way that it helps in influx DB IOCs is that okay, it's great if you can write unlimited amount of cardinality into influx Cbis, but if you don't have a query engine that can successfully query that data, then I don't know how much value it is for you. So Data fusion helps enable the, the query process and transformation of that data. It also has a PANDAS API so that you could take advantage of PANDAS data frames as well and all of the machine learning tools associated with Pandas. >>Okay. You're also leveraging Par K in the platform cause we heard a lot about Par K in the middle of the last decade cuz as a storage format to improve on Hadoop column stores. What are you doing with Parque and why is it important? >>Sure. So parque is the column oriented durable file format. So it's important because it'll enable bulk import, bulk export, it has compatibility with Python and Pandas, so it supports a broader ecosystem. Par K files also take very little disc disc space and they're faster to scan because again, they're column oriented in particular, I think PAR K files are like 16 times cheaper than CSV files, just as kind of a point of reference. And so that's essentially a lot of the, the benefits of par k. >>Got it. Very popular. So and he's, what exactly is influx data focusing on as a committer to these projects? What is your focus? What's the value that you're bringing to the community? >>Sure. So Influx DB first has contributed a lot of different, different things to the Apache ecosystem. For example, they contribute an implementation of Apache Arrow and go and that will support clearing with flux. Also, there has been a quite a few contributions to data fusion for things like memory optimization and supportive additional SQL features like support for timestamp, arithmetic and support for exist clauses and support for memory control. So yeah, Influx has contributed a a lot to the Apache ecosystem and continues to do so. And I think kind of the idea here is that if you can improve these upstream projects and then the long term strategy here is that the more you contribute and build those up, then the more you will perpetuate that cycle of improvement and the more we will invest in our own project as well. So it's just that kind of symbiotic relationship and appreciation of the open source community. >>Yeah. Got it. You got that virtuous cycle going, the people call the flywheel. Give us your last thoughts and kind of summarize, you know, where what, what the big takeaways are from your perspective. >>So I think the big takeaway is that influx data is doing a lot of really exciting things with Influx DB IOx and I really encourage, if you are interested in learning more about the technologies that Influx is leveraging to produce IOCs, the challenges associated with it and all of the hard work questions and you just wanna learn more, then I would encourage you to go to the monthly Tech talks and community office hours and they are on every second Wednesday of the month at 8:30 AM Pacific time. There's also a community forums and a community Slack channel look for the influx DDB unders IAC channel specifically to learn more about how to join those office hours and those monthly tech tech talks as well as ask any questions they have about iacs, what to expect and what you'd like to learn more about. I as a developer advocate, I wanna answer your questions. So if there's a particular technology or stack that you wanna dive deeper into and want more explanation about how INFLUX DB leverages it to build IOCs, I will be really excited to produce content on that topic for you. >>Yeah, that's awesome. You guys have a really rich community, collaborate with your peers, solve problems, and, and you guys super responsive, so really appreciate that. All right, thank you so much Anise for explaining all this open source stuff to the audience and why it's important to the future of data. >>Thank you. I really appreciate it. >>All right, you're very welcome. Okay, stay right there and in a moment I'll be back with Tim Yoakum, he's the director of engineering for Influx Data and we're gonna talk about how you update a SAS engine while the plane is flying at 30,000 feet. You don't wanna miss this. >>I'm really glad that we went with InfluxDB Cloud for our hosting because it has saved us a ton of time. It's helped us move faster, it's saved us money. And also InfluxDB has good support. My name's Alex Nada. I am CTO at Noble nine. Noble Nine is a platform to measure and manage service level objectives, which is a great way of measuring the reliability of your systems. You can essentially think of an slo, the product we're providing to our customers as a bunch of time series. So we need a way to store that data and the corresponding time series that are related to those. The main reason that we settled on InfluxDB as we were shopping around is that InfluxDB has a very flexible query language and as a general purpose time series database, it basically had the set of features we were looking for. >>As our platform has grown, we found InfluxDB Cloud to be a really scalable solution. We can quickly iterate on new features and functionality because Influx Cloud is entirely managed, it probably saved us at least a full additional person on our team. We also have the option of running InfluxDB Enterprise, which gives us the ability to even host off the cloud or in a private cloud if that's preferred by a customer. Influx data has been really flexible in adapting to the hosting requirements that we have. They listened to the challenges we were facing and they helped us solve it. As we've continued to grow, I'm really happy we have influx data by our side. >>Okay, we're back with Tim Yokum, who is the director of engineering at Influx Data. Tim, welcome. Good to see you. >>Good to see you. Thanks for having me. >>You're really welcome. Listen, we've been covering open source software in the cube for more than a decade, and we've kind of watched the innovation from the big data ecosystem. The cloud has been being built out on open source, mobile, social platforms, key databases, and of course influx DB and influx data has been a big consumer and contributor of open source software. So my question to you is, where have you seen the biggest bang for the buck from open source software? >>So yeah, you know, influx really, we thrive at the intersection of commercial services and open, so open source software. So OSS keeps us on the cutting edge. We benefit from OSS in delivering our own service from our core storage engine technologies to web services temping engines. Our, our team stays lean and focused because we build on proven tools. We really build on the shoulders of giants and like you've mentioned, even better, we contribute a lot back to the projects that we use as well as our own product influx db. >>You know, but I gotta ask you, Tim, because one of the challenge that that we've seen in particular, you saw this in the heyday of Hadoop, the, the innovations come so fast and furious and as a software company you gotta place bets, you gotta, you know, commit people and sometimes those bets can be risky and not pay off well, how have you managed this challenge? >>Oh, it moves fast. Yeah, that, that's a benefit though because it, the community moves so quickly that today's hot technology can be tomorrow's dinosaur. And what we, what we tend to do is, is we fail fast and fail often. We try a lot of things. You know, you look at Kubernetes for example, that ecosystem is driven by thousands of intelligent developers, engineers, builders, they're adding value every day. So we have to really keep up with that. And as the stack changes, we, we try different technologies, we try different methods, and at the end of the day, we come up with a better platform as a result of just the constant change in the environment. It is a challenge for us, but it's, it's something that we just do every day. >>So we have a survey partner down in New York City called Enterprise Technology Research etr, and they do these quarterly surveys of about 1500 CIOs, IT practitioners, and they really have a good pulse on what's happening with spending. And the data shows that containers generally, but specifically Kubernetes is one of the areas that has kind of, it's been off the charts and seen the most significant adoption and velocity particularly, you know, along with cloud. But, but really Kubernetes is just, you know, still up until the right consistently even with, you know, the macro headwinds and all, all of the stuff that we're sick of talking about. But, so what are you doing with Kubernetes in the platform? >>Yeah, it, it's really central to our ability to run the product. When we first started out, we were just on AWS and, and the way we were running was, was a little bit like containers junior. Now we're running Kubernetes everywhere at aws, Azure, Google Cloud. It allows us to have a consistent experience across three different cloud providers and we can manage that in code so our developers can focus on delivering services, not trying to learn the intricacies of Amazon, Azure, and Google and figure out how to deliver services on those three clouds with all of their differences. >>Just to follow up on that, is it, no. So I presume it's sounds like there's a PAs layer there to allow you guys to have a consistent experience across clouds and out to the edge, you know, wherever is that, is that correct? >>Yeah, so we've basically built more or less platform engineering, This is the new hot phrase, you know, it, it's, Kubernetes has made a lot of things easy for us because we've built a platform that our developers can lean on and they only have to learn one way of deploying their application, managing their application. And so that, that just gets all of the underlying infrastructure out of the way and, and lets them focus on delivering influx cloud. >>Yeah, and I know I'm taking a little bit of a tangent, but is that, that, I'll call it a PAs layer if I can use that term. Is that, are there specific attributes to Influx db or is it kind of just generally off the shelf paths? You know, are there, is, is there any purpose built capability there that, that is, is value add or is it pretty much generic? >>So we really build, we, we look at things through, with a build versus buy through a, a build versus by lens. Some things we want to leverage cloud provider services, for instance, Postgres databases for metadata, perhaps we'll get that off of our plate, let someone else run that. We're going to deploy a platform that our engineers can, can deliver on that has consistency that is, is all generated from code that we can as a, as an SRE group, as an ops team, that we can manage with very few people really, and we can stamp out clusters across multiple regions and in no time. >>So how, so sometimes you build, sometimes you buy it. How do you make those decisions and and what does that mean for the, for the platform and for customers? >>Yeah, so what we're doing is, it's like everybody else will do, we're we're looking for trade offs that make sense. You know, we really want to protect our customers data. So we look for services that support our own software with the most uptime, reliability, and durability we can get. Some things are just going to be easier to have a cloud provider take care of on our behalf. We make that transparent for our own team. And of course for customers you don't even see that, but we don't want to try to reinvent the wheel, like I had mentioned with SQL data stores for metadata, perhaps let's build on top of what of these three large cloud providers have already perfected. And we can then focus on our platform engineering and we can have our developers then focus on the influx data, software, influx, cloud software. >>So take it to the customer level, what does it mean for them? What's the value that they're gonna get out of all these innovations that we've been been talking about today and what can they expect in the future? >>So first of all, people who use the OSS product are really gonna be at home on our cloud platform. You can run it on your desktop machine, on a single server, what have you, but then you want to scale up. We have some 270 terabytes of data across, over 4 billion series keys that people have stored. So there's a proven ability to scale now in terms of the open source, open source software and how we've developed the platform. You're getting highly available high cardinality time series platform. We manage it and, and really as, as I mentioned earlier, we can keep up with the state of the art. We keep reinventing, we keep deploying things in real time. We deploy to our platform every day repeatedly all the time. And it's that continuous deployment that allows us to continue testing things in flight, rolling things out that change new features, better ways of doing deployments, safer ways of doing deployments. >>All of that happens behind the scenes. And like we had mentioned earlier, Kubernetes, I mean that, that allows us to get that done. We couldn't do it without having that platform as a, as a base layer for us to then put our software on. So we, we iterate quickly. When you're on the, the Influx cloud platform, you really are able to, to take advantage of new features immediately. We roll things out every day and as those things go into production, you have, you have the ability to, to use them. And so in the end we want you to focus on getting actual insights from your data instead of running infrastructure, you know, let, let us do that for you. So, >>And that makes sense, but so is the, is the, are the innovations that we're talking about in the evolution of Influx db, do, do you see that as sort of a natural evolution for existing customers? I, is it, I'm sure the answer is both, but is it opening up new territory for customers? Can you add some color to that? >>Yeah, it really is it, it's a little bit of both. Any engineer will say, well, it depends. So cloud native technologies are, are really the hot thing. Iot, industrial iot especially, people want to just shove tons of data out there and be able to do queries immediately and they don't wanna manage infrastructure. What we've started to see are people that use the cloud service as their, their data store backbone and then they use edge computing with R OSS product to ingest data from say, multiple production lines and downsample that data, send the rest of that data off influx cloud where the heavy processing takes place. So really us being in all the different clouds and iterating on that and being in all sorts of different regions allows for people to really get out of the, the business of man trying to manage that big data, have us take care of that. And of course as we change the platform end users benefit from that immediately. And, >>And so obviously taking away a lot of the heavy lifting for the infrastructure, would you say the same thing about security, especially as you go out to IOT and the Edge? How should we be thinking about the value that you bring from a security perspective? >>Yeah, we take, we take security super seriously. It, it's built into our dna. We do a lot of work to ensure that our platform is secure, that the data we store is, is kept private. It's of course always a concern. You see in the news all the time, companies being compromised, you know, that's something that you can have an entire team working on, which we do to make sure that the data that you have, whether it's in transit, whether it's at rest, is always kept secure, is only viewable by you. You know, you look at things like software, bill of materials, if you're running this yourself, you have to go vet all sorts of different pieces of software. And we do that, you know, as we use new tools. That's something that, that's just part of our jobs to make sure that the platform that we're running it has, has fully vetted software and, and with open source especially, that's a lot of work. And so it's, it's definitely new territory. Supply chain attacks are, are definitely happening at a higher clip than they used to, but that is, that is really just part of a day in the, the life for folks like us that are, are building platforms. >>Yeah, and that's key. I mean especially when you start getting into the, the, you know, we talk about IOT and the operations technologies, the engineers running the, that infrastructure, you know, historically, as you know, Tim, they, they would air gap everything. That's how they kept it safe. But that's not feasible anymore. Everything's >>That >>Connected now, right? And so you've gotta have a partner that is again, take away that heavy lifting to r and d so you can focus on some of the other activities. Right. Give us the, the last word and the, the key takeaways from your perspective. >>Well, you know, from my perspective I see it as, as a a two lane approach with, with influx, with Anytime series data, you know, you've got a lot of stuff that you're gonna run on-prem, what you had mentioned, air gaping. Sure there's plenty of need for that, but at the end of the day, people that don't want to run big data centers, people that want torus their data to, to a company that's, that's got a full platform set up for them that they can build on, send that data over to the cloud, the cloud is not going away. I think more hybrid approach is, is where the future lives and that's what we're prepared for. >>Tim, really appreciate you coming to the program. Great stuff. Good to see you. >>Thanks very much. Appreciate it. >>Okay, in a moment I'll be back to wrap up. Today's session, you're watching The Cube. >>Are you looking for some help getting started with InfluxDB Telegraph or Flux Check >>Out Influx DB University >>Where you can find our entire catalog of free training that will help you make the most of your time series data >>Get >>Started for free@influxdbu.com. >>We'll see you in class. >>Okay, so we heard today from three experts on time series and data, how the Influx DB platform is evolving to support new ways of analyzing large data sets very efficiently and effectively in real time. And we learned that key open source components like Apache Arrow and the Rust Programming environment Data fusion par K are being leveraged to support realtime data analytics at scale. We also learned about the contributions in importance of open source software and how the Influx DB community is evolving the platform with minimal disruption to support new workloads, new use cases, and the future of realtime data analytics. Now remember these sessions, they're all available on demand. You can go to the cube.net to find those. Don't forget to check out silicon angle.com for all the news related to things enterprise and emerging tech. And you should also check out influx data.com. There you can learn about the company's products. You'll find developer resources like free courses. You could join the developer community and work with your peers to learn and solve problems. And there are plenty of other resources around use cases and customer stories on the website. This is Dave Valante. Thank you for watching Evolving Influx DB into the smart data platform, made possible by influx data and brought to you by the Cube, your leader in enterprise and emerging tech coverage.

>>This past May, The Cube in collaboration with Influx data shared with you the latest innovations in Time series databases. We talked at length about why a purpose built time series database for many use cases, was a superior alternative to general purpose databases trying to do the same thing. Now, you may, you may remember the time series data is any data that's stamped in time, and if it's stamped, it can be analyzed historically. And when we introduced the concept to the community, we talked about how in theory, those time slices could be taken, you know, every hour, every minute, every second, you know, down to the millisecond and how the world was moving toward realtime or near realtime data analysis to support physical infrastructure like sensors and other devices and IOT equipment. A time series databases have had to evolve to efficiently support realtime data in emerging use cases in iot T and other use cases. >>And to do that, new architectural innovations have to be brought to bear. As is often the case, open source software is the linchpin to those innovations. Hello and welcome to Evolving Influx DB into the smart Data platform, made possible by influx data and produced by the Cube. My name is Dave Valante and I'll be your host today. Now in this program we're going to dig pretty deep into what's happening with Time series data generally, and specifically how Influx DB is evolving to support new workloads and demands and data, and specifically around data analytics use cases in real time. Now, first we're gonna hear from Brian Gilmore, who is the director of IOT and emerging technologies at Influx Data. And we're gonna talk about the continued evolution of Influx DB and the new capabilities enabled by open source generally and specific tools. And in this program you're gonna hear a lot about things like Rust, implementation of Apache Arrow, the use of par k and tooling such as data fusion, which powering a new engine for Influx db. >>Now, these innovations, they evolve the idea of time series analysis by dramatically increasing the granularity of time series data by compressing the historical time slices, if you will, from, for example, minutes down to milliseconds. And at the same time, enabling real time analytics with an architecture that can process data much faster and much more efficiently. Now, after Brian, we're gonna hear from Anna East Dos Georgio, who is a developer advocate at In Flux Data. And we're gonna get into the why of these open source capabilities and how they contribute to the evolution of the Influx DB platform. And then we're gonna close the program with Tim Yokum, he's the director of engineering at Influx Data, and he's gonna explain how the Influx DB community actually evolved the data engine in mid-flight and which decisions went into the innovations that are coming to the market. Thank you for being here. We hope you enjoy the program. Let's get started. Okay, we're kicking things off with Brian Gilmore. He's the director of i t and emerging Technology at Influx State of Bryan. Welcome to the program. Thanks for coming on. >>Thanks Dave. Great to be here. I appreciate the time. >>Hey, explain why Influx db, you know, needs a new engine. Was there something wrong with the current engine? What's going on there? >>No, no, not at all. I mean, I think it's, for us, it's been about staying ahead of the market. I think, you know, if we think about what our customers are coming to us sort of with now, you know, related to requests like sql, you know, query support, things like that, we have to figure out a way to, to execute those for them in a way that will scale long term. And then we also, we wanna make sure we're innovating, we're sort of staying ahead of the market as well and sort of anticipating those future needs. So, you know, this is really a, a transparent change for our customers. I mean, I think we'll be adding new capabilities over time that sort of leverage this new engine, but you know, initially the customers who are using us are gonna see just great improvements in performance, you know, especially those that are working at the top end of the, of the workload scale, you know, the massive data volumes and things like that. >>Yeah, and we're gonna get into that today and the architecture and the like, but what was the catalyst for the enhancements? I mean, when and how did this all come about? >>Well, I mean, like three years ago we were primarily on premises, right? I mean, I think we had our open source, we had an enterprise product, you know, and, and sort of shifting that technology, especially the open source code base to a service basis where we were hosting it through, you know, multiple cloud providers. That was, that was, that was a long journey I guess, you know, phase one was, you know, we wanted to host enterprise for our customers, so we sort of created a service that we just managed and ran our enterprise product for them. You know, phase two of this cloud effort was to, to optimize for like multi-tenant, multi-cloud, be able to, to host it in a truly like sass manner where we could use, you know, some type of customer activity or consumption as the, the pricing vector, you know, And, and that was sort of the birth of the, of the real first influx DB cloud, you know, which has been really successful. >>We've seen, I think like 60,000 people sign up and we've got tons and tons of, of both enterprises as well as like new companies, developers, and of course a lot of home hobbyists and enthusiasts who are using out on a, on a daily basis, you know, and having that sort of big pool of, of very diverse and very customers to chat with as they're using the product, as they're giving us feedback, et cetera, has has, you know, pointed us in a really good direction in terms of making sure we're continuously improving that and then also making these big leaps as we're doing with this, with this new engine. >>Right. So you've called it a transparent change for customers, so I'm presuming it's non-disruptive, but I really wanna understand how much of a pivot this is and what, what does it take to make that shift from, you know, time series, you know, specialist to real time analytics and being able to support both? >>Yeah, I mean, it's much more of an evolution, I think, than like a shift or a pivot. You know, time series data is always gonna be fundamental and sort of the basis of the solutions that we offer our customers, and then also the ones that they're building on the sort of raw APIs of our platform themselves. You know, the time series market is one that we've worked diligently to lead. I mean, I think when it comes to like metrics, especially like sensor data and app and infrastructure metrics, if we're being honest though, I think our, our user base is well aware that the way we were architected was much more towards those sort of like backwards looking historical type analytics, which are key for troubleshooting and making sure you don't, you know, run into the same problem twice. But, you know, we had to ask ourselves like, what can we do to like better handle those queries from a performance and a, and a, you know, a time to response on the queries, and can we get that to the point where the results sets are coming back so quickly from the time of query that we can like limit that window down to minutes and then seconds. >>And now with this new engine, we're really starting to talk about a query window that could be like returning results in, in, you know, milliseconds of time since it hit the, the, the ingest queue. And that's, that's really getting to the point where as your data is available, you can use it and you can query it, you can visualize it, and you can do all those sort of magical things with it, you know? And I think getting all of that to a place where we're saying like, yes to the customer on, you know, all of the, the real time queries, the, the multiple language query support, but, you know, it was hard, but we're now at a spot where we can start introducing that to, you know, a a limited number of customers, strategic customers and strategic availability zones to start. But you know, everybody over time. >>So you're basically going from what happened to in, you can still do that obviously, but to what's happening now in the moment? >>Yeah, yeah. I mean if you think about time, it's always sort of past, right? I mean, like in the moment right now, whether you're talking about like a millisecond ago or a minute ago, you know, that's, that's pretty much right now, I think for most people, especially in these use cases where you have other sort of components of latency induced by the, by the underlying data collection, the architecture, the infrastructure, the, you know, the, the devices and you know, the sort of highly distributed nature of all of this. So yeah, I mean, getting, getting a customer or a user to be able to use the data as soon as it is available is what we're after here. >>I always thought, you know, real, I always thought of real time as before you lose the customer, but now in this context, maybe it's before the machine blows up. >>Yeah, it's, it's, I mean it is operationally or operational real time is different, you know, and that's one of the things that really triggered us to know that we were, we were heading in the right direction, is just how many sort of operational customers we have. You know, everything from like aerospace and defense. We've got companies monitoring satellites, we've got tons of industrial users, users using us as a processes storing on the plant floor, you know, and, and if we can satisfy their sort of demands for like real time historical perspective, that's awesome. I think what we're gonna do here is we're gonna start to like edge into the real time that they're used to in terms of, you know, the millisecond response times that they expect of their control systems, certainly not their, their historians and databases. >>I, is this available, these innovations to influx DB cloud customers only who can access this capability? >>Yeah. I mean commercially and today, yes. You know, I think we want to emphasize that's a, for now our goal is to get our latest and greatest and our best to everybody over time. Of course. You know, one of the things we had to do here was like we double down on sort of our, our commitment to open source and availability. So like anybody today can take a look at the, the libraries in on our GitHub and, you know, can ex inspect it and even can try to, you know, implement or execute some of it themselves in their own infrastructure. You know, we are, we're committed to bringing our sort of latest and greatest to our cloud customers first for a couple of reasons. Number one, you know, there are big workloads and they have high expectations of us. I think number two, it also gives us the opportunity to monitor a little bit more closely how it's working, how they're using it, like how the system itself is performing. >>And so just, you know, being careful, maybe a little cautious in terms of, of, of how big we go with this right away, just sort of both limits, you know, the risk of, of, you know, any issues that can come with new software rollouts. We haven't seen anything so far, but also it does give us the opportunity to have like meaningful conversations with a small group of users who are using the products, but once we get through that and they give us two thumbs up on it, it'll be like, open the gates and let everybody in. It's gonna be exciting time for the whole ecosystem. >>Yeah, that makes a lot of sense. And you can do some experimentation and, you know, using the cloud resources. Let's dig into some of the architectural and technical innovations that are gonna help deliver on this vision. What, what should we know there? >>Well, I mean, I think foundationally we built the, the new core on Rust. You know, this is a new very sort of popular systems language, you know, it's extremely efficient, but it's also built for speed and memory safety, which goes back to that us being able to like deliver it in a way that is, you know, something we can inspect very closely, but then also rely on the fact that it's going to behave well. And if it does find error conditions, I mean we, we've loved working with Go and, you know, a lot of our libraries will continue to, to be sort of implemented in Go, but you know, when it came to this particular new engine, you know, that power performance and stability rust was critical. On top of that, like, we've also integrated Apache Arrow and Apache Parque for persistence. I think for anybody who's really familiar with the nuts and bolts of our backend and our TSI and our, our time series merged Trees, this is a big break from that, you know, arrow on the sort of in MI side and then Par K in the on disk side. >>It, it allows us to, to present, you know, a unified set of APIs for those really fast real time inquiries that we talked about, as well as for very large, you know, historical sort of bulk data archives in that PARQUE format, which is also cool because there's an entire ecosystem sort of popping up around Parque in terms of the machine learning community, you know, and getting that all to work, we had to glue it together with aero flight. That's sort of what we're using as our, our RPC component. You know, it handles the orchestration and the, the transportation of the Coer data. Now we're moving to like a true Coer database model for this, this version of the engine, you know, and it removes a lot of overhead for us in terms of having to manage all that serialization, the deserialization, and, you know, to that again, like blurring that line between real time and historical data. It's, you know, it's, it's highly optimized for both streaming micro batch and then batches, but true streaming as well. >>Yeah. Again, I mean, it's funny you mentioned Rust. It is, it's been around for a long time, but it's popularity is, is you know, really starting to hit that steep part of the S-curve. And, and we're gonna dig into to more of that, but give us any, is there anything else that we should know about Bryan? Give us the last word? >>Well, I mean, I think first I'd like everybody sort of watching just to like take a look at what we're offering in terms of early access in beta programs. I mean, if, if, if you wanna participate or if you wanna work sort of in terms of early access with the, with the new engine, please reach out to the team. I'm sure you know, there's a lot of communications going out and you know, it'll be highly featured on our, our website, you know, but reach out to the team, believe it or not, like we have a lot more going on than just the new engine. And so there are also other programs, things we're, we're offering to customers in terms of the user interface, data collection and things like that. And, you know, if you're a customer of ours and you have a sales team, a commercial team that you work with, you can reach out to them and see what you can get access to because we can flip a lot of stuff on, especially in cloud through feature flags. >>But if there's something new that you wanna try out, we'd just love to hear from you. And then, you know, our goal would be that as we give you access to all of these new cool features that, you know, you would give us continuous feedback on these products and services, not only like what you need today, but then what you'll need tomorrow to, to sort of build the next versions of your business. Because you know, the whole database, the ecosystem as it expands out into to, you know, this vertically oriented stack of cloud services and enterprise databases and edge databases, you know, it's gonna be what we all make it together, not just, you know, those of us who were employed by Influx db. And then finally I would just say please, like watch in ICE in Tim's sessions, like these are two of our best and brightest, They're totally brilliant, completely pragmatic, and they are most of all customer obsessed, which is amazing. And there's no better takes, like honestly on the, the sort of technical details of this, then there's, especially when it comes to like the value that these investments will, will bring to our customers and our communities. So encourage you to, to, you know, pay more attention to them than you did to me, for sure. >>Brian Gilmore, great stuff. Really appreciate your time. Thank you. >>Yeah, thanks Dave. It was awesome. Look forward to it. >>Yeah, me too. Looking forward to see how the, the community actually applies these new innovations and goes, goes beyond just the historical into the real time really hot area. As Brian said in a moment, I'll be right back with Anna East dos Georgio to dig into the critical aspects of key open source components of the Influx DB engine, including Rust, Arrow, Parque, data fusion. Keep it right there. You don't wanna miss this >>Time series Data is everywhere. The number of sensors, systems and applications generating time series data increases every day. All these data sources producing so much data can cause analysis paralysis. Influx DB is an entire platform designed with everything you need to quickly build applications that generate value from time series data influx. DB Cloud is a serverless solution, which means you don't need to buy or manage your own servers. There's no need to worry about provisioning because you only pay for what you use. Influx DB Cloud is fully managed so you get the newest features and enhancements as they're added to the platform's code base. It also means you can spend time building solutions and delivering value to your users instead of wasting time and effort managing something else. Influx TVB Cloud offers a range of security features to protect your data, multiple layers of redundancy ensure you don't lose any data access controls ensure that only the people who should see your data can see it. >>And encryption protects your data at rest and in transit between any of our regions or cloud providers. InfluxDB uses a single API across the entire platform suite so you can build on open source, deploy to the cloud and then then easily query data in the cloud at the edge or on prem using the same scripts. And InfluxDB is schemaless automatically adjusting to changes in the shape of your data without requiring changes in your application. Logic. InfluxDB Cloud is production ready from day one. All it needs is your data and your imagination. Get started today@influxdata.com slash cloud. >>Okay, we're back. I'm Dave Valante with a Cube and you're watching evolving Influx DB into the smart data platform made possible by influx data. Anna ETOs Georgio is here, she's a developer advocate for influx data and we're gonna dig into the rationale and value contribution behind several open source technologies that Influx DB is leveraging to increase the granularity of time series analysis analysis and bring the world of data into real-time analytics and is welcome to the program. Thanks for coming on. >>Hi, thank you so much. It's a pleasure to be here. >>Oh, you're very welcome. Okay, so IX is being touted as this next gen open source core for Influx db. And my understanding is that it leverages in memory of course for speed. It's a kilo store, so it gives you a compression efficiency, it's gonna give you faster query speeds, you store files and object storage, so you got very cost effective approach. Are these the salient points on the platform? I know there are probably dozens of other features, but what are the high level value points that people should understand? >>Sure, that's a great question. So some of the main requirements that IOx is trying to achieve and some of the most impressive ones to me, the first one is that it aims to have no limits on cardinality and also allow you to write any kind of event data that you want, whether that's live tag or a field. It also wants to deliver the best in class performance on analytics queries. In addition to our already well served metrics queries, we also wanna have operator control over memory usage. So you should be able to define how much memory is used for buffering caching and query processing. Some other really important parts is the ability to have bulk data export and import super useful. Also broader ecosystem compatibility where possible we aim to use and embrace emerging standards in the data analytics ecosystem and have compatibility with things like sql, Python, and maybe even pandas in the future. >>Okay, so lot there. Now we talked to Brian about how you're using Rust and which is not a new programming language and of course we had some drama around Rust during the pandemic with the Mozilla layoffs, but the formation of the Rust Foundation really addressed any of those concerns. You got big guns like Amazon and Google and Microsoft throwing their collective weights behind it. It's really, the adoption is really starting to get steep on the S-curve. So lots of platforms, lots of adoption with rust, but why rust as an alternative to say c plus plus for example? >>Sure, that's a great question. So Russ was chosen because of his exceptional performance and reliability. So while Russ is synt tactically similar to c plus plus and it has similar performance, it also compiles to a native code like c plus plus. But unlike c plus plus, it also has much better memory safety. So memory safety is protection against bugs or security vulnerabilities that lead to excessive memory usage or memory leaks. And rust achieves this memory safety due to its like innovative type system. Additionally, it doesn't allow for dangling pointers. And dangling pointers are the main classes of errors that lead to exploitable security vulnerabilities in languages like c plus plus. So Russ like helps meet that requirement of having no limits on ality, for example, because it's, we're also using the Russ implementation of Apache Arrow and this control over memory and also Russ Russ's packaging system called crates IO offers everything that you need out of the box to have features like AY and a weight to fix race conditions, to protection against buffering overflows and to ensure thread safe async cashing structures as well. So essentially it's just like has all the control, all the fine grain control, you need to take advantage of memory and all your resources as well as possible so that you can handle those really, really high ity use cases. >>Yeah, and the more I learn about the, the new engine and, and the platform IOCs et cetera, you know, you, you see things like, you know, the old days not even to even today you do a lot of garbage collection in these, in these systems and there's an inverse, you know, impact relative to performance. So it looks like you really, you know, the community is modernizing the platform, but I wanna talk about Apache Arrow for a moment. It it's designed to address the constraints that are associated with analyzing large data sets. We, we know that, but please explain why, what, what is Arrow and and what does it bring to Influx db? >>Sure, yeah. So Arrow is a, a framework for defining in memory calmer data. And so much of the efficiency and performance of IOx comes from taking advantage of calmer data structures. And I will, if you don't mind, take a moment to kind of of illustrate why column or data structures are so valuable. Let's pretend that we are gathering field data about the temperature in our room and also maybe the temperature of our stove. And in our table we have those two temperature values as well as maybe a measurement value, timestamp value, maybe some other tag values that describe what room and what house, et cetera we're getting this data from. And so you can picture this table where we have like two rows with the two temperature values for both our room and the stove. Well usually our room temperature is regulated so those values don't change very often. >>So when you have calm oriented st calm oriented storage, essentially you take each row, each column and group it together. And so if that's the case and you're just taking temperature values from the room and a lot of those temperature values are the same, then you'll, you might be able to imagine how equal values will then enable each other and when they neighbor each other in the storage format, this provides a really perfect opportunity for cheap compression. And then this cheap compression enables high cardinality use cases. It also enables for faster scan rates. So if you wanna define like the men and max value of the temperature in the room across a thousand different points, you only have to get those a thousand different points in order to answer that question and you have those immediately available to you. But let's contrast this with a row oriented storage solution instead so that we can understand better the benefits of calmer oriented storage. >>So if you had a row oriented storage, you'd first have to look at every field like the temperature in, in the room and the temperature of the stove. You'd have to go across every tag value that maybe describes where the room is located or what model the stove is. And every timestamp you'd then have to pluck out that one temperature value that you want at that one time stamp and do that for every single row. So you're scanning across a ton more data and that's why Rowe Oriented doesn't provide the same efficiency as calmer and Apache Arrow is in memory calmer data, commoner data fit framework. So that's where a lot of the advantages come >>From. Okay. So you basically described like a traditional database, a row approach, but I've seen like a lot of traditional database say, okay, now we've got, we can handle colo format versus what you're talking about is really, you know, kind of native i, is it not as effective? Is the, is the foreman not as effective because it's largely a, a bolt on? Can you, can you like elucidate on that front? >>Yeah, it's, it's not as effective because you have more expensive compression and because you can't scan across the values as quickly. And so those are, that's pretty much the main reasons why, why RO row oriented storage isn't as efficient as calm, calmer oriented storage. Yeah. >>Got it. So let's talk about Arrow Data Fusion. What is data fusion? I know it's written in Rust, but what does it bring to the table here? >>Sure. So it's an extensible query execution framework and it uses Arrow as it's in memory format. So the way that it helps in influx DB IOCs is that okay, it's great if you can write unlimited amount of cardinality into influx Cbis, but if you don't have a query engine that can successfully query that data, then I don't know how much value it is for you. So Data fusion helps enable the, the query process and transformation of that data. It also has a PANDAS API so that you could take advantage of PANDAS data frames as well and all of the machine learning tools associated with Pandas. >>Okay. You're also leveraging Par K in the platform cause we heard a lot about Par K in the middle of the last decade cuz as a storage format to improve on Hadoop column stores. What are you doing with Parque and why is it important? >>Sure. So parque is the column oriented durable file format. So it's important because it'll enable bulk import, bulk export, it has compatibility with Python and Pandas, so it supports a broader ecosystem. Par K files also take very little disc disc space and they're faster to scan because again, they're column oriented in particular, I think PAR K files are like 16 times cheaper than CSV files, just as kind of a point of reference. And so that's essentially a lot of the, the benefits of par k. >>Got it. Very popular. So and he's, what exactly is influx data focusing on as a committer to these projects? What is your focus? What's the value that you're bringing to the community? >>Sure. So Influx DB first has contributed a lot of different, different things to the Apache ecosystem. For example, they contribute an implementation of Apache Arrow and go and that will support clearing with flux. Also, there has been a quite a few contributions to data fusion for things like memory optimization and supportive additional SQL features like support for timestamp, arithmetic and support for exist clauses and support for memory control. So yeah, Influx has contributed a a lot to the Apache ecosystem and continues to do so. And I think kind of the idea here is that if you can improve these upstream projects and then the long term strategy here is that the more you contribute and build those up, then the more you will perpetuate that cycle of improvement and the more we will invest in our own project as well. So it's just that kind of symbiotic relationship and appreciation of the open source community. >>Yeah. Got it. You got that virtuous cycle going, the people call the flywheel. Give us your last thoughts and kind of summarize, you know, where what, what the big takeaways are from your perspective. >>So I think the big takeaway is that influx data is doing a lot of really exciting things with Influx DB IOx and I really encourage, if you are interested in learning more about the technologies that Influx is leveraging to produce IOCs, the challenges associated with it and all of the hard work questions and you just wanna learn more, then I would encourage you to go to the monthly Tech talks and community office hours and they are on every second Wednesday of the month at 8:30 AM Pacific time. There's also a community forums and a community Slack channel look for the influx DDB unders IAC channel specifically to learn more about how to join those office hours and those monthly tech tech talks as well as ask any questions they have about iacs, what to expect and what you'd like to learn more about. I as a developer advocate, I wanna answer your questions. So if there's a particular technology or stack that you wanna dive deeper into and want more explanation about how INFLUX DB leverages it to build IOCs, I will be really excited to produce content on that topic for you. >>Yeah, that's awesome. You guys have a really rich community, collaborate with your peers, solve problems, and, and you guys super responsive, so really appreciate that. All right, thank you so much Anise for explaining all this open source stuff to the audience and why it's important to the future of data. >>Thank you. I really appreciate it. >>All right, you're very welcome. Okay, stay right there and in a moment I'll be back with Tim Yoakum, he's the director of engineering for Influx Data and we're gonna talk about how you update a SAS engine while the plane is flying at 30,000 feet. You don't wanna miss this. >>I'm really glad that we went with InfluxDB Cloud for our hosting because it has saved us a ton of time. It's helped us move faster, it's saved us money. And also InfluxDB has good support. My name's Alex Nada. I am CTO at Noble nine. Noble Nine is a platform to measure and manage service level objectives, which is a great way of measuring the reliability of your systems. You can essentially think of an slo, the product we're providing to our customers as a bunch of time series. So we need a way to store that data and the corresponding time series that are related to those. The main reason that we settled on InfluxDB as we were shopping around is that InfluxDB has a very flexible query language and as a general purpose time series database, it basically had the set of features we were looking for. >>As our platform has grown, we found InfluxDB Cloud to be a really scalable solution. We can quickly iterate on new features and functionality because Influx Cloud is entirely managed, it probably saved us at least a full additional person on our team. We also have the option of running InfluxDB Enterprise, which gives us the ability to even host off the cloud or in a private cloud if that's preferred by a customer. Influx data has been really flexible in adapting to the hosting requirements that we have. They listened to the challenges we were facing and they helped us solve it. As we've continued to grow, I'm really happy we have influx data by our side. >>Okay, we're back with Tim Yokum, who is the director of engineering at Influx Data. Tim, welcome. Good to see you. >>Good to see you. Thanks for having me. >>You're really welcome. Listen, we've been covering open source software in the cube for more than a decade, and we've kind of watched the innovation from the big data ecosystem. The cloud has been being built out on open source, mobile, social platforms, key databases, and of course influx DB and influx data has been a big consumer and contributor of open source software. So my question to you is, where have you seen the biggest bang for the buck from open source software? >>So yeah, you know, influx really, we thrive at the intersection of commercial services and open, so open source software. So OSS keeps us on the cutting edge. We benefit from OSS in delivering our own service from our core storage engine technologies to web services temping engines. Our, our team stays lean and focused because we build on proven tools. We really build on the shoulders of giants and like you've mentioned, even better, we contribute a lot back to the projects that we use as well as our own product influx db. >>You know, but I gotta ask you, Tim, because one of the challenge that that we've seen in particular, you saw this in the heyday of Hadoop, the, the innovations come so fast and furious and as a software company you gotta place bets, you gotta, you know, commit people and sometimes those bets can be risky and not pay off well, how have you managed this challenge? >>Oh, it moves fast. Yeah, that, that's a benefit though because it, the community moves so quickly that today's hot technology can be tomorrow's dinosaur. And what we, what we tend to do is, is we fail fast and fail often. We try a lot of things. You know, you look at Kubernetes for example, that ecosystem is driven by thousands of intelligent developers, engineers, builders, they're adding value every day. So we have to really keep up with that. And as the stack changes, we, we try different technologies, we try different methods, and at the end of the day, we come up with a better platform as a result of just the constant change in the environment. It is a challenge for us, but it's, it's something that we just do every day. >>So we have a survey partner down in New York City called Enterprise Technology Research etr, and they do these quarterly surveys of about 1500 CIOs, IT practitioners, and they really have a good pulse on what's happening with spending. And the data shows that containers generally, but specifically Kubernetes is one of the areas that has kind of, it's been off the charts and seen the most significant adoption and velocity particularly, you know, along with cloud. But, but really Kubernetes is just, you know, still up until the right consistently even with, you know, the macro headwinds and all, all of the stuff that we're sick of talking about. But, so what are you doing with Kubernetes in the platform? >>Yeah, it, it's really central to our ability to run the product. When we first started out, we were just on AWS and, and the way we were running was, was a little bit like containers junior. Now we're running Kubernetes everywhere at aws, Azure, Google Cloud. It allows us to have a consistent experience across three different cloud providers and we can manage that in code so our developers can focus on delivering services, not trying to learn the intricacies of Amazon, Azure, and Google and figure out how to deliver services on those three clouds with all of their differences. >>Just to follow up on that, is it, no. So I presume it's sounds like there's a PAs layer there to allow you guys to have a consistent experience across clouds and out to the edge, you know, wherever is that, is that correct? >>Yeah, so we've basically built more or less platform engineering, This is the new hot phrase, you know, it, it's, Kubernetes has made a lot of things easy for us because we've built a platform that our developers can lean on and they only have to learn one way of deploying their application, managing their application. And so that, that just gets all of the underlying infrastructure out of the way and, and lets them focus on delivering influx cloud. >>Yeah, and I know I'm taking a little bit of a tangent, but is that, that, I'll call it a PAs layer if I can use that term. Is that, are there specific attributes to Influx db or is it kind of just generally off the shelf paths? You know, are there, is, is there any purpose built capability there that, that is, is value add or is it pretty much generic? >>So we really build, we, we look at things through, with a build versus buy through a, a build versus by lens. Some things we want to leverage cloud provider services, for instance, Postgres databases for metadata, perhaps we'll get that off of our plate, let someone else run that. We're going to deploy a platform that our engineers can, can deliver on that has consistency that is, is all generated from code that we can as a, as an SRE group, as an ops team, that we can manage with very few people really, and we can stamp out clusters across multiple regions and in no time. >>So how, so sometimes you build, sometimes you buy it. How do you make those decisions and and what does that mean for the, for the platform and for customers? >>Yeah, so what we're doing is, it's like everybody else will do, we're we're looking for trade offs that make sense. You know, we really want to protect our customers data. So we look for services that support our own software with the most uptime, reliability, and durability we can get. Some things are just going to be easier to have a cloud provider take care of on our behalf. We make that transparent for our own team. And of course for customers you don't even see that, but we don't want to try to reinvent the wheel, like I had mentioned with SQL data stores for metadata, perhaps let's build on top of what of these three large cloud providers have already perfected. And we can then focus on our platform engineering and we can have our developers then focus on the influx data, software, influx, cloud software. >>So take it to the customer level, what does it mean for them? What's the value that they're gonna get out of all these innovations that we've been been talking about today and what can they expect in the future? >>So first of all, people who use the OSS product are really gonna be at home on our cloud platform. You can run it on your desktop machine, on a single server, what have you, but then you want to scale up. We have some 270 terabytes of data across, over 4 billion series keys that people have stored. So there's a proven ability to scale now in terms of the open source, open source software and how we've developed the platform. You're getting highly available high cardinality time series platform. We manage it and, and really as, as I mentioned earlier, we can keep up with the state of the art. We keep reinventing, we keep deploying things in real time. We deploy to our platform every day repeatedly all the time. And it's that continuous deployment that allows us to continue testing things in flight, rolling things out that change new features, better ways of doing deployments, safer ways of doing deployments. >>All of that happens behind the scenes. And like we had mentioned earlier, Kubernetes, I mean that, that allows us to get that done. We couldn't do it without having that platform as a, as a base layer for us to then put our software on. So we, we iterate quickly. When you're on the, the Influx cloud platform, you really are able to, to take advantage of new features immediately. We roll things out every day and as those things go into production, you have, you have the ability to, to use them. And so in the end we want you to focus on getting actual insights from your data instead of running infrastructure, you know, let, let us do that for you. So, >>And that makes sense, but so is the, is the, are the innovations that we're talking about in the evolution of Influx db, do, do you see that as sort of a natural evolution for existing customers? I, is it, I'm sure the answer is both, but is it opening up new territory for customers? Can you add some color to that? >>Yeah, it really is it, it's a little bit of both. Any engineer will say, well, it depends. So cloud native technologies are, are really the hot thing. Iot, industrial iot especially, people want to just shove tons of data out there and be able to do queries immediately and they don't wanna manage infrastructure. What we've started to see are people that use the cloud service as their, their data store backbone and then they use edge computing with R OSS product to ingest data from say, multiple production lines and downsample that data, send the rest of that data off influx cloud where the heavy processing takes place. So really us being in all the different clouds and iterating on that and being in all sorts of different regions allows for people to really get out of the, the business of man trying to manage that big data, have us take care of that. And of course as we change the platform end users benefit from that immediately. And, >>And so obviously taking away a lot of the heavy lifting for the infrastructure, would you say the same thing about security, especially as you go out to IOT and the Edge? How should we be thinking about the value that you bring from a security perspective? >>Yeah, we take, we take security super seriously. It, it's built into our dna. We do a lot of work to ensure that our platform is secure, that the data we store is, is kept private. It's of course always a concern. You see in the news all the time, companies being compromised, you know, that's something that you can have an entire team working on, which we do to make sure that the data that you have, whether it's in transit, whether it's at rest, is always kept secure, is only viewable by you. You know, you look at things like software, bill of materials, if you're running this yourself, you have to go vet all sorts of different pieces of software. And we do that, you know, as we use new tools. That's something that, that's just part of our jobs to make sure that the platform that we're running it has, has fully vetted software and, and with open source especially, that's a lot of work. And so it's, it's definitely new territory. Supply chain attacks are, are definitely happening at a higher clip than they used to, but that is, that is really just part of a day in the, the life for folks like us that are, are building platforms. >>Yeah, and that's key. I mean especially when you start getting into the, the, you know, we talk about IOT and the operations technologies, the engineers running the, that infrastructure, you know, historically, as you know, Tim, they, they would air gap everything. That's how they kept it safe. But that's not feasible anymore. Everything's >>That >>Connected now, right? And so you've gotta have a partner that is again, take away that heavy lifting to r and d so you can focus on some of the other activities. Right. Give us the, the last word and the, the key takeaways from your perspective. >>Well, you know, from my perspective I see it as, as a a two lane approach with, with influx, with Anytime series data, you know, you've got a lot of stuff that you're gonna run on-prem, what you had mentioned, air gaping. Sure there's plenty of need for that, but at the end of the day, people that don't want to run big data centers, people that want torus their data to, to a company that's, that's got a full platform set up for them that they can build on, send that data over to the cloud, the cloud is not going away. I think more hybrid approach is, is where the future lives and that's what we're prepared for. >>Tim, really appreciate you coming to the program. Great stuff. Good to see you. >>Thanks very much. Appreciate it. >>Okay, in a moment I'll be back to wrap up. Today's session, you're watching The Cube. >>Are you looking for some help getting started with InfluxDB Telegraph or Flux Check >>Out Influx DB University >>Where you can find our entire catalog of free training that will help you make the most of your time series data >>Get >>Started for free@influxdbu.com. >>We'll see you in class. >>Okay, so we heard today from three experts on time series and data, how the Influx DB platform is evolving to support new ways of analyzing large data sets very efficiently and effectively in real time. And we learned that key open source components like Apache Arrow and the Rust Programming environment Data fusion par K are being leveraged to support realtime data analytics at scale. We also learned about the contributions in importance of open source software and how the Influx DB community is evolving the platform with minimal disruption to support new workloads, new use cases, and the future of realtime data analytics. Now remember these sessions, they're all available on demand. You can go to the cube.net to find those. Don't forget to check out silicon angle.com for all the news related to things enterprise and emerging tech. And you should also check out influx data.com. There you can learn about the company's products. You'll find developer resources like free courses. You could join the developer community and work with your peers to learn and solve problems. And there are plenty of other resources around use cases and customer stories on the website. This is Dave Valante. Thank you for watching Evolving Influx DB into the smart data platform, made possible by influx data and brought to you by the Cube, your leader in enterprise and emerging tech coverage.

(upbeat music) >> Welcome back to theCUBE's continuing coverage of Snowflake Summit 22 live from Caesars Forum in Las Vegas. I'm Lisa Martin with Dave Vellante. We've got a couple of guests joining us now. We're going to be talking about financial services. Rinesh Patel joins us, the Global Head of Financial Services for Snowflake, and Jack Berkowitz, Chief Data Officer at ADP. Guys, welcome to the program. >> Thanks, thanks for having us. >> Thanks for having us. >> Talk to us about what's going on in the financial services industry as a whole. Obviously, we've seen so much change in the last couple of years. What does the data experience look like for internal folks and of course, for those end user consumers and clients? >> So, one of the big things happening inside of the financial services industry is overcoming the COVID wait, right? A lot of banks, a lot of institutions like ours had a lot of stuff on-prem. And then the move to the Cloud allows us to have that flexibility to deal with it. And out of that is also all these new capabilities. So the machine learning revolution has really hit the services industry, right? And so it's affecting how our IT teams or our data teams are building applications. Also really affecting what the end consumers get out of them. And so there's all sorts of consumerization of the experience over the past couple of years much faster than we ever expected it to happen. >> Right, we have these expectations as consumers that bleed into our business lives that I can do transactions. It's going to be on the swipe in terms of checking authenticity, fraud detection, et cetera. And of course we don't want things to go back in terms of how brands are serving us. Talk about some of the things that you guys have put in place with Snowflake in the last couple of years, particularly at ADP. >> Yeah, so one of the big things that we've done, is, one of the things that we provide is compensation data. So we issue a thing called the National Employment Report that informs the world as to what's happening in the U.S. economy in terms of workers. And then we have compensation data on top of that. So the thing that we've been able to do with Snowflake is to lower the time that it takes us to process that and get that information out into the fingertips of people. And so people can use it to see what's changed in terms of with the worker changes, how much people are making. And they can get it very, very quickly. And we're able to do that with Snowflake now. Used to take us weeks, now it's in a matter of moments we can get that updated information out to people. >> Interesting. It helps with the talent war and- >> Helps in the talent war, helps people adjust, even where they're going to put supply chain in reaction to where people are migrating. We can have all of that inside of the Snowflake system and available almost instantaneously. >> You guys announced the Financial Data Cloud last year. What was that like? 'Cause I know we had Frank on early, he clearly was driving the verticalization of Snowflake if you will, which is kind of rare for a relatively new software company but what's that been like? Give us the update on where you're at and biggest vertical, right? >> Absolutely, it's been an exciting 12 months. We're a platform, but the journey and the vision is more. We're trying to bring together a fragmented ecosystem across financial services. The aim is really to bring together key customers, key data providers, key solution providers all across the different Clouds that exist to allow them to collaborate with data in a seamless way. To solve industry problems. To solve industry problems like ESG, to solve industry problems like quantitative research. And we're seeing a massive groundswell of customers coming to Snowflake, looking at the Financial Services Data Cloud now to actually solve business problems, business critical problems. That's really driving a lot of change in terms of how they operate, in terms of how they win customers, mitigate risk and so forth. >> Jack, I think, I feel like the only industry that's sometimes more complicated than security, is data. Maybe not, security's still maybe more fragmented- >> Well really the intersection of the two is a nightmare. >> And so as you look out on this ecosystem, how do you as the chief data officer, how do you and your organization, what process do you use to decide, okay, which of the, like a chef, which of these ingredients am I going to put together for my business. >> It's a great question, right? There's been explosion of companies. We kind of look at it in two ways. One is we want to make sure that the software and the data can interoperate because we don't want to be in the business of writing bridge code. So first thing is, is having the ecosystem so that the things are tested and can work together. The other area is, and it's important to us is understanding the risk profile of that company. We process about 20% of the U.S. payroll, another 25% of the taxes. And so there's a risk to us that we have an imperative to protect. So we're looking at those companies are they financed, what's their management team. What's the sales experience like, that's important to us. And so technology and the experience of the company coming together are super important to us. >> What's your purview as a chief data officer, I mean, a lot of CDOs that I know came out of the back office and it was a compliance or data quality. You come out of industry from a technology company. So you're sort of the modern... You're like the modern CDO. >> Thanks. Thanks. >> Dave: What's your role? >> I appreciate that. >> You know what I'm saying though? >> And for a while it was like, oh yeah, compliance. >> So I actually- >> And then all of a sudden, boom, big deal. >> Yeah, I really have two jobs. So I have that job with data governance but a lot of data security. But I also have a product development unit, a massive business in monetization of data or people analytics or these compensation benchmarks or helping people get mortgages. So providing that information, so that people can get their mortgage, or their bank loans, or all this other type of transactional data. *So it's both sides of that equation is my reading inside. >> You're responsible for building data products? >> That's right. >> Directly. >> That's right. I've got a massive team that builds data products. >> Okay. That's somewhat unique in your... >> I think it's where CDOs need to be. So we build data products. We build, and we assist as a hub to allow other business units to build analytics that help them either optimize their cost or increase their sales. And then we help with all that governance and communication, we don't want to divide it up. There's a continuum to it. >> And you're a peer of the CIO and the CISO? >> Yeah, exactly. They're my peers. I actually talk to them almost every day. So I've got the CIO as a peer. >> It's a team. >> I've got the security as a peer and we get things done together. >> Talk about the alignment with business. We've been talking a lot about alignment with the data folks, the business folks, the technical folks to identify the right solutions, to be able to govern data, to monetize it, to create data products. What does that... You mentioned a couple of your cohorts, but on the business side, who are some of those key folks? >> So we're like any other big, big organization. We have lots of different business units. So we work directly with either the operational team or the heads of those business units to divine analytic missions that they'll actually execute. And at the same time, we actually have a business unit that's all around data monetization. And so I work with them every single day. And so these business units will come together. I think the big thing for us is to define value and measure that value as we go. As long as we're measuring that value as we go, then we can continue to see improvements. And so, like I said, sometimes it's bottom line, sometimes it's top line, but we're involved. Data is actually a substrate of the company. It's not a side thing to the company. >> Yeah, you are. >> ADP. >> Yeah but if they say data first but you really are data first. >> Yeah. I mean, our CEO says- >> Data's your product. >> Data's our middle name. And it literally is. >> Well, so what do you do in the Snowflake financial services data Cloud? Are you monetizing? >> Yeah. >> What's the plan? >> Yeah, so we have clients. So part of our data monetization is actually providing aggregate and anonymized information that helps other clients make business decisions. So they'll take it into their analytics. So, supply chain optimization, where should we actually put the warehouses based on the population shifts? And so we're actually using the file distribution capabilities or the information distribution, no longer files, where we use Snowflake to actually be that data cloud for those clients. So the data just pops up for our other clients. >> I think the industry's existed a lot with the physical movement of data. When you physically move data, you also physically move the data management challenges. Where do you store it? How do you map it? How do you concord it? And ultimately data sharing is taking away that friction that exists. So it's easier to be able to make informed decisions with the data at hand across two counterparties. >> Yeah, and there's a benefit to us 'cause it lowers our friction. We can have a conversation and somebody can be... Obviously the contracts have to be signed, but once they get done, somebody's up and running on it within minutes. And where it used to be, as you were saying, the movement of data and loss of control, we never actually lose control of it. We know where it is. >> Or yeah, contracts signed, now you got to go through this long process of making sure everything's cool, or a lot of times it could slow down the sale. >> That's right. >> Let's see how that's going to... Let's do a little advanced work. Now you're working without a contract. Here, you can say, "Hey, we're in the Snowflake data cloud. It's governed, you're a part of the ecosystem." >> Yeah, and the ecosystem we announced, oh gee, I think it's probably almost a year and a half ago, a relationship with ICE, Intercontinental Exchange, where they're actually taking our information and their information and creating a new data product that they in turn sell. So you get this sort of combination. >> Absolutely. The ability to form partnerships and monetize data with your partners vastly increases as a consequence. >> Talk to us about the adoption of the financial services data cloud in the last what, maybe nine months or so, since it was announced? And also in terms of the its value proposition, how does the ADP use case articulate that? >> So, very much so. So in terms of momentum, we're a global organization, as you mentioned, we are verticalized. So we have increasingly more expertise and expertise experience now within financial services that allows us to really engage and accelerate our momentum with the top banks, with the biggest asset managers by AUM, insurance companies, sovereign wealth funds on Snowflake. And obviously those data providers and solution providers that we engage with. So the momentum's really there. We're really moving very, very fast in a great market because we've got great opportunity with the capabilities that we have. I mean, ADP is just one of many use cases that we're working with and collaborations that we're taking to market. So yeah, the opportunity to monetize data and help our partners monetize the data has vastly increased within this space. >> When you think about... Oh go ahead, please. >> Yeah I was just going to say, and from our perspective, as we were getting into this, Snowflake was with us on the journey. And that's been a big deal. >> So when you think about data privacy, governance, et cetera, and public policy, it seems like you have, obviously you got things going on in Europe, and you got California, you have other states, there's increasing in complexity. You guys probably love that. (Dave laughs) More data warehouses, but where are we at with that whole? >> It's a great question. Privacy is... We hold some of the most critical information about people because that's our job to help people get paid. And we respect that as sort of our prime agenda. Part of it deals with the technology. How do you monitor, how do you see, make sure that you comply with all these regulations, but a lot of it has to do with the basic ethics of why you're doing and what you're doing. So we have a data and AI ethics board that meets and reviews our use cases. Make sure not only are we doing things properly to the regulation, but are these the types of products, are these the types of opportunities that we as a company want to stand behind on behalf of the consumers? Our company's been around 75 years. We talk about ourselves as a national asset. We have a trust relationship. We want to ensure that that trust relationship is never violated. >> Are you in a position where you can influence public policy and create more standards or framework. >> We actually are, right. We issue something every month called the National Employment Report. It actually tells you what's happening in the U.S. economy. We also issue it in some overseas countries like France. Because of that, we work a lot with various groups. And we can help shape, either data policy, we're involved in understanding although we don't necessarily want to be out in the front, but we want to learn about what's happening with federal trade commission, EOC, because at the end of the day we serve people, I always joke ADP, it's my grandfather's ADP. Well, it was actually my grandfather's ADP. (Dave laughs) He was a small businessman, and he used a ADP all those years ago. So we want to be part of that conversation because we want to continue to earn that trust every day. >> Well, plus your observation space is pretty wide. >> And you've got context and perspective on that that you can bring. >> We move somewhere between two, two and a half trillion dollars a year through our systems. And so we understand what's happening in the economy. >> What are some of the, oh sorry. >> Can your National Employment Report combined with a little Snowflake magic tell us what the hell's going to happen with this economy? >> It's really interesting you say that. Yeah, we actually can. >> Okay. (panelists laugh) >> I think when you think about the amount of data that we are working with, the types of partners that we're working with, the opportunities are infinite. They really, really are. >> So it's either a magic eight ball or it's a crystal ball, but you have it. >> We think- >> We've just uncovered that here on theCUBE. >> We think we have great partners. We have great data. We have a set of industry problems out there that we're working, collaboration with the community to be able to solve. >> What are some of the upcoming use cases Rinesh, that excite you, that are coming up in financial services- >> Great question. >> That snowflake is just going to knock out of the park. >> So look, I think there's a set of here and now problems that the industry faces, ESG's a good one. If you think about ESG, it means many different things from business ethics, to diversity, to your carbon footprint and every asset manager has to make sure they have now some form of green strategy that reflects the values of their investors. And every bank is looking to put in place sustainable lending to help their corporate customers transition. That's a big data problem. And so we're very much at the center of helping those organizations support those informed investors and help those corporates transition to a more sustainable landscape. >> Let me give you an example on Snowflake, we launched capabilities about diversity benchmarks. The first time in the industry companies can understand for their industry, their size, their location what their diversity profile looks like and their org chart profile looks like to differentiate or at least to understand are they doing the right things inside the business. The ability for banks to understand that and everything else, it's a big deal. And that was built on Snowflake. >> I think it's massive, especially in the context of the question around regulation 'cause we're seeing more and more disclosure agreements come out where regulators are making sure that there's no greenwashing taking place. So when you have really strong sources of data that are standardized, that allow that investment process to ingest that data, it does allow for a better outcome for investors. >> Real data, I mean, that diversity example they don't have to rely on a survey. >> It's not a survey. >> Anecdotes. >> It's coming right out of the transactional systems and it's updated, whenever those paychecks are run, whether it's weekly, whether it's biweekly or monthly, all that information gets updated and it's available. >> So it sounds like ADP is a facilitator of a lot of companies ESG initiatives, at least in part? >> Well, we partner with companies all the time. We have over 900,000 clients and all of them are... We've never spoken to a client who's not concerned about their people. And that's just good business. And so, yeah we're involved in that and we'll see where it goes over time now. >> I think there's tremendous opportunity if you think about the data that the ADP have in terms of diversity, in terms of gender pay gap. Huge, huge opportunity to incorporate that, as I said into the ESG principles and criteria. >> Good, 'cause that definitely is what needs to be addressed. (Lisa laughs) Guys thank you so much for joining Dave and me on the program, talking about Snowflake ADP, what you're doing together, and the massive potential that you're helping unlock with the value of data. We appreciate your insights and your time. >> Thank you for having us. >> Dave: Thanks guys. >> Thank you so much. >> For our guests, and Dave Vellante, I'm Lisa Martin. You're watching theCUBE, live in Las Vegas at Snowflake Summit 22. Dave and I will be right back with our next guest. (upbeat music)

(bright music) >> Welcome back everyone to theCUBE's coverage of AWS re:Invent 2021 in person. So a live event, physical in-person, also virtual hybrid. So a lot of great action online, check out the website. All the videos are there on theCUBE, as well as what's going on all of the actions on site and theCUBE's here. I'm John Furrier, your host with Dave Vellante, my cohost. Finally, we've got David Brown, VP of Elastic Compute Cloud. EC2, the bread and butter. Our favorite part of Amazon. David, great to have you back on theCUBE in person. >> John, it's great to be back. It's the first time I'd been on theCUBE in person as well. A lot of virtual events with you guys, but it's amazing to be back at re:Invent. >> We're so excited for you. I know, Matt Garman and I've talked in the past. We've talked in the past. EC2 is just an amazing product. It's always been the core block of AWS. More and more action happening and developers are now getting more action and there's well, we wrote a big piece about it. What's going on? The Silicon's really paying off. You've got to also general purpose Intel and AMD, and you've got the custom silicon, all working together. What's the new update? Give us a scoop. >> Well, John, it's actually 15 years of EC2 this year and I've been lucky to be on that team for 14 years and so incredible to see the growth. It's been an amazing journey. The thing that's really driven us, two things. One is supporting new workloads. And so what are the workloads that customers have available out there trying to do on the cloud that we don't support and launch new instance types. And that's the first thing. The second one is price performance. How do we give customers more performance at a continuously decreasing price year-over-year? And that's just driven innovation across EC2 over the years with things like Graviton. All of our inferential chips are custom silicon, but also instance types with the latest Intel Ice Lake CPU's, latest Milan. We just announced the AMD Milan instance. It's just constantly innovation across the ever-increasing list of instances. So super exciting. >> So instances become the new thing. Provision an instance, spin up an instance. Instance becomes, and you can get instances, flavors, almost like flavors, right? >> David: Yeah. >> Take us through the difference between an instance and then the EC2 itself. >> That's correct, yeah. So we actually have, by end of the year, right now we have over 475 different instances available to you whether it's GPU accelerators, high-performance computing instances, memory optimized, just enormous number. We'll actually hit 500 by the end of the year, but that is it. I mean, customers are looking for different types of machines and those are the instances. >> So the Custom Silicon, it's one of the most interesting developments. We've written about it. AWS secret weapon is one of them. I wonder if you could take us back to the decision points and the journey. The Annapurna acquisition, you started working with them as a partner, then you said, all right, let's just buy the company. >> David: Yeah. >> And then now, you're seeing the acceleration, your time to tapeout is way, way compressed. Maybe what was the catalyst and maybe we can get into where it's going. >> Yeah, absolutely. Super interesting story 'cause it actually starts all the way back in 2008. In 2008, EC2 had actually been around for just a little under two years. And if you remember back then, everybody was like, will virtualize and hypervisors, specialization would never really get you the same performances, what they were calling bare metal back then. Everybody's looking at the cloud. And so we took a look at that. And I mean, network latencies, in some cases with hypervisors were as high as 200 or 300 milliseconds. And it was a number of real challenges. And so we knew that we would have to change the way that virtualization works and get into hardware. And so in 2010, 2011, we started to look at how could I offload my network processing, my IO processing to additional hardware. And that's what we delivered our first Nitro card in 2012 and 2013. We actually offloaded all of the processing of network to a Nitro card. And that Nitro card actually had a Annapurna arm chip on it. Our Nitro 1 chip. >> For the offload? >> The offload card, yeah. And so that's when my team started to code for Arm. We started to work on our Linux works for Arm. We actually had to write our own operating system initially 'cause there weren't any operating systems available we could use. And so that's what we started this journey. And over the years, when we saw how well it worked for networking, we said, let's do it for storage as well. And then we said, Hey, we could actually improve security significantly. And by 2017, we'd actually offloaded 100% of everything we did on that server to our offload cards Leaving a 100% of the server available for customers. And we're still actually the only cloud provider that does that today. >> Just to interject, in the data center today, probably 30% of the general purpose cores are used for offloads. You're saying 0% in the cloud. >> On our nitro instances, so every instance we've launched since 2017, our C5. We use 0% of that central core. And you can actually see that in our instance types. If you look at our largest instance type, you can see that we're giving you 96 cores and we're giving you, and our largest instance, 24 terabytes of memory. We're not giving you 23.6 terabytes 'cause we need some. It's all given to you as the customer. >> So much more efficient, >> Much, much more efficient, much better, better price performance as well. But then ultimately those Nitro chips, we went through Nitro 1, Nitro 2, Nitro 3, Nitro 4. We said, Hey, could we build a general purpose server chip? Could we actually bring Arm into the cloud? And in 2018, we launched the A1 instance, which was our Graviton1 instance. And what we didn't tell people at the time is that it was actually the same chip we were using on our network card. So essentially, it was a network card that we were giving to you as a server. But what it did is it sparked the ecosystem. That's why we put it out there. And I remember before launch, some was saying, is this just going to be a university project? Are we going to see people from big universities using Arm in the cloud? Was it really going to take off? And the response was amazing. The ecosystem just grew. We had customers move to it and immediately begin to see improvements. And we knew that a year later, Graviton2 was going to come out. And Graviton2 was just an amazing chip. It continues to see incredible adoption, 40% price performance improvement over other instances. >> So this is worth calling out because I think that example of the network card, I mean, innovation can come from anywhere. This is what Jassy always would say is do the experiments. Think about the impact of what's going on here. You're focused on a mission. Let's get that processing of the lowest cost, pick up some workloads. So you're constantly tinkering with tuning the engine. New discovery comes in. Nitro is born. The chip comes in. But I think the fundamental thing, and I want to get your reaction to this 'cause we've put this out there on our post on Sunday. And I said, in every inflection point, I'm old enough, my birthday was yesterday. I'm old enough to know that. >> David: I saw that. >> I'm old enough to know that in the eighties, the client server shifts. Every inflection point where development changed, the methodology, the mindset or platforms change, all the apps went to the better platform. Who wants to run their application on a slower platform? And so, and those inflects. So now that's happening now, I believe. So you got better performance and I'm imagining that the app developers are coding for it. Take us through how you see that because okay, you're offering up great performance for workloads. Now it's cloud workloads. That's almost all apps. Can you comment on that? >> Well, it has been really interesting to see. I mean, as I said, we were unsure who was going to use it when we initially launched and the adoption has been amazing. Initially, obviously it's always, a lot of the startups, a lot of the more agile companies that can move a lot faster, typically a little bit smaller. They started experimenting, but the data got out there. That 40% price performance was a reality. And not only for specific workloads, it was broadly successful across a number of workloads. And so we actually just had SAP who obviously is an enormous enterprise, supporting enterprises all over the world, announced that they are going to be moving the S/4 HANA Cloud to run on Graviton2. It's just phenomenal. And we've seen enterprises of that scale and game developers, every single vertical looking to move to Graviton2 and get that 40% price performance. >> Now we have to, as analysts, we have to say, okay, how did you get to that 40%? And you have to make some assumptions obviously. And it feels like you still have some dry powder when you looked at Graviton2. I think you were running, I don't know, it's speculated anyway. I don't know if you guys, it's your data, two and a half, 2.5 gigahertz. >> David: Yeah. >> I don't know if we can share what's going on with Graviton3, but my point is you had some dry powder and now with Graviton3, quite a range of performance, 'cause it really depends on the workload. >> David: That's right. >> Maybe you could give some insight as to that. What can you share about how you tuned Graviton3? >> When we look at benchmarking, we don't want to be trying to find that benchmark that's highly tuned and then put out something that is, Hey, this is the absolute best we can get it to and that's 40%. So that 40% is actually just on average. So we just went and ran real world workloads. And we saw some that were 55%. We saw some that were 25. It depends on what it was, but on average, it was around the 35, 45%, and we said 40%. And the great thing about that is customers come back and say, Hey, we saw 40% in this workload. It wasn't that I had to tune it. And so with Graviton3, launching this week. Available in our C7g instance, we said 25%. And that is just a very standard benchmark in what we're seeing. And as we start to see more customer workloads, I think it's going to be incredible to see what that range looks like. Graviton2 for single-threaded applications, it didn't give you that much of a performance. That's what we meant by cloud applications, generally, multi-threaded. In Graviton3, that's no longer the case. So we've had some customers report up to 80% performance improvements of Graviton2 to Graviton3 when the application was more of a single-threaded application. So we started to see. (group chattering) >> You have to keep going, the time to market is compressing. So you have that, go ahead, sorry. >> No, no, I always want to add one thing on the difference between single and multi-threaded applications. A lot of legacy, you're single threaded. So this is kind of an interesting thing. So the mainframe, migration stuff, you start to see that. Is that where that comes in the whole? >> Well, a lot of the legacy apps, but also even some of the new apps, like single threading like video transcoding, for example, is all done on a single core. It's very difficult. I mean, almost impossible to do that multi-threaded way. A lot of the crypto algorithms as well, encryption and cryptography is often single core. So with Graviton3, we've seen a significant performance boost for video encoding, cryptographic algorithms, that sort of thing, which really impacts even the most modern applications. >> So that's an interesting point because now single threaded is where the vertical use cases come in. It's not like more general purpose OS kind of things. >> Yeah, and Graviton has already been very broad. I think we're just knocking down the last few verticals where maybe it didn't support it and now it absolutely does. >> And if an ISV then ports, like an SAP's ports to Graviton, then the customer doesn't see any, I mean, they're going to see the performance difference, but they don't have to think about it. >> David: Yeah. >> They just say, I choose that instance and I'm going to get better price performance. >> Exactly, so we've seen that from our ISVs. We've also been doing that with our AWS services. So services like EMR, RDS, Elastic Cache, it will be moving and making Graviton2 available for customers, which means the customer doesn't have to do the migration at all. It's all done for them. They just pick the instance and get the price performance benefits, and so yeah. >> I think, oh, no, that was serverless. Sorry. >> Well, Lambda actually just did launch on Graviton2. And I think they were talking about a 35% price performance improvement. >> Who was that? >> Lambda, a couple of months ago. >> So what does an ISV have to do to port to Graviton. >> It's relatively straightforward, and this is actually one of the things that has slowed customers down is the, wow, that must be a big migration. And that ecosystem that I spoke about is the important part. And today, with all the Linux operating systems being available for Arm running on Graviton2, with all of the container runtimes being available, and then slowly open source applications in ISV is being available. It's actually really, really easy. And we just ran the Graviton2 four-day challenge. And we did that because we actually had an enterprise migrate one of the largest production applications in just four days. Now, I probably wouldn't recommend that to most enterprises that we see is a little too fast, but they could actually do that. >> But just from a numbers standpoint, that's insanely amazing. I mean, when you think about four days. >> Yeah. >> And when we talked on virtually last year, this year, I can't remember now. You said, we'll just try it. >> David: That's right. >> And see what happens, so I presume a lot of people have tried it. >> Well, that's my advice. It's the unknown, it's the what will it take? So take a single engineer, tell them and give them a time. Say you have one week, get this running on Graviton2, and I think the results are pretty amazing, very surprised. >> We were one of the first, if not the first to say that Arm is going to be dominant in the enterprise. We know it's dominant in the Edge. And when you look at the performance curves and the time to tape out, it's just astounding. And I don't know if people appreciate that relative to the traditional Moore's law curve. I mean, it's a style. And then when you combine the power of the CPU, the GPU, the NPU, kind of what Apple does in the iPhone, it blows away the historical performance curves. And you're on that curve. >> That's right. >> I wonder if you could sort of explain that. >> So with Graviton, we're optimizing just across every single part of AWS. So one of the nice things is we actually own that end-to-end. So when it starts with the early design of Graviton2 and Graviton3, and we obviously working on other chips right now. We're actually using the cloud to do all of the electronic design automation. So we're able to test with AWS how that Graviton3 chip is going to work long before we've even started taping it out. And so those workloads are running on high-frequency CPU's on Graviton. Actually we're using Graviton to build Graviton now in the cloud. The other thing we're doing is we're making sure that the Annapurna team that's building those CPUs is deeply engaged with my team and we're going to ultimately go and build those instances so that when that chip arrives from tapeout. I'm not waiting nine months or two years, like would normally be the case, but I actually had an instance up and running within a week or two on somebody's desk studying to do the integration. And that's something we've optimized significantly to get done. And so it allows us to get that iteration time. It also allows us to be very, very accurate with our tapeouts. We're not having to go back with Graviton. They're all A1 chips. We're not having to go back and do multiple runs of these things because we can do so much validation and performance testing in the cloud ahead of time. >> This is the epiphany of the Arm model. >> It really is. >> It's a standard. When you send it to the fab, they know what's going to work. You hit volume and it's just no fab. >> Well, this is a great thread. We'll stay on this 'cause Adam told us when we met with them for re:Invent that they're seeing a lot more visibility into use cases at the scale. So the scale gives you an advantage on what instances might work. >> And makes the economics works. >> Makes the economics work, hence the timing, the shrinking time to market, not there, but also for the apps. Talk about the scale advantage you guys have. >> Absolutely. I mean, the scale advantage of AWS plays out in a number of ways for our customers. The first thing is being able to deliver highly optimized hardware. So we don't just look at the Graviton3 CPU, you were speaking about the core count and the frequency and Peter spoke about a lot of that in his keynote yesterday. But we look at how does the Graviton3 CPU work with the rest of the instance. What is the right balance between the CPU and memory? The CPU and the Hydro. What's the performance and the drive? We just launched the Nitro SSD, which is now we've actually building our own custom SSDs for Nitro getting better performance, being able to do updates, better security, making it more cloudy. We're just saying, we've been challenged with the SSD in the parts. The other place that scales really helping is in capacity. Being able to make sure that we can absorb things like the COVID spike, or the stuff you see in the financial industry with just enormous demand for compute. We can do that because of our scale. We are able to scale. And the final area is actually in quality because I have such an enormous fleet. I'm actually able to drive down AFR. So annual failure rates, are we well below what the mathematical theoretical tenant or possibility is? So if you look at what's put on that actual sticker on the box that says you should be able to get a full percent AFR. At scale and with focus, we're actually able to get that down to significantly below what the mathematical entitlement was actually be. >> Yeah, it's incredible. I've got a great, and this is the advantage, and that's why I believe anyone who's writing applications that has includes a database, data transfer, any kind of execution of code will use the stack. >> Why would they? Really, why? We've seen this, like you said before, whether it was PC, then the fastest Pentium or somebody. >> Why would you want your app to run slower? >> Unix box, right? ISVS want it to run as fast and as cheaply as possible. Now power plays into it as well. >> Yeah, well, we do have, I agree with what you're saying. We do have a number of customers that are still looking to run on x86, but obviously customers that want windows. Windows isn't available for Arm and so that's a challenge. They'll continue to do that. And you know the way we do look at it is most law kind of died out on us in 2002, 2003. And what I'm hoping is, not necessarily bringing wars a little back, but then we say, let's not accept the 10%, 15% improvement year-over-year. There's absolutely more we can all be doing. And so I'm excited to see where the x86 world's going and they doing a lot of great stuff. Intel Ice Lakes looking amazing. Milan is really great to have an AWS as well. >> Well, I'm thinking it's fair point 'cause we certainly look what Pat's doing it at Intel and he's remaking the company. I've said he's going to follow on the Arm playbook in my mind a little bit, and which is the right thing to do. So competition is a good thing. >> David: Absolutely. >> We're excited for you and a great to see Graviton and you guys have this kind of inflection point. We've been tracking for a while, but now the world's starting to see it. So congratulations to your team. >> David: Thank you. >> Just a couple of things. You guys have some news on instances. Talk about the deprecation issue and how you guys are keeping instances alive real quick. >> Yeah, we're super customer obsessed at Amazon. And so that really drives us. And one of the worst things for us to do is to have to tell a customer that we no longer supporting a service. We recently actually just deprecated the ECG classic network. I'm not sure if you saw that and that's actually off the 10 years of continuing to support it. And the only reason we did it is we have a tiny percentage of customers still using that from back in 2012. But one of the challenges is obviously instance hardware eventually will ultimately time out and fail and have hardware issues as it gets older and older. And so we didn't want to be in a place, in EC2, where we would have to constantly go to customers and say that M1 small, that C3, whatever you were running, it's no longer supported, please move. That's just a text that customers shouldn't have to do. And if they still getting value out of an older instance, let them keep using it. So we actually just announced at re:Invent, in my keynote on Tuesday, the longevity support for EC2 instances, which means we will never come back to you again and ask you to please get off an instance, because we can actually emulate all those instances on our Nitro system. And so all of these instances are starting to migrate to Nitro. You're getting all the benefits of Nitro for now some of our older zen instances, but also you don't have to worry about that work. That's just not something you need to do to get off in all the instance. >> That's great. That's a great test service. Stay on as long as you want. When you're ready to move, move. Okay, final question for you. I know we've got time, I want to get this in. The global network, you guys are known for AWS cloud WAN serve. Gives you updates on what's going on with that. >> So Werner just announced that in his keynote and over the last two to three years or so, we've seen a lot of customers starting to use the AWS backbone, which is extensive. I mean, you've seen the slides in Werner's keynote. It really does span the world. I think it's probably one of the largest networks out there. Customers starting to use that for actually their branch office communication. So instead of going and provisioning the own international MPLS networks and that sort of thing, they say, let me onboard to AWS with VPN or direct connect, and I can actually run the AWS backbone around the world. Now doing that actually has some complexity. You got to think about transit gateways. You got to think about those inter-region peering. And AWS cloud when takes all of that complexity away, you essentially create a cloud WAN, connecting to it to VPN or direct connect, and you can even go and actually set up network segments. So essentially VLANs for different parts of the organization. So super excited to get out that out of there. >> So the ease of use is the key there. >> Massively easy to use. and we have 26 SD-WAN partners. We even partnering with folks like Verizon and Swisscom in Switzerland to telco to actually allow them to use it for their customers as well. >> We'll probably use your service someday when we have a global rollout date. >> Let's do that, CUBE Global. And then the other was the M1 EC2 instance, which got a lot of applause. >> David: Absolutely. >> M1, I think it was based on A15. >> Yeah, that's for Mac. We've got to be careful 'cause M1 is our first instance as well. >> Yeah right, it's a little confusion there. >> So it's a Mac. The EC2 Mac is with M1 silicon from Apple, which super excited to put out there. >> Awesome. >> David Brown, great to see you in person. Congratulations to you and the team and all the work you guys have done over the years. And now that people starting to realize the cloud platform, the compute just gets better and better. It's a key part of the system. >> Thanks John, it's great to be here. >> Thanks for sharing. >> The SiliconANGLE is here. We're talking about custom silicon here on AWS. I'm John Furrier with Dave Vellante. You're watching theCUBE. The global leader in tech coverage. We'll be right back with more covers from re:Invent after this break. (bright music)

(bright music) >> Welcome to this session of the AWS EC2 15th birthday event. I'm your host, Lisa Martin. I'm joined by Meena Gowdar, the principal product manager for AWS Outposts at AWS. Meena, welcome to the program. >> Thanks Lisa. It's great to be joining here today. >> So you were the first product manager hired to lead the development of the Outpost service. Talk to us about back in the day. The vision of Outpost at that time. >> Yeah, Outpost vision has always been to extend the AWS experience to customers on premises location, and provide a truly consistent hybrid experience, with the same AWS services, APIs and suite of tools available at the region. So we launched Outpost to support customers' workloads that cannot migrate to the region. These are applications that are sensitive to latency, such as manufacturing, workloads, financial trading workloads. Then there are applications that do heavy edge data processing, like image assisted diagnostics and hospitals for example, or smart cities that are fitted with cameras and sensors that gather so much data. And then another use case was regarding data residency that need to remain within certain jurisdictions. Now that AWS cloud is available in 25 regions and we have seven more coming, but that doesn't cover every corner of the world, and customers want us to be closer to their end-users. So Outpost allows them to bring the AWS experience where customer wants us to be. To answer your question about the use case evolution, along the way, in addition to the few that I just mentioned, we've seen a couple of surprises. The first one is application migration. It is an interesting trend from large enterprises that could run applications in the cloud, but must first rearchitect their applications to be cloud ready. These applications need to go through modernization while remaining in close proximity to other dependent systems. So by using Outpost, customers can modernize and containerize using AWS services, while they continued to remain on premises before moving to the region. Here, Outpost acts as a launchpad, serving them to make that leap to the region. We were also surprised by the different types of data residency use cases that customers are thinking about Outposts. For example, iGaming, as sports betting is a growing trend in many countries, they're also heavily regulated requiring providers to run their applications within state boundaries. Outposts allows application providers to standardize on a common AWS infrastructure and deploy the application in as many locations as they want to scale. >> So a lot of evolution and it's short time-frame, and I know that as we're here talking about the EC2 15th birthday, Amazon EC2 Core to AWS, but it's also at the core of Outposts, how does EC2 work on Outposts? >> The simple answer is EC2 works just the same as Outposts does in the region, so giving customers access to the same APIs, tools, and metrics that they are familiar with. With Outposts, customers will access the capacity, just like how they would access them in an availability zone. Customers can extend their VPC from the region and launch EC2 instances using the same APIs, just like how they would do in the region. So they also get to benefit all the tools like auto-scaling, CloudWatch metrics, Flow Logs that they are already familiar with. So the other thing that I also want to share is, at GA, we launched Outposts with the Gen 5 Intel Cascade Lake Processor based instances, that's because they run on AWS Nitro Systems. The Nitro Systems allows us to extend the AWS experience to customers location in a secure manner, and bring all the capabilities to manage and virtualize the underlying compute storage and network capabilities, just the way we do that in the region. So staying true to that Outpost product vision, customers can experience the same sort of EC2 feature sets like EC2 placement groups on demand, capacity, reservations, sharing through resource access managers, IM policies, and security groups so it really is the same EC2. >> I imagine having that same experience, the user experience was a big advantage for customers that were in the last 18 months rapidly transforming and digitizing their businesses. Any customer examples pop up that to you that really speak to, we kept this user experience the same, it really helped customers pivot quickly when the pandemic struck. >> It almost feels like we haven't missed a beat Outpost being a fully managed service that can be rolled into customer's data center, has been a huge differentiator. Especially at a time where customers have to be nimble and ready to respond to their customers or end users. If at all, we've seen the adoption accelerate in the last 12 to 18 months, and that is reflected through our global expansion. We currently support 60 countries worldwide, and we've seen customers deploying Outposts and migrating more applications to run on Outpost worldwide. >> Right. So lots of evolution going on as I mentioned a minute ago. Talk to me about some of the things that you're most excited about. What do you think is coming down the pike in the next 6 to 10 months? >> We're excited about expanding the core EC2 instance offerings, especially bringing our own Graviton Arm processor based instances on Outposts, because of the AWS nitro systems. Most easy to instances that launch in the region will also become available on Outpost. Again, back to the vision to provide a consistent hybrid experience for AWS customers. We're also excited about the 1U and 2U Outpost server form factors, which we will launch later this year. The Outpost service will support both the Intel Ice Lake Processor based instances, and also Graviton Processor based instances. So customers who can't install and, you know, 42U form factor Outposts, can now bring AWS experience in retail stores, back office, and other remote locations that are not traditional data centers. So we're very excited about our next couple of years, and what we are going to be launching for customers. >> Excellent. Meena, Thank you for joining me today for the EC2 15th birthday, talking about the vision of outposts. Again, you were the first product manager hired to lead the development of that. Pretty exciting. What's gone on then the unique use cases that have driven its evolution, and some of the things that are coming down the pike. Very exciting. Thank you for your time. >> Thank you, Lisa, >> For Meena Gowdar, I'm Lisa Martin. Thanks for watching. (bright music)

>>mhm, mm. All right. Mhm. Mhm, mm mm. Mhm. Yeah, mm. Mhm. Yeah, yeah. Mhm, mm. Okay. Mm. Yeah, Yeah. >>Mhm. Mhm. Yeah. Welcome to future cloud made possible by Cisco. My name is Dave Volonte and I'm your host. You know, the cloud is evolving like the universe is expanding at an accelerated pace. No longer is the cloud. Just a remote set of services, you know, somewhere up there. No, the cloud, it's extending to on premises. Data centers are reaching into the cloud through adjacent locations. Clouds are being connected together to each other and eventually they're gonna stretch to the edge and the far edge workloads, location latency, local laws and economics will define the value customers can extract from this new cloud model which unifies the operating experience independent of location. Cloud is moving rapidly from a spare capacity slash infrastructure resource to a platform for application innovation. Now, the challenge is how to make this new cloud simple, secure, agile and programmable. Oh and it has to be cloud agnostic. Now, the real opportunity for customers is to tap into a layer across clouds and data centers that abstracts the underlying complexity of the respective clouds and locations. And it's got to accommodate both mission critical workloads as well as general purpose applications across the spectrum cost, effectively enabling simplicity with minimal labor costs requires infrastructure i. E. Hardware, software, tooling, machine intelligence, AI and partnerships within an ecosystem. It's kind of accommodate a variety of application deployment models like serverless and containers and support for traditional work on VMS. By the way, it also requires a roadmap that will take us well into the next decade because the next 10 years they will not be like the last So why are we here? Well, the cube is covering Cisco's announcements today that connect next generation compute shared memory, intelligent networking and storage resource pools, bringing automation, visibility, application assurance and security to this new decentralized cloud. Now, of course in today's world you wouldn't be considered modern without supporting containers ai and operational tooling that is demanded by forward thinking practitioners. So sit back and enjoy the cubes, special coverage of Cisco's future cloud >>From around the globe. It's the Cube presenting future cloud one event, a world of opportunities brought to you by Cisco. >>We're here with Dejoy Pandey, a VP of emerging tech and incubation at Cisco. V. Joy. Good to see you. Welcome. >>Good to see you as well. Thank you Dave and pleasure to be here. >>So in 2020 we kind of had to redefine the notion of agility when it came to digital business or you know organizations, they had to rethink their concept of agility and business resilience. What are you seeing in terms of how companies are thinking about their operations in this sort of new abnormal context? >>Yeah, I think that's a great question. I think what what we're seeing is that pretty much the application is the center of the universe. And if you think about it, the application is actually driving brand recognition and the brand experience and the brand value. So the example I like to give is think about a banking app uh recovered that did everything that you would expect it to do. But if you wanted to withdraw cash from your bank you would actually have to go to the ATM and punch in some numbers and then look at your screen and go through a process and then finally withdraw cash. Think about what that would have, what what that would do in a post pandemic era where people are trying to go contact less. And so in a situation like this, the digitization efforts that all of these companies are going through and and the modernization of the automation is what is driving brand recognition, brand trust and brand experience. >>Yeah. So I was gonna ask you when I heard you say that, I was gonna say well, but hasn't it always been about the application, but it's different now, isn't it? So I wonder if you talk more about how the application is experience is changing. Yes. As a result of this new digital mandate. But how should organizations think about optimizing those experiences in this new world? >>Absolutely. And I think, yes, it's always been about the application, but it's becoming the center of the universe right now because all interactions with customers and consumers and even businesses are happening through that application. So if the application is unreliable or if the application is not available is untrusted insecure, uh, there's a problem. There's a problem with the brand, with the company and the trust that consumers and customers have with our company. So if you think about an application developer, the weight he or she is carrying on their shoulders is tremendous because you're thinking about rolling features quickly to be competitive. That's the only way to be competitive in this world. You need to think about availability and resiliency. Like you pointed out and experience, you need to think about security and trust. Am I as a customer or consumer willing to put my data in that application? So velocity, availability, Security and trust and all of that depends on the developer. So the experience, the security, the trust, the feature, velocity is what is driving the brand experience now. >>So are those two tensions that say agility and trust, you know, Zero Trust used to be a buzzword now it's a mandate. But are those two vectors counter posed? Can they be merged into one and not affect each other? Does the question makes sense? Right? Security usually handcuffs my speed. But how do you address that? >>Yeah that's a great question. And I think if you think about it today that's the way things are. And if you think about this developer all they want to do is run fast because they want to build those features out and they're going to pick and choose a piece and services that matter to them and build up their app and they want the complexities of the infrastructure and security and trust to be handled by somebody else is not that they don't care about it but they want that abstraction so that is handled by somebody else. And typically within an organization we've seen in the past where this friction between Netapp Sec ops I. T. Tops and and the cloud platform Teams and the developer on one side and these these frictions and these meetings and toil actually take a toll on the developer and that's why companies and apps and developers are not as agile as they would like to be. So I think but it doesn't have to be that way. So I think if there was something that would allow a developer to pick and choose, discover the apis that they would like to use connect those api is in a very simple manner and then be able to scale them out and be able to secure them and in fact not just secure them during the run time when it's deployed. We're right off the back when the fire up that I'd and start developing the application. Wouldn't that be nice? And as you do that, there is a smooth transition between that discovery connectivity and ease of consumption and security with the idea cops. Netapp psych ops teams and see source to ensure that they are not doing something that the organization won't allow them to do in a very seamless manner. >>I want to go back and talk about security but I want to add another complexity before we do that. So for a lot of organizations in the public cloud became a staple of keeping the lights on during the pandemic but it brings new complexities and differences in terms of latency security, which I want to come back to deployment models etcetera. So what are some of the specific networking challenges that you've seen with the cloud native architecture is how are you addressing those? >>Yeah. In fact, if you think about cloud, to me that is a that is a different way of seeing a distributed system. And if you think about a distributed system, what is at the center of the distributed system is the network. So my my favorite comment here is that the network is the wrong time for all distribute systems and modern applications. And that is true because if you think about where things are today, like you said, there's there's cloud assets that a developer might use in the banking example that I gave earlier. I mean if you want to build a contact less app so that you get verified, a customer gets verified on the app. They walk over to the ATM and they were broadcast without touching that ATM. In that kind of an example, you're touching the mobile Rus, let's say U S A P is you're touching cloud API is where the back end might sit. You're touching on primary PS maybe it's an oracle database or a mainframe even where transactional data exists. You're touching branch pipes were the team actually exists and the need for consistency when you withdraw cash and you're carrying all of this and in fact there might be customer data sitting in salesforce somewhere. So it's cloud API is a song premise branch. It's ass is mobile and you need to bring all of these things together and over time you will see more and more of these API is coming from various as providers. So it's not just cloud providers but saas providers that the developer has to use. And so this complexity is very, very real. And this complexity is across the wide open internet. So the application is built across this wide open internet. So the problems of discovery ability, the problems of being able to simply connect these apis and manage the data flow across these apis. The problems of consistency of policy and consumption because all of these areas have their own nuances and what they mean, what the arguments mean and what the A. P. I. Actually means. How do you make it consistent and easy for the developer? That is the networking problem. And that is a problem of building out this network, making traffic engineering easy, making policy easy, making scale out, scale down easy, all of that our networking problems. And so we are solving those problems uh Francisco. >>Yeah the internet is the new private network but it's not so private. So I want to go back to security. I often say that the security model of building a moat, you dig the moat, you get the hardened castle that's just outdated now that the queen is left her castle, I always say it's dangerous out there. And the point is you touched on this, it's it's a huge decentralized system and with distributed apps and data, that notion of perimeter security, it's just no longer valid. So I wonder if you could talk more about how you're thinking about this problem and you definitely address some of that in your earlier comments. But what are you specifically doing to address this and how do you see it evolving? >>Yeah, I mean, that's that's a very important point. I mean, I think if you think about again the wide open internet being the wrong time for all modern applications, what is perimeter security in this uh in this new world? I mean, it's to me it boils down to securing an API because again, going with that running example of this contact lists cash withdrawal feature for a bank, the ap wherever it's it's entre branch SAs cloud, IOS android doesn't matter that FBI is your new security perimeter. And the data object that is trying to access is also the new security perimeter. So if you can secure ap to ap communication and P two data object communication, you should be good. So that is the new frontier. But guess what software is buggy? Everybody's software not saying Cisco software, everybody's Softwares buggy. Uh software is buggy, humans are not reliable and so things mature, things change, things evolve over time. So there needs to be defense in depth. So you need to secure at the API layer had the data object layer, but you also need to secure at every layer below it so that you have good defense and depth if any layer in between is not working out properly. So for us that means ensuring ap to ap communication, not just during long time when the app has been deployed and is running, but during deployment and also during the development life cycle. So as soon as the developer launches an ID, they should be able to figure out that this api is security uses reputable, it has compliant, it is compliant to my to my organization's needs because it is hosted, let's say from Germany and my organization wants appears to be used only if they are being hosted out of Germany so compliance needs and and security needs and reputation. Is it available all the time? Is it secure? And being able to provide that feedback all the time between the security teams and the developer teams in a very seamless real time manner. Yes, again, that's something that we're trying to solve through some of the services that we're trying to produce in san Francisco. >>Yeah, I mean those that layered approach that you're talking about is critical because every layer has, you know, some vulnerability. And so you you've got to protect that with some depth in terms of thinking about security, how should we think about where where Cisco's primary value add is, I mean as parts of the interview has a great security business is growing business, Is it your intention to to to to add value across the entire value chain? I mean obviously you can't do everything so you've got a partner but so has the we think about Cisco's role over the next I'm thinking longer term over the over the next decade. >>Yeah, I mean I think so, we do come in with good strength from the runtime side of the house. So if you think about the security aspects that we haven't played today, uh there's a significant set of assets that we have around user security around around uh with with do and password less. We have significant assets in runtime security. I mean, the entire portfolio that Cisco brings to the table is around one time security, the secure X aspects around posture and policy that will bring to the table. And as you see, Cisco evolve over time, you will see us shifting left. I mean, I know it's an overused term, but that is where security is moving towards. And so that is where api security and data security are moving towards. So learning what we have during runtime because again, runtime is where you learn what's available and that's where you can apply all of the M. L. And I models to figure out what works what doesn't taking those learnings, Taking those catalogs, taking that reputation database and moving it into the deployment and development life cycle and making sure that that's part of that entire they have to deploy to runtime chain is what you will see. Cisco do overtime. >>That's fantastic phenomenal perspective video. Thanks for coming on the cube. Great to have you and look forward to having you again. >>Absolutely. Thank you >>in a moment. We'll talk hybrid cloud applications operations and potential gaps that need to be addressed with costume, Das and VJ Venugopal. You're watching the cube the global leader in high tech coverage. Mhm >>You were cloud. It isn't just a cloud. It's everything flowing through it. It's alive. Yeah, connecting users, applications, data and devices and whether it's cloud, native hybrid or multi cloud, it's more distributed than ever. One company takes you inside, giving you the visibility and the insight you need to take action. >>One company >>has the vision to understand it, all the experience, to securely connect at all on any platform in any environment. So you can work wherever work takes you in a cloud first world between your cloud and being cloud smart, there's a bridge. Cisco the bridge to possible. >>Okay. We're here with costume does, who is the Senior Vice President, General Manager of Cloud and compute at Cisco. And VJ Venugopal, who is the Senior Director for Product Management for cloud compute at Cisco. KTV. J. Good to see you guys welcome. >>Great to see you. Dave to be here. >>Katie, let's talk about cloud you And I last time we're face to face was in Barcelona where we love talking about cloud and I always say to people look, Cisco is not a hyper Scaler, but the big public cloud players, they're like giving you a gift. They spent almost actually over $100 billion last year on Capex. The big four. So you can build on that infrastructure. Cisco is all about hybrid cloud. So help us understand the strategy. There may be how you can leverage that build out and importantly what a customer is telling you they want out of hybrid cloud. >>Yeah, no that's that's that's a perfect question to start with. Dave. So yes. So the hybrid hyper scholars have invested heavily building out their assets. There's a great lot of innovation coming from that space. Um There's also a great innovation set of innovation coming from open source and and that's another source of uh a gift. In fact the I. T. Community. But when I look at my customers they're saying well how do I in the context of my business implement a strategy that takes into consideration everything that I have to manage um in terms of my contemporary work clothes, in terms of my legacy, in terms of everything my developer community wants to do on DEVOPS and really harnessed that innovation that's built in the public cloud, that built an open source that built internally to me, and that naturally leads them down the path of a hybrid cloud strategy. And Siskel's mission is to provide for that imperative, the simplest more power, more powerful platform to deliver hybrid cloud and that platform. Uh It's inter site we've been investing in. Inner side, it's a it's a SAS um service um inner side delivers to them that bridge between their estates of today that were closer today, the need for them to be guardians of enterprise grade resiliency with the agility uh that's needed for the future. The embracing of cloud. Native of new paradigms of deVOPS models, the embracing of innovation coming from public cloud and an open source and bridging those two is what inner side has been doing. That's kind of that's kind of the crux of our strategy. Of course we have the entire portfolio behind it to support any, any version of that, whether that is on prem in the cloud, hybrid, cloud, multi cloud and so forth. >>But but if I understand it correctly from what I heard earlier today, the inter site is really a linchpin of that strategy, is it not? >>It really is and may take a second to totally familiarize those who don't know inner side with what it is. We started building this platform quite a few years back and we we built a ground up to be an immensely scalable SAs, super simple hybrid cloud platform and it's a platform that provides a slew of service is inherently and then on top of that there are suites of services, the sweets of services that are tied to infrastructure, automation. Cisco, as well as Cisco partners. The streets of services that have nothing to do with Cisco um products from a hardware perspective. And it's got to do with more cloud orchestration and cloud native and inner side and its suite of services um continue to kind of increase in pace and velocity of delivery video. Just over the last two quarters we've announced a whole number of things will go a little bit deeper into some of those but they span everything from infrastructure automation to kubernetes and delivering community than service to workload optimization and having visibility into your cloud estate. How much it's costing into your on premise state into your work clothes and how they're performing. It's got integrations with other tooling with both Cisco Abdi uh as well as non Cisco um, assets and then and then it's got a whole slew of capabilities around orchestration because at the end of the day, the job of it is to deliver something that works and works at scale that you can monitor and make sure is resilient and that includes that. That includes a workflow and ability to say, you know, do this and do this and do this. Or it includes other ways of automation, like infrastructure as code and so forth. So it includes self service that so that expand that. But inside the world's simplest hybrid cloud platform, rapidly evolving rapidly delivering new services. And uh we'll talk about some more of those day. >>Great, thank you, Katie VJ. Let's bring you into the discussion. You guys recently made an announcement with the ASCIi corp. I was stoked because even though it seemed like a long time ago, pre covid, I mean in my predictions post, I said, ha, she was a name to watch our data partners. Et are you look at the survey data and they really have become mainstream? You know, particularly we think very important in the whole multi cloud discussion. And as well, they're attractive to customers. They have open source offerings. You can very easily experiment. Smaller organizations can take advantage. But if you want to upgrade to enterprise features like clustering or whatever, you can plug right in. Not a big complicated migration. So a very, very compelling story there. Why is this important? Why is this partnership important to Cisco's customers? Mhm. >>Absolutely. When the spot on every single thing that you said, let me just start by paraphrasing what ambition statement is in the cloud and computer group. Right ambition statement is to enable a cloud operating model for hybrid cloud. And what we mean by that is the ability to have extreme amounts of automation orchestration and observe ability across your hybrid cloud idea operations now. Uh So developers and applications team get a great amount of agility in public clouds and we're on a mission to bring that kind of agility and automation to the private cloud and to the data centers and inter site is a quickie platform and lynchpin to enable that kind of operations. Uh, Cloud like operations in the in the private clouds and the key uh As you rightly said, harsher car is the, you know, they were the inventors of the concept of infrastructure at school and in terra form, they have the world's number one infrastructure as code platform. So it became a natural partnership for Cisco to enter into a technology partnership with harsher card to integrate inter site with hardship cops, terra form to bring the benefits of infrastructure as code to the to hybrid cloud operations. And we've entered into a very tight integration and uh partnership where we allow developers devops teams and infrastructure or administrators to allow the use of infrastructure as code in a SAS delivered manner for both public and private club. So it's a very unique partnership and a unique integration that allows the benefits of cloud managed i E C. To be delivered to hybrid cloud operations. And we've been very happy and proud to be partnering with Russian government shutdown. >>Yeah, Terra form gets very high marks from customers. The a lot of value there. The inner side integration adds to that value. Let's stay on cloud native for a minute. We all talk about cloud native cady was sort of mentioning before you got the the core apps, uh you want to protect those, make sure their enterprise create but they gotta be cool as well for developers. You're connecting to other apps in the cloud or wherever. How are you guys thinking about this? Cloud native trend? What other movies are you making in this regard? >>I mean cloud native is there is one of the paramount I. D. Trends of today and we're seeing massive amounts of adoption of cloud native architecture in all modern applications. Now, Cloud Native has become synonymous with kubernetes these days and communities has emerged as a de facto cloud native platform for modern cloud native app development. Now, what Cisco has done is we have created a brand new SAs delivered kubernetes service that is integrated with inter site, we call it the inter site community service for A. Ks. And this just geared a little over one month ago. Now, what interstate kubernetes service does is it delivers a cloud managed and cloud delivered kubernetes service that can be deployed on any supported target infrastructure. It could be a Cisco infrastructure, it could be a third party infrastructure or it could even be public club. But think of it as kubernetes anywhere delivered as says, managed from inside. It's a very powerful capability that we've just released into inter site to enable the power of communities and clog native to be used to be used anywhere. But today we made a very important aspect because we are today announced the brand new Cisco service mess manager, the Cisco service mesh manager, which is available as an extension to the KS are doing decide basically we see service measures as being the future of networking right in the past we had layer to networking and layer three networking and now with service measures, application networking and layer seven networking is the next frontier of, of networking. But you need to think about networking for the application age very differently how it is managed, how it is deployed. It needs to be ready, developer friendly and developer centric. And so what we've done is we've built out an application networking strategy and built out the service match manager as a very simple way to deliver application networking through the consumers, like like developers and application teams. This is built on an acquisition that Cisco made recently of Banzai Cloud and we've taken the assets of Banzai Cloud and deliver the Cisco service mesh manager as an extension to KS. That brings the promise of future networking and modern networking to application and development gives >>God thank you. BJ. And so Katie, let's let's let's wrap this up. I mean, there was a lot in this announcement today, a lot of themes around openness, heterogeneity and a lot of functionality and value. Give us your final thoughts. >>Absolutely. So, couple of things to close on, first of all, um Inner side is the simplest, most powerful hybrid cloud platform out there. It enables that that cloud operating model that VJ talked about, but enables that across cloud. So it's sad, it's relatively easy to get into it and give it a spin so that I'd highly encouraged anybody who's not familiar with it to try it out and anybody who is familiar with it to look at it again, because they're probably services in there that you didn't notice or didn't know last time you looked at it because we're moving so fast. So that's the first thing. The second thing I close with is um, we've been talking about this bridge that's kind of bridging, bridging uh your your on prem your open source, your cloud estates. And it's so important to to make that mental leap because uh in past generation, we used to talk about integrating technologies together and then with public cloud, we started talking about move to public cloud, but it's really how do we integrate, how do we integrate all of that innovation that's coming from the hyper scale, is everything they're doing to innovate superfast, All of that innovation is coming from open source, all of that innovation that's coming from from companies around the world, including Cisco, How do we integrate that to deliver an outcome? Because at the end of the day, if you're a cloud of Steam, if you're an idea of Steam, your job is to deliver an outcome and our mission is to make it super simple for you to do that. That's the mission we're on and we're hoping that everybody that's excited as we are about how simple we made that. >>Great, thank you a lot in this announcement today, appreciate you guys coming back on and help us unpack you know, some of the details. Thank thanks so much. Great having you. >>Thank you >>Dave in a moment. We're gonna come back and talk about disruptive technologies and futures in the age of hybrid cloud with Vegas Rattana and James leach. You're watching the cube, the global leader in high tech coverage. >>What if your server box >>wasn't a box at >>all? What if it could do anything run anything? >>Be any box you >>need with massive scale precision and intelligence managed and optimized from the cloud integrated with all your clouds, private, public or hybrid. So you can build whatever you need today and tomorrow. The potential of this box is unlimited. Unstoppable unseen ever before. Unbox the future with Cisco UCS X series powered by inter site >>Cisco. >>The bridge to possible. Yeah >>we're here with Vegas Rattana who's the director of product management for Pcs at Cisco. And James Leach is the director of business development for U. C. S. At the Cisco as well. We're gonna talk about computing in the age of hybrid cloud. Welcome gentlemen. Great to see you. >>Thank you. >>Thank you because let's start with you and talk about a little bit about computing architectures. We know that they're evolving. They're supporting new data intensive and other workloads especially as high performance workload requirements. What's this guy's point of view on all this? I mean specifically interested in your thoughts on fabrics. I mean it's kind of your wheelhouse, you've got accelerators. What are the workloads that are driving these evolving technologies and how how is it impacting customers? What are you seeing? >>Sure. First of all, very excited to be here today. You're absolutely right. The pace of innovation and foundational platform ingredients have just been phenomenal in recent years. The fabric that's writers that drives the processing power, the Golden city all have been evolving just an amazing place and the peace will only pick up further. But ultimately it is all about applications and the way applications leverage those innovations. And we do see applications evolving quite rapidly. The new classes of applications are evolving to absorb those innovations and deliver much better business values. Very, very exciting time step. We're talking about the impact on the customers. Well, these innovations have helped them very positively. We do see significant challenges in the data center with the point product based approach of delivering these platforms, innovations to the applications. What has happened is uh, these innovations today are being packaged as point point products to meet the needs of a specific application and as you know, the different applications have no different needs. Some applications need more to abuse, others need more memory, yet others need, you know, more course, something different kinds of fabrics. As a result, if you walk into a data center today, it is very common to see many different point products in the data center. This creates a manageability challenge. Imagine the aspect of managing, you know, several different form factors want you to you purpose built servers. The variety of, you know, a blade form factor, you know, this reminds me of the situation we had before smartphones arrived. You remember the days when you when we used to have a GPS device for navigation system, a cool music device for listening to the music. A phone device for making a call camera for taking the photos right? And we were all excited about it. It's when a smart phones the right that we realized all those cool innovations could be delivered in a much simpler, much convenient and easy to consume through one device. And you know, I could uh, that could completely transform our experience. So we see the customers were benefiting from these innovations to have a way to consume those things in a much more simplistic way than they are able to go to that. >>And I like to look, it's always been about the applications. But to your point, the applications are now moving in a much faster pace. The the customer experience is expectation is way escalated. And when you combine all these, I love your analogy there because because when you combine all these capabilities, it allows us to develop new Applications, new capabilities, new customer experiences. So that's that I always say the next 10 years, they ain't gonna be like the last James Public Cloud obviously is heavily influencing compute design and and and customer operating models. You know, it's funny when the public cloud first hit the market, everyone we were swooning about low cost standard off the shelf servers in storage devices, but it quickly became obvious that customers needed more. So I wonder if you could comment on this. How are the trends that we've seen from the hyper scale, Is how are they filtering into on prem infrastructure and maybe, you know, maybe there's some differences there as well that you could address. >>Absolutely. So I'd say, first of all, quite frankly, you know, public cloud has completely changed the expectations of how our customers want to consume, compute, right? So customers, especially in a public cloud environment, they've gotten used to or, you know, come to accept that they should consume from the application out, right? They want a very application focused view, a services focused view of the world. They don't want to think about infrastructure, right? They want to think about their application, they wanna move outward, Right? So this means that the infrastructure basically has to meet the application where it lives. So what that means for us is that, you know, we're taking a different approach. We're we've decided that we're not going to chase this single pane of glass view of the world, which, frankly, our customers don't want, they don't want a single pane of glass. What they want is a single operating model. They want an operating model that's similar to what they can get at the public with the public cloud, but they wanted across all of their cloud options they wanted across private cloud across hybrid cloud options as well. So what that means is they don't want to just consume infrastructure services. They want all of their cloud services from this operating model. So that means that they may want to consume infrastructure services for automation Orchestration, but they also need kubernetes services. They also need virtualization services, They may need terror form workload optimization. All of these services have to be available, um, from within the operating model, a consistent operating model. Right? So it doesn't matter whether you're talking about private cloud, hybrid cloud anywhere where the application lives. It doesn't matter what matters is that we have a consistent model that we think about it from the application out. And frankly, I'd say this has been the stumbling block for private cloud. Private cloud is hard, right. This is why it hasn't been really solved yet. This is why we had to take a brand new approach. And frankly, it's why we're super excited about X series and inter site as that operating model that fits the hybrid cloud better than anything else we've seen >>is acute. First, first time technology vendor has ever said it's not about a single pane of glass because I've been hearing for decades, we're gonna deliver a single pane of glass is going to be seamless and it never happens. It's like a single version of the truth. It's aspirational and, and it's just not reality. So can we stay in the X series for a minute James? Uh, maybe in this context, but in the launch that we saw today was like a fire hose of announcements. So how does the X series fit into the strategy with inter site and hybrid cloud and this operating model that you're talking about? >>Right. So I think it goes hand in hand, right. Um the two pieces go together very well. So we have uh, you know, this idea of a single operating model that is definitely something that our customers demand, right? It's what we have to have, but at the same time we need to solve the problems of the cost was talking about before we need a single infrastructure to go along with that single operating model. So no longer do we need to have silos within the infrastructure that give us different operating models are different sets of benefits when you want infrastructure that can kind of do all of those configurations, all those applications. And then, you know, the operating model is very important because that's where we abstract the complexity that could come with just throwing all that technology at the infrastructure so that, you know, this is, you know, the way that we think about is the data center is not centered right? It's no longer centered applications live everywhere. Infrastructure lives everywhere. And you know, we need to have that consistent operating model but we need to do things within the infrastructure as well to take full advantage. Right? So we want all the sas benefits um, of a Ci CD model of, you know, the inter site can bring, we want all that that proactive recommendation engine with the power of A I behind it. We want the connected support experience went all of that. They want to do it across the single infrastructure and we think that that's how they tie together, that's why one or the other doesn't really solve the problem. But both together, that's why we're here. That's why we're super excited. >>So Vegas, I make you laugh a little bit when I was an analyst at I D C, I was deep in infrastructure and then when I left I was doing, I was working with application development heads and like you said, uh infrastructure, it was just a, you know, roadblock but but so the target speakers with Cisco announced UCS a decade ago, I totally missed it. I didn't understand it. I thought it was Cisco getting into the traditional server business and it wasn't until I dug in then I realized that your vision was really to transform infrastructure, deployment and management and change them all. I was like, okay, I got that wrong uh but but so let's talk about the the ecosystem and the joint development efforts that are going on there, X series, how does it fit into this, this converged infrastructure business that you've, you've built and grown with partners, you got storage partners like Netapp and Pure, you've got i SV partners in the ecosystem. We see cohesive, he has been a while since we we hung out with all these companies at the Cisco live hopefully next year, but tell us what's happening in that regard. >>Absolutely, I'm looking forward to seeing you in the Cisco live next year. You know, they have absolutely you brought up a very good point. You see this is about the ecosystem that it brings together, it's about making our customers bring up the entire infrastructure from the core foundational hardware all the way to the application level so that they can, you know, go off and running pretty quick. The converse infrastructure has been one of the corners 2.5 hour of the strategy, as you pointed out in the last decade. And and and I'm I'm very glad to share that converse infrastructure continues to be a very popular architecture for several enterprise applications. Seven today, in fact, it is the preferred architecture for mission critical applications where performance resiliency latency are the critical requirements there almost a de facto standards for large scale deployments of virtualized and business critical data bases and so forth with X series with our partnerships with our Stories partners. Those architectures will absolutely continue and will get better. But in addition as a hybrid cloud world, so we are now bringing in the benefits of canvas in infrastructure uh to the world of hybrid cloud will be supporting the hybrid cloud applications now with the CIA infrastructure that we have built together with our strong partnership with the Stories partners to deliver the same benefits to the new ways applications as well. >>Yeah, that's what customers want. They want that cloud operating model. Right, go ahead please. >>I was going to say, you know, the CIA model will continue to thrive. It will transition uh it will expand the use cases now for the new use cases that were beginning to, you know, say they've absolutely >>great thank you for that. And James uh have said earlier today, we heard this huge announcement, um a lot of lot of parts to it and we heard Katie talk about this initiative is it's really computing built for the next decade. I mean I like that because it shows some vision and you've got a road map that you've thought through the coming changes in workloads and infrastructure management and and some of the technology that you can take advantage of beyond just uh, you know, one or two product cycles. So, but I want to understand what you've done here specifically that you feel differentiates you from other competitive architectures in the industry. >>Sure. You know that's a great question. Number one. Number two, um I'm frankly a little bit concerned at times for for customers in general for our customers customers in general because if you look at what's in the market, right, these rinse and repeat systems that were effectively just rehashes of the same old design, right? That we've seen since before 2000 and nine when we brought you C. S to market these are what we're seeing over and over and over again. That's that's not really going to work anymore frankly. And I think that people are getting lulled into a false sense of security by seeing those things continually put in the market. We rethought this from the ground up because frankly future proofing starts now, right? If you're not doing it right today, future proofing isn't even on your radar because you're not even you're not even today proved. So we re thought the entire chassis, the entire architecture from the ground up. Okay. If you look at other vendors, if you look at other solutions in the market, what you'll see is things like management inside the chassis. That's a great example, daisy chaining them together >>like who >>needs that? Who wants that? Like that kind of complexity is first of all, it's ridiculous. Um, second of all, um, if you want to manage across clouds, you have to do it from the cloud, right. It's just common sense. You have to move management where it can have the scale and the scope that it needs to impact your entire domain, your world, which is much larger now than it was before. We're talking about true hybrid cloud here. Right. So we had to solve certain problems that existed in the traditional architecture. You know, I can't tell you how many times I heard you talk about the mid plane is a great example. You know, the mid plane and a chastity is a limiting factor. It limits us on how much we can connect or how much bandwidth we have available to the chassis. It limits us on air flow and other things. So how do you solve that problem? Simple. Just get rid of it. Like we just we took it out, right. It's not no longer a problem. We designed an architecture that doesn't need it. It doesn't rely on it. No forklift upgrades. So, as we start moving down the path of needing liquid cooling or maybe we need to take advantage of some new, high performance, low latency fabrics. We can do that with almost. No problem at all. Right, So, we don't have any forklift upgrades. Park your forklift on the side. You won't need it anymore because you can upgrade gradually. You can move along as technologies come into existence that maybe don't even exist. They they may not even be on our radar today to take advantage of. But I like to think of these technologies, they're really important to our customers. These are, you know, we can call them disruptive technologies. The reality is that we don't want to disrupt our customers with these technologies. We want to give them these technologies so they can go out and be disruptive themselves. Right? And this is the way that we've designed this from the ground up to be easy to consume and to take advantage of what we know about today and what's coming in the future that we may not even know about. So we think this is a way to give our customers that ultimate capability flexibility and and future proofing. >>I like I like that phrase True hybrid cloud. It's one that we've used for years and but to me this is all about that horizontal infrastructure that can support that vision of what true hybrid cloud is. You can support the mission critical applications. You can you can develop on the system and you can support a variety of workload. You're not locked into one narrow stovepipe and that does have legs, Vegas and James. Thanks so much for coming on the program. Great to see you. >>Yeah. Thank you. Thank you. >>When we return shortly thomas Shiva who leads Cisco's data center group will be here and thomas has some thoughts about the transformation of networking I. T. Teams. You don't wanna miss what he has to say. You're watching the cube. The global leader in high tech company. Okay, >>mm. Mhm, mm. Okay. Mhm. Yeah. Mhm. Yeah. >>Mhm. Yes. Yeah. Okay. We're here with thomas Shiva who is the Vice president of Product Management, A K A VP of all things data center, networking STN cloud. You name it in that category. Welcome thomas. Good to see you again. >>Hey Sam. Yes. Thanks for having me on. >>Yeah, it's our pleasure. Okay, let's get right into observe ability. When you think about observe ability, visibility, infrastructure monitoring problem resolution across the network. How does cloud change things? In other words, what are the challenges that networking teams are currently facing as they're moving to the cloud and trying to implement hybrid cloud? >>Yeah. Yeah, visibility as always is very, very important. And it's quite frankly, it's not just it's not just the networking team is actually the application team to write. And as you pointed out, the underlying impetus to what's going on here is the data center is where the data is. And I think we set us a couple years back and really what happens the applications are going to be deployed uh in different locations, right. Whether it's in a public cloud, whether it's on prayer, uh, and they are built differently right there, built as microservices, they might actually be distributed as well at the same application. And so what that really means is you need as an operator as well as actually a user better visibility. Where are my pieces and you need to be able to correlate between where the app is and what the underlying network is that is in place in these different locations. So you have actually a good knowledge while the app is running so fantastic or sometimes not. So I think that's that's really the problem statement. What what we're trying to go afterwards, observe ability. >>Okay, and let's double click on that. So a lot of customers tell me that you gotta stare at log files until your eyes bleed and you gotta bring in guys with lab coats who have phds to figure all this stuff out. So, so you just described, it's getting more complex, but at the same time you have to simplify things. So how how are you doing that, >>correct? So what we basically have done is we have this fantastic product that that is called 1000 Ice. And so what this does is basically as the name, which I think is a fantastic fantastic name. You have these sensors everywhere. Um, and you can have a good correlation on uh links between if I run from a site to aside from a site to a cloud, from a cloud to cloud and you basically can measure what is the performance of these links. And so what we're, what we're doing here is we're actually extending the footprint of these thousands agent. Right? Instead of just having uh inversion machine clouds, we are now embedding them with the Cisco network devices. Right? We announced this with the catalyst 9000 and we're extending this now to our 8000 catalyst product line for the for the SD were in products as well as to the data center products the next line. Um and so what you see is is, you know, half a saying, you have 1000 eyes, you get a million insights and you get a billion dollar of improvements uh for how your applications run. And this is really uh, the power of tying together the footprint of where the network is with the visibility, what is going on. So you actually know the application behavior that is attached to this network. >>I see. So okay. So as the cloud evolves and expands it connects your actually enabling 1000 eyes to go further, not just confined within a single data center location, but out to the network across clouds, et cetera, >>correct. Wherever the network is, you're going to have 1000 I sensor and you can't bring this together and you can quite frankly pick if you want to say, hey, I have my application in public cloud provider, a uh, domain one and I have another one domain to, I can't do monitor that link. I can also monitor have a user that has a campus location or branch location. I kind of put an agent there and then I can monitor the connectivity from that branch location all the way to the let's say corporations that data centre, our headquarter or to the cloud. And I can have these probes and just we have visibility and saying, hey, if there's a performance, I know where the issue is and then I obviously can use all the other foods that we have to address those. >>All right, let's talk about the cloud operating model. Everybody tells us it's really the change in the model that drives big numbers in terms of R. O. I. And I want you to maybe address how you're bringing automation and devops to this world of of hybrid and specifically how is Cisco enabling I. T. Organizations to move to a cloud operating model? Is that cloud definition expands? >>Yeah, no that's that's another interesting topic beyond the observe ability. So really, really what we're seeing and this is going on for uh I want to say a couple of years now, it's really this transition from operating infrastructure as a networking team more like a service like what you would expect from a cloud provider. Right? It's really around the network team offering services like a cloud provided us. And that's really what the meaning is of cloud operating model. Right? But this is infrastructure running your own data center where that's linking that infrastructure was whatever runs on the public club is operating and like a cloud service. And so we are on this journey for why? So one of the examples uh then we have removing some of the control software assets, the customers that they can deploy on prayer uh to uh an instance that they can deploy in a cloud provider and just busy, insane. She ate things there and then just run it that way. Right. And so the latest example for this is what we have our identity service engine that is now limited availability available on AWS and will become available in mid this year, both in Italy as unusual as a service. You can just go to market place, you can load it there and now you create, you can start running your policy control in a cloud, managing your access infrastructure in your data center, in your campus wherever you want to do it. And so that's just one example of how we see our customers network operations team taking advantage of a cloud operating model and basically employing their, their tools where they need them and when they need them. >>So what's the scope of, I hope I'm saying it right. Ice, right. I see. I think it's called ice. What's the scope of that like for instance, turn in effect my or even, you know, address simplify my security approach. >>Absolutely. That's now coming to what is the beauty of the product itself? Yes. What you can do is really is that there's a lot of people talking about else. How do I get to zero trust approach to networking? How do I get to a much more dynamic, flexible segmentation in my infrastructure. Again, whether this is only campus X as well as a data center and Ice help today, you can use this as a point to define your policies and then any connect from there. Right. In this particular case we would instant Ice in the cloud as a software load. You now can connect and say, hey, I want to manage and program my network infrastructure and my data center on my campus, going to the respective control over this DNA Center for campus or whether it is the A. C. I. Policy controller. And so yes, what you get as an effect out of this is a very elegant way to automatically manage in one place. What is my policy and then drive the right segmentation in your network infrastructure? >>zero. Trust that, you know, it was pre pandemic. It was kind of a buzzword. Now it's become a mandate. I wonder if we could talk about right. I mean I wonder if you talk about cloud native apps, you got all these developers that are working inside organizations. They're maintaining legacy apps. They're connecting their data to systems in the cloud there, sharing that data. I need these developers, they're rapidly advancing their skill sets. How is Cisco enabling its infrastructure to support this world of cloud? Native making infrastructure more responsive and agile for application developers? >>Yeah. So, you know, we're going to the top of his visibility, we talked about the operating model, how how our network operators actually want to use tools going forward. Now, the next step to this is it's not just the operator. How do they actually, where do they want to put these tools, how they, how they interact with these tools as well as quite frankly as how, let's say, a devops team on application team or Oclock team also wants to take advantage of the program ability of the underlying network. And this is where we're moving into this whole cloud native discussion, right? Which is really two angles, that is the cloud native way, how applications are being built. And then there is the cloud native way, how you interact with infrastructure. Right? And so what we have done is we're a putting in place the on ramps between clouds and then on top of it we're exposing for all these tools, a P I S that can be used in leverage by standard uh cloud tools or uh cloud native tools. Right. And one example or two examples we always have and again, we're on this journey for a while is both answerable uh script capabilities that exist from red hat as well as uh Ashitaka from capabilities that you can orchestrate across infrastructure to drive infrastructure, automation and what what really stands behind it is what either the networking operations team wants to do or even the ap team. They want to be able to describe the application as a code and then drive automatically or programmatically in situation of infrastructure needed for that application. And so what you see us doing is providing all these capability as an interface for all our network tools. Right. Whether it's this ice that I just mentioned, whether this is our D. C. And controllers in the data center, uh whether these are the controllers in the in the campus for all of those, we have cloud native interfaces. So operator or uh devops team can actually interact directly with that infrastructure the way they would do today with everything that lives in the cloud, with everything how they brought the application. >>This is key. You can't even have the conversation of op cloud operating model that includes and comprises on prem without programmable infrastructure. So that's that's very important. Last question, thomas our customers actually using this, they made the announcement today. There are there are there any examples of customers out there doing this? >>We do have a lot of customers out there that are moving down the past and using the D. D. Cisco high performance infrastructure, but also on the compute side as well as on an exercise one of the customers. Uh and this is like an interesting case. It's Rakuten uh record and is a large tackle provider, a mobile five G. Operator uh in Japan and expanding and is in different countries. Uh and so people something oh, cloud, you must be talking about the public cloud provider, the big the big three or four. But if you look at it, there's a lot of the tackle service providers are actually cloud providers as well and expanding very rapidly. And so we're actually very proud to work together with with Rakuten and help them building a high performance uh, data and infrastructure based on hard gig and actually phone a gig uh to drive their deployment to. It's a five G mobile cloud infrastructure, which is which is uh where the whole the whole world where traffic is going. And so it's really exciting to see this development and see the power of automation visibility uh together with the high performance infrastructure becoming reality and delivering actually services, >>you have some great points you're making there. Yes, you have the big four clouds, your enormous, but then you have a lot of actually quite large clouds. Telcos that are either approximate to those clouds or they're in places where those hyper scholars may not have a presence and building out their own infrastructure. So so that's a great case study uh thomas, hey, great having you on. Thanks so much for spending some time with us. >>Yeah, same here. I appreciate it. Thanks a lot. >>I'd like to thank Cisco and our guests today V Joy, Katie VJ, viscous James and thomas for all your insights into this evolving world of hybrid cloud, as we said at the top of the next decade will be defined by an entirely new set of rules. And it's quite possible things will evolve more quickly because the cloud is maturing and has paved the way for a new operating model where everything is delivered as a service, automation has become a mandate because we just can't keep throwing it labor at the problem anymore. And with a I so much more as possible in terms of driving operational efficiencies, simplicity and support of the workloads that are driving the digital transformation that we talk about all the time. This is Dave Volonte and I hope you've enjoyed today's program. Stay Safe, be well and we'll see you next time.

>> Announcer: From around the globe, it's theCUBE with digital coverage of AWS reInvent 2020 sponsored by Intel, AWS and our community partners. >> Hi, and welcome to theCUBE virtual and our coverage of AWS reInvent 2020. I'm your host Rebecca Knight. Joining me is Sachin Dhoot, he is the vice president for data and platform engineering at Ellie Mae. Thank you so much for coming on theCUBE, Sachin. >> Nice to be here. >> So we are talking today about Ellie Mae's journey towards data monetization. Before we begin though, I want you to give our viewers a little bit, tell our viewers a little bit about yourself and your role at Ellie Mae. >> Sure. So I'm the vice president for data and platform engineering at Ellie Mae. A little bit about Ellie Mae before I talk about myself. So Ellie Mae, which is now part of ICE Mortgage Technology, a division of Intercontinental exchange is the leading cloud based loan origination platform for the mortgage industry. Our technology solutions actually enable lenders to originate more loans, lower origination cost and reduce the time to close. Or when ensuring the highest degree of compliance quality and efficiency. Our mission as we call it here internally is to automate everything 'automatable' for the residential mortgage industry. So that's what we do here. And we take great pride in doing that. >> Everything automatable, I love it. >> Yes. And if you have gone through the mortgage process, you'll see the number of papers you have to sign. And so we are on the journey to automate as much as possible in this. So as part of this, my charter here so I'm the vice president of data and platform engineering. Like I said, I lead and I'm responsible for all AWS based platform and data solutions including our highly secure, scalable data platform and the global, literally. Just to give you a magnitude of how much data we are talking about; so currently Ellie Mae in its platform stores data of nearly 50% of all US for mortgages. So that's the scale which we are talking about and I'm responsible for having the AWS based data platform to support that. >> So in terms of the data monetization journey like most innovations, it starts with a problem. What was the problem that you were trying to solve here? >> Yes, that's a great question. So earlier in our initial design what used to happen is the customers had access to their loan origination system and data in it. And the way they had access to the data was writing some customer SDK applications to actually export our data from their production systems. So this had its own share of challenges. Like for example, if I wrote some inefficient queries to export out the data, since they were acting on the same production database it used to slow down their loan origination system. Plus they did not get access to all of their data. And we had heard it loud and clear from our customers that not only did they need access to the data, but they also wanted us to manage their data. They did not want to get into managing the database or schema changes and all of that. Plus we also had such a rich industry data set. We are talking about 50% of all US home mortgages. So they were also very interested in using that data to get actionable insights about the industry, about their competitive advantages and develop some innovative services on top of it. So those were the challenges which we were trying to solve. >> So what was the original architecture like you're describing what sounds like a very poor experience for Ellie Mae and the lenders themselves. It sounds clunky and cumbersome. And then also leaving a lot on the table because as you said, it was a rich dataset. What was the original architecture? >> So the original architecture was not a cloud-based architecture. We were in our own private data center and every customer had their own database to work with. So, and it wasn't great architecture at that time when the technologies had not evolved. And we had a highly successful product as a result of that but when it came to data it was not a very good experience for them. So why did their loan origination system was working great? The access to the data was not to the extent what we wanted. >> So using best-in-class technologies from AWS tell us a little bit about the new product. >> Yes. So, our journey really started when we heard all of the customer's feedback and the requirements. Then we basically went back to the drawing board. We said, yes, we have a highly successful encompass product in the market, but we also want to solve this problem without affecting their experience with the loan origination system. So that was the challenge which we had taken internally. So what we did was we evaluated quite a bit of cloud providers and technology stacks and the parameters which we had put in that time because of the scale of data was, we needed unlimited scalability and reliability of any provider. We needed a secure data storage including the personally identifiable information protection. So as you can imagine, we deal with loan mortgages, I mean the mortgage and we pretty much have so much of PII data as we call it. Security is on the forefront for us. So we needed a cloud provider which could match up with that expectation. We needed.. >> AWS, was it? >> AWS was definitely it and there were some other parameters which also we were able to check because of that highly scalable and performance data Lake. We needed a big data Lake for this, storage compute separation. We also needed ability to seamlessly import data from any applications internal or external, right? And AWS absolutely gave us all of this. And we did evaluate a lot of cloud vendors and AWS came up on the top. So AWS along with persistent technologies actually helped us with this evaluation and the development of the data platform. >> So tell our viewers a little bit now about data connect and what it is for lenders now. >> Yeah. So what we did was as any cloud technology, we first developed a common platform and then we started building data connect solutions on top of it, right? So we created solutions based on the customer's needs. So one solution which we have is what we call as the data connects future products. In this, they can replicate, customers can replicate their data from the cloud, from their private data Lake into their warehouse, or they can access reports and run analytical queries directly on our warehouse which is again in the cloud. So all the solutions that are available depending on the customer's needs but that is all separate from the loan origination system. So we made sure that we are not impacting that existing business while creating this new solutions in the market. And all of these were built on AWS. >> But you also took things a step further and explored what was possible if you aggregated data from all lenders the resulting being insights. Tell our viewers a little bit about insights and what it allows. >> Absolutely. So that was a very cool product which we came up with. So again, because of the rich data set, which we have, right? We are in the position right now to aggregate the data and come up with actionable insights on top of the data. And so we call this product insights. This is our latest offering from Ellie Mae, again based off AWS and the data platform. So this product gives us information about the industry dreams on how the mortgage industry is going in US. It gives the lenders the ability to compare themselves with their peers and with the industry. So they can actually benchmark themselves and decide whether they are doing great, not great, what do they have to change? And this is all in near real time. So this is not like a month old data and all that. So that's the beauty of this product. >> And what are you hearing from customers? Because as you said, that real-time benchmarking and understanding how they're doing relative to their rivals is a game changer. It is and customers are super excited about it. We just launched this few months back and we are seeing amazing adoption for this product. In fact, just not the adoption side of things, we are also seeing so many new use cases and requirements coming from the customer now that they understand we have such a massive data and this data can scale and it's not impacted their business. They just want to add more and more things to it so that it can solve their problem. So it gives a unique opportunity for us where we can monetize more but we can also help solve lenders problems. >> Right. Helping them solve the challenges that they're facing. Talk a little bit more about the primary benefits of the solution, the unlimited scalability, the fact that it's fully managed, the storage compute separation. Tell our viewers a little bit more about the benefits. So the benefits about the solutions are, the customers or lenders don't have to worry about how it is managed. It is all taken care of. They just how to access it when they need it. It is available on demand. It is available 24/7. In this time, this year has been especially very busy for us where the interest rates have dropped and the loan volume and the loan applications have just gone through the roof. But I'm very proud to say that Ellie Mae stack or, all of the data solutions, and in fact, all of our other products, they are able to scale and they have been able to scale to the record volume this year, all because of how we have designed it using the AWS technology stack. So the customers really benefit. They just need to focus on their business. They don't have to worry about underlying infrastructure or how things are going to scale if their volume is going to go up or not or is there any security issues of that? We take care of all of those things and this is all a self provision just web based access for some of our products. So they don't even have to do a lot of customization to get hold of these products. >> So I want to ask what's next for you. You just referenced the fact that Ellie Mae's incredibly busy with record mortgage applications, of course, companies and people around the globe are still grappling with the COVID-19 pandemic. What are some of the big trends you're seeing and what's next for Ellie Mae in the coming coming year? >> We have a exciting and a very rich roadmap coming up. So as I started this interview, I said, Ellie Mae is now part of ICE mortgage technology, which is a Intercontinental exchange division. So as part of this transition, which happened recently, we also have under our umbrella, two companies called MERS and Simplifile, which actually touch so if you take MERS as an example, it touches close to 80% of US loans for home mortgages. So we have such a unique opportunity now to not only expand our data set, make it more rich, and then come up with more additional use cases which are going to help solve customer's problem and also make them competitive in the market. So we have a lot of good opportunity related to data and I feel a lot confident because of the data platform and the technology stack we to use. We will be able to handle all of those things. >> Sachin, tell our viewers a little bit about the partners that are helping you on this data monetization journey. >> So AWS definitely helped us in the initial parts in evaluating the design and the solution architects came in and worked with us. But along with that, I would definitely want to mention Persistent Technologies. They came up with a lot of good design suggestions on how we should develop the data platform and the solutions on top of it. Those insights product, which I talked about is done along with their help. So I'm very happy with the partnership I have with the Persistent Technologies and AWS. >> Excellent, well, Sachin Dhoot, thank you so much for coming on theCUBE. I really appreciate talking to you >> Same here, nice talking to you. >> Stay tuned for more of theCUBE virtual coverage at AWS reInvent. (upbeat music)

>>from around the globe. It's the Q. With digital coverage >>of exa scale day made possible by Hewlett Packard Enterprise. We're back at the celebration of Exa Scale Day. This is Dave Volant, and I'm pleased to welcome to great guests Brian Dance Berries Here. Here's what The ISS Program Science office at the Johnson Space Center. And Dr Mark Fernandez is back. He's the Americas HPC technology officer at Hewlett Packard Enterprise. Gentlemen, welcome. >>Thank you. Yeah, >>well, thanks for coming on. And, Mark, Good to see you again. And, Brian, I wonder if we could start with you and talk a little bit about your role. A T. I s s program Science office as a scientist. What's happening these days? What are you working on? >>Well, it's been my privilege the last few years to be working in the, uh, research integration area of of the space station office. And that's where we're looking at all of the different sponsors NASA, the other international partners, all the sponsors within NASA, and, uh, prioritizing what research gets to go up to station. What research gets conducted in that regard. And to give you a feel for the magnitude of the task, but we're coming up now on November 2nd for the 20th anniversary of continuous human presence on station. So we've been a space faring society now for coming up on 20 years, and I would like to point out because, you know, as an old guy myself, it impresses me. That's, you know, that's 25% of the US population. Everybody under the age of 20 has never had a moment when they were alive and we didn't have people living and working in space. So Okay, I got off on a tangent there. We'll move on in that 20 years we've done 3000 experiments on station and the station has really made ah, miraculously sort of evolution from, ah, basic platform, what is now really fully functioning national lab up there with, um, commercially run research facilities all the time. I think you can think of it as the world's largest satellite bus. We have, you know, four or five instruments looking down, measuring all kinds of things in the atmosphere during Earth observation data, looking out, doing astrophysics, research, measuring cosmic rays, X ray observatory, all kinds of things, plus inside the station you've got racks and racks of experiments going on typically scores, you know, if not more than 50 experiments going on at any one time. So, you know, the topic of this event is really important. Doesn't NASA, you know, data transmission Up and down, all of the cameras going on on on station the experiments. Um, you know, one of one of those astrophysics observatory's you know, it has collected over 15 billion um uh, impact data of cosmic rays. And so the massive amounts of data that that needs to be collected and transferred for all of these experiments to go on really hits to the core. And I'm glad I'm able toe be here and and speak with you today on this. This topic. >>Well, thank you for that, Bryan. A baby boomer, right? Grew up with the national pride of the moon landing. And of course, we've we've seen we saw the space shuttle. We've seen international collaboration, and it's just always been something, you know, part of our lives. So thank you for the great work that you guys were doing their mark. You and I had a great discussion about exa scale and kind of what it means for society and some of the innovations that we could maybe expect over the coming years. Now I wonder if you could talk about some of the collaboration between what you guys were doing and Brian's team. >>Uh, yeah, so yes, indeed. Thank you for having me early. Appreciate it. That was a great introduction. Brian, Uh, I'm the principal investigator on Space Born computer, too. And as the two implies, where there was one before it. And so we worked with Bryant and his team extensively over the past few years again high performance computing on board the International Space Station. Brian mentioned the thousands of experiments that have been done to date and that there are currently 50 orm or going on at any one time. And those experiments collect data. And up until recently, you've had to transmit that data down to Earth for processing. And that's a significant amount of bandwidth. Yeah, so with baseball and computer to we're inviting hello developers and others to take advantage of that onboard computational capability you mentioned exa scale. We plan to get the extra scale next year. We're currently in the era that's called PETA scale on. We've been in the past scale era since 2000 and seven, so it's taken us a while to make it that next lead. Well, 10 years after Earth had a PETA scale system in 2017 were able to put ah teraflop system on the International space station to prove that we could do a trillion calculations a second in space. That's where the data is originating. That's where it might be best to process it. So we want to be able to take those capabilities with us. And with H. P. E. Acting as a wonderful partner with Brian and NASA and the space station, we think we're able to do that for many of these experiments. >>It's mind boggling you were talking about. I was talking about the moon landing earlier and the limited power of computing power. Now we've got, you know, water, cool supercomputers in space. I'm interested. I'd love to explore this notion of private industry developing space capable computers. I think it's an interesting model where you have computer companies can repurpose technology that they're selling obviously greater scale for space exploration and apply that supercomputing technology instead of having government fund, proprietary purpose built systems that air. Essentially, you use case, if you will. So, Brian, what are the benefits of that model? The perhaps you wouldn't achieve with governments or maybe contractors, you know, kind of building these proprietary systems. >>Well, first of all, you know, any any tool, your using any, any new technology that has, you know, multiple users is going to mature quicker. You're gonna have, you know, greater features, greater capabilities, you know, not even talking about computers. Anything you're doing. So moving from, you know, governor government is a single, um, you know, user to off the shelf type products gives you that opportunity to have things that have been proven, have the technology is fully matured. Now, what had to happen is we had to mature the space station so that we had a platform where we could test these things and make sure they're gonna work in the high radiation environments, you know, And they're gonna be reliable, because first, you've got to make sure that that safety and reliability or taken care of so that that's that's why in the space program you're gonna you're gonna be behind the times in terms of the computing power of the equipment up there because, first of all and foremost, you needed to make sure that it was reliable and say, Now, my undergraduate degree was in aerospace engineering and what we care about is aerospace engineers is how heavy is it, how big and bulky is it because you know it z expensive? You know, every pound I once visited Gulfstream Aerospace, and they would pay their employees $1000 that they could come up with a way saving £1 in building that aircraft. That means you have more capacity for flying. It's on the orders of magnitude. More important to do that when you're taking payloads to space. So you know, particularly with space born computer, the opportunity there to use software and and check the reliability that way, Uh, without having to make the computer, you know, radiation resistance, if you will, with heavy, you know, bulky, um, packaging to protect it from that radiation is a really important thing, and it's gonna be a huge advantage moving forward as we go to the moon and on to Mars. >>Yeah, that's interesting. I mean, your point about cots commercial off the shelf technology. I mean, that's something that obviously governments have wanted to leverage for a long, long time for many, many decades. But but But Mark the issue was always the is. Brian was just saying the very stringent and difficult requirements of space. Well, you're obviously with space Born one. You got to the point where you had visibility of the economics made sense. It made commercial sense for companies like Hewlett Packard Enterprise. And now we've sort of closed that gap to the point where you're sort of now on that innovation curve. What if you could talk about that a little bit? >>Yeah, absolutely. Brian has some excellent points, you know, he said, anything we do today and requires computers, and that's absolutely correct. So I tell people that when you go to the moon and when you go to the Mars, you probably want to go with the iPhone 10 or 11 and not a flip phone. So before space born was sent up, you went with 2000 early two thousands computing technology there which, like you said many of the people born today weren't even around when the space station began and has been occupied so they don't even know how to program or use that type of computing. Power was based on one. We sent the exact same products that we were shipping to customers today, so they are current state of the art, and we had a mandate. Don't touch the hardware, have all the protection that you can via software. So that's what we've done. We've got several philosophical ways to do that. We've implemented those in software. They've been successful improving in the space for one, and now it's space born to. We're going to begin the experiments so that the rest of the community so that the rest of the community can figure out that it is economically viable, and it will accelerate their research and progress in space. I'm most excited about that. Every venture into space as Brian mentioned will require some computational capability, and HP has figured out that the economics air there we need to bring the customers through space ball into in order for them to learn that we are reliable but current state of the art, and that we could benefit them and all of humanity. >>Guys, I wanna ask you kind of a two part question. And, Brian, I'll start with you and it z somewhat philosophical. Uh, I mean, my understanding was and I want to say this was probably around the time of the Bush administration w two on and maybe certainly before that, but as technology progress, there was a debate about all right, Should we put our resource is on moon because of the proximity to Earth? Or should we, you know, go where no man has gone before and or woman and get to Mars? Where What's the thinking today, Brian? On that? That balance between Moon and Mars? >>Well, you know, our plans today are are to get back to the moon by 2024. That's the Artemus program. Uh, it's exciting. It makes sense from, you know, an engineering standpoint. You take, you know, you take baby steps as you continue to move forward. And so you have that opportunity, um, to to learn while you're still, you know, relatively close to home. You can get there in days, not months. If you're going to Mars, for example, toe have everything line up properly. You're looking at a multi year mission you know, it may take you nine months to get there. Then you have to wait for the Earth and Mars to get back in the right position to come back on that same kind of trajectory. So you have toe be there for more than a year before you can turn around and come back. So, you know, he was talking about the computing power. You know, right now that the beautiful thing about the space station is, it's right there. It's it's orbiting above us. It's only 250 miles away. Uh, so you can test out all of these technologies. You can rely on the ground to keep track of systems. There's not that much of a delay in terms of telemetry coming back. But as you get to the moon and then definitely is, you get get out to Mars. You know, there are enough minutes delay out there that you've got to take the computing power with you. You've got to take everything you need to be able to make those decisions you need to make because there's not time to, um, you know, get that information back on the ground, get back get it back to Earth, have people analyze the situation and then tell you what the next step is to do. That may be too late. So you've got to think the computing power with you. >>So extra scale bring some new possibilities. Both both for, you know, the moon and Mars. I know Space Born one did some simulations relative. Tomorrow we'll talk about that. But But, Brian, what are the things that you hope to get out of excess scale computing that maybe you couldn't do with previous generations? >>Well, you know, you know, market on a key point. You know, bandwidth up and down is, of course, always a limitation. In the more computing data analysis you can do on site, the more efficient you could be with parsing out that that bandwidth and to give you ah, feel for just that kind of think about those those observatory's earth observing and an astronomical I was talking about collecting data. Think about the hours of video that are being recorded daily as the astronauts work on various things to document what they're doing. They many of the biological experiments, one of the key key pieces of data that's coming back. Is that video of the the microbes growing or the plants growing or whatever fluid physics experiments going on? We do a lot of colloids research, which is suspended particles inside ah liquid. And that, of course, high speed video. Is he Thio doing that kind of research? Right now? We've got something called the I s s experience going on in there, which is basically recording and will eventually put out a syriza of basically a movie on virtual reality recording. That kind of data is so huge when you have a 360 degree camera up there recording all of that data, great virtual reality, they There's still a lot of times bringing that back on higher hard drives when the space six vehicles come back to the Earth. That's a lot of data going on. We recorded videos all the time, tremendous amount of bandwidth going on. And as you get to the moon and as you get further out, you can a man imagine how much more limiting that bandwidth it. >>Yeah, We used to joke in the old mainframe days that the fastest way to get data from point a to Point B was called C Tam, the Chevy truck access method. Just load >>up a >>truck, whatever it was, tapes or hard drive. So eso and mark, of course space born to was coming on. Spaceport one really was a pilot, but it proved that the commercial computers could actually work for long durations in space, and the economics were feasible. Thinking about, you know, future missions and space born to What are you hoping to accomplish? >>I'm hoping to bring. I'm hoping to bring that success from space born one to the rest of the community with space born to so that they can realize they can do. They're processing at the edge. The purpose of exploration is insight, not data collection. So all of these experiments begin with data collection. Whether that's videos or samples are mold growing, etcetera, collecting that data, we must process it to turn it into information and insight. And the faster we can do that, the faster we get. Our results and the better things are. I often talk Thio College in high school and sometimes grammar school students about this need to process at the edge and how the communication issues can prevent you from doing that. For example, many of us remember the communications with the moon. The moon is about 250,000 miles away, if I remember correctly, and the speed of light is 186,000 miles a second. So even if the speed of light it takes more than a second for the communications to get to the moon and back. So I can remember being stressed out when Houston will to make a statement. And we were wondering if the astronauts could answer Well, they answered as soon as possible. But that 1 to 2 second delay that was natural was what drove us crazy, which made us nervous. We were worried about them in the success of the mission. So Mars is millions of miles away. So flip it around. If you're a Mars explorer and you look out the window and there's a big red cloud coming at you that looks like a tornado and you might want to do some Mars dust storm modeling right then and there to figure out what's the safest thing to do. You don't have the time literally get that back to earth have been processing and get you the answer back. You've got to take those computational capabilities with you. And we're hoping that of these 52 thousands of experiments that are on board, the SS can show that in order to better accomplish their missions on the moon. And Omar, >>I'm so glad you brought that up because I was gonna ask you guys in the commercial world everybody talks about real time. Of course, we talk about the real time edge and AI influencing and and the time value of data I was gonna ask, you know, the real time, Nous, How do you handle that? I think Mark, you just answered that. But at the same time, people will say, you know, the commercial would like, for instance, in advertising. You know, the joke the best. It's not kind of a joke, but the best minds of our generation tryingto get people to click on ads. And it's somewhat true, unfortunately, but at any rate, the value of data diminishes over time. I would imagine in space exploration where where you're dealing and things like light years, that actually there's quite a bit of value in the historical data. But, Mark, you just You just gave a great example of where you need real time, compute capabilities on the ground. But but But, Brian, I wonder if I could ask you the value of this historic historical data, as you just described collecting so much data. Are you? Do you see that the value of that data actually persists over time, you could go back with better modeling and better a i and computing and actually learn from all that data. What are your thoughts on that, Brian? >>Definitely. I think the answer is yes to that. And, you know, as part of the evolution from from basically a platform to a station, we're also learning to make use of the experiments in the data that we have there. NASA has set up. Um, you know, unopened data access sites for some of our physical science experiments that taking place there and and gene lab for looking at some of the biological genomic experiments that have gone on. And I've seen papers already beginning to be generated not from the original experimenters and principal investigators, but from that data set that has been collected. And, you know, when you're sending something up to space and it to the space station and volume for cargo is so limited, you want to get the most you can out of that. So you you want to be is efficient as possible. And one of the ways you do that is you collect. You take these earth observing, uh, instruments. Then you take that data. And, sure, the principal investigators air using it for the key thing that they designed it for. But if that data is available, others will come along and make use of it in different ways. >>Yeah, So I wanna remind the audience and these these these air supercomputers, the space born computers, they're they're solar powered, obviously, and and they're mounted overhead, right? Is that is that correct? >>Yeah. Yes. Space borne computer was mounted in the overhead. I jokingly say that as soon as someone could figure out how to get a data center in orbit, they will have a 50 per cent denser data station that we could have down here instead of two robes side by side. You can also have one overhead on. The power is free. If you can drive it off a solar, and the cooling is free because it's pretty cold out there in space, so it's gonna be very efficient. Uh, space borne computer is the most energy efficient computer in existence. Uh, free electricity and free cooling. And now we're offering free cycles through all the experimenters on goal >>Eso Space born one exceeded its mission timeframe. You were able to run as it was mentioned before some simulations for future Mars missions. And, um and you talked a little bit about what you want to get out of, uh, space born to. I mean, are there other, like, wish list items, bucket bucket list items that people are talking about? >>Yeah, two of them. And these air kind of hypothetical. And Brian kind of alluded to them. Uh, one is having the data on board. So an example that halo developers talk to us about is Hey, I'm on Mars and I see this mold growing on my potatoes. That's not good. So let me let me sample that mold, do a gene sequencing, and then I've got stored all the historical data on space borne computer of all the bad molds out there and let me do a comparison right then and there before I have dinner with my fried potato. So that's that's one. That's very interesting. A second one closely related to it is we have offered up the storage on space borne computer to for all of your raw data that we process. So, Mr Scientist, if if you need the raw data and you need it now, of course, you can have it sent down. But if you don't let us just hold it there as long as they have space. And when we returned to Earth like you mentioned, Patrick will ship that solid state disk back to them so they could have a new person, but again, reserving that network bandwidth, uh, keeping all that raw data available for the entire duration of the mission so that it may have value later on. >>Great. Thank you for that. I want to end on just sort of talking about come back to the collaboration between I S s National Labs and Hewlett Packard Enterprise, and you've got your inviting project ideas using space Bourne to during the upcoming mission. Maybe you could talk about what that's about, and we have A We have a graphic we're gonna put up on DSM information that you can you can access. But please, mark share with us what you're planning there. >>So again, the collaboration has been outstanding. There. There's been a mention off How much savings is, uh, if you can reduce the weight by a pound. Well, our partners ice s national lab and NASA have taken on that cost of delivering baseball in computer to the international space station as part of their collaboration and powering and cooling us and giving us the technical support in return on our side, we're offering up space borne computer to for all the onboard experiments and all those that think they might be wanting doing experiments on space born on the S s in the future to take advantage of that. So we're very, very excited about that. >>Yeah, and you could go toe just email space born at hp dot com on just float some ideas. I'm sure at some point there'll be a website so you can email them or you can email me david dot volonte at at silicon angle dot com and I'll shoot you that that email one or that website once we get it. But, Brian, I wanna end with you. You've been so gracious with your time. Uh, yeah. Give us your final thoughts on on exa scale. Maybe how you're celebrating exa scale day? I was joking with Mark. Maybe we got a special exa scale drink for 10. 18 but, uh, what's your final thoughts, Brian? >>Uh, I'm going to digress just a little bit. I think I think I have a unique perspective to celebrate eggs a scale day because as an undergraduate student, I was interning at Langley Research Center in the wind tunnels and the wind tunnel. I was then, um, they they were very excited that they had a new state of the art giant room size computer to take that data we way worked on unsteady, um, aerodynamic forces. So you need a lot of computation, and you need to be ableto take data at a high bandwidth. To be able to do that, they'd always, you know, run their their wind tunnel for four or five hours. Almost the whole shift. Like that data and maybe a week later, been ableto look at the data to decide if they got what they were looking for? Well, at the time in the in the early eighties, this is definitely the before times that I got there. They had they had that computer in place. Yes, it was a punchcard computer. It was the one time in my life I got to put my hands on the punch cards and was told not to drop them there. Any trouble if I did that. But I was able thio immediately after, uh, actually, during their run, take that data, reduce it down, grabbed my colored pencils and graph paper and graph out coefficient lift coefficient of drag. Other things that they were measuring. Take it back to them. And they were so excited to have data two hours after they had taken it analyzed and looked at it just pickled them. Think that they could make decisions now on what they wanted to do for their next run. Well, we've come a long way since then. You know, extra scale day really, really emphasizes that point, you know? So it really brings it home to me. Yeah. >>Please, no, please carry on. >>Well, I was just gonna say, you know, you talked about the opportunities that that space borne computer provides and and Mark mentioned our colleagues at the I S s national lab. You know, um, the space station has been declared a national laboratory, and so about half of the, uh, capabilities we have for doing research is a portion to the national lab so that commercial entities so that HP can can do these sorts of projects and universities can access station and and other government agencies. And then NASA can focus in on those things we want to do purely to push our exploration programs. So the opportunities to take advantage of that are there marks opening up the door for a lot of opportunities. But others can just Google S s national laboratory and find some information on how to get in the way. Mark did originally using s national lab to maybe get a good experiment up there. >>Well, it's just astounding to see the progress that this industry is made when you go back and look, you know, the early days of supercomputing to imagine that they actually can be space born is just tremendous. Not only the impacts that it can have on Space six exploration, but also society in general. Mark Wayne talked about that. Guys, thanks so much for coming on the Cube and celebrating Exa scale day and helping expand the community. Great work. And, uh, thank you very much for all that you guys dio >>Thank you very much for having me on and everybody out there. Let's get the XO scale as quick as we can. Appreciate everything you all are >>doing. Let's do it. >>I've got a I've got a similar story. Humanity saw the first trillion calculations per second. Like I said in 1997. And it was over 100 racks of computer equipment. Well, space borne one is less than fourth of Iraq in only 20 years. So I'm gonna be celebrating exa scale day in anticipation off exa scale computers on earth and soon following within the national lab that exists in 20 plus years And being on Mars. >>That's awesome. That mark. Thank you for that. And and thank you for watching everybody. We're celebrating Exa scale day with the community. The supercomputing community on the Cube Right back

>>from around the globe. It's the Cube with digital coverage of CONMEBOL. Future Ready 2020. Brought to you by combo. Hi, I'm Stew Minuteman. And this is the Cube's coverage of Con Volt Future ready event Welcoming back to the program. Fresh off the keynote stage. Sanjay Mirchandani. He's the CEO of Con Volt. Sanjay. Nice job on the keynote. And thanks so much for joining us. >>Thanks to Good to see you again. >>Nice to see you too. So, Sanjay, about a year and 1/2 into your journey with Conn Volt, you took over. And you know what it looks like? You've almost completely refreshed the portfolio there. Start a little bit, you know, future. Ready. Tell us how you're getting Conn Volt and its customers ready to be prepared for what happened today as well as the >>right. So, you know, we've we've given visit The past 18 months, have flown by in the past four or five. Even faster. Um, the change. You know, the change that we've had all deal with us as organizations has been tremendous. We've been hard at work. When I came on board, I should have talked about how we were setting out to simplify, innovate and execute all three of those pillars and, ah, future ready, which I love as a term completely embodies what I think the work we've been up to and what the world needs today, which is really getting it ready for whatever's next. And, you know, and it's coming together of innovation, simplification and and hopefully you'll agree some good execution to bring it all together. Yeah, so we've been busy. >>Sanjay, you talked a bit about just the moment in time that we're in. Wonder if you could bring us inside. You know your customers. So there's certain things that we saw for a couple of months. People put a pause on. Other things absolutely have been accelerated. We talk to customers about their adoption of cloud, you know, digital transformation. It's one of those things. That boy, I hope I'm through some of those or you know, can be as agile as possible. But, you know, what do you hearing specifically from our customer base and how they're dealing with things? >>You know, Cto, I touched a little bit on that during my keynote. And you know this this this this time that we're in has really caused, I think a couple of shifts. The first structural shift was Oh, hey, this thing is here to stay and let's get our employees Working and productive and keep the business is running and keeping them safe and everything else. That first shift happened right on. Honest about What was it that March, April and businesses small and big had to figure out how to take go from their their their operating model into, ah, remote. With the remote model, you re prioritize and you thought through what was important at the time and what it was was really getting laptops into the hands of your employees, getting them safe into their working environment, making sure your business processes leaning in that direction. You could take care of your customers. And so that was sort of the first structural faith, the second structural failures. Okay, how do we really drive productivity? One of the new priorities. What do we need to do, what you want to invest in? What do you want to pull back from? And from our vantage point from A from a technology and data point of view, what we're hearing is the themes that if I had a paraphrase of conversations I have with CIOs, it's NGOs. It's really around a simplification. This is a This is a great time to really simplify and, you know, and make sure that you're working with the tried and tested. This is not the time to experiment. This is not the time for esoteric. This is really about simplifying and working with the tried and tested. The second is really about focusing on skills, you know, this is you need you need to be able to leverage, and you need to be able to bring productivity from the from the people that you have an I t. And really focus around that that's, you know, that sometimes for gotten, you know that I like to call them. The unsung heroes of technology has just been pushed into their homes. They're now doing their jobs, longer hours, tougher scenarios. They have no access to their data centers. So it's over. So let's think about skills and the third, you know, the third thing, really that has been propelled into this conversation is cloud. So if you were on a journey, you're off the journey you need to get there quickly, okay? And you need to really newly leverage a light touch, low touch, remote sort of capability. A So fast is you can't call a digital transformation. Call it whatever you'd like to say. But it is about truly leveraging the cloud in a way that that was no longer, you know, a one year, two year three applying. You just have to bring it right to those kinds of things we're hearing and dealing with. >>Yeah, it's so important, Sanjay. Especially that simplicity piece. You know, I remember a few years ago there were certain customers that were adopting cloud, and it was the reminder. Oh, hey, your data protection in your security, you need to make sure you take care of that when you go to the cloud. And unfortunately, you know, some of the people that are now accelerating things you have to quickly say Oh, wait. I can't work this in a few months. I need to take care of this upfront, so help us understand a little bit. You know, the announcements that you've made. How are you making sure that you're ready for customers? The simplicity that they need to take advantage of the innovation and opportunity that the cloud on solutions provider >>absolutely and and make a mistake for me to. Simplification is not just the technology is easy to use, even though that is a big part of what we're working on and working and delivering through these announcements. But we've also got to make sure that the partnerships that we that we that we have lend themselves to what customers need, you know, engineered better its source not in the field, you know, and then and then the ecosystem to make the technology available and consumed commercially in the way that customers would like to keep that simple to. But today, if I just focus on the portfolio, you know, we've we've you could say we've completely rebuilt this incredible stack of technology that we've built this company out and, you know, and we weave in a nutshell. What we've done is announced A. We've taken our backup and recovery suite and be saying we've got a new company, backup and recovery product. We've got a brand new con Volt disaster recovery product. You can get them together as a unit Azaz the complete backup and recovery suite, if you would. So that's one big set of offerings. The second and you know the second is is we bought Hedvig sort of next generation software defined storage technology company last year, and we've been feverishly work quietly at work, integrating Hedvig into calm bolt not just as a company, but in the technology and our new hyper scale technology. Hyper scale. ECs is the embodiment of those two things coming together, the best of data protection from Con Volt and the best storage subsystem to drive that from Hedvig, also from console. So the two come together on all of this technology, whether it's the suite that I mentioned or the hyper scaler, all of it you can. You can mix and match any way you want with it with a world class user interface or user interfaces if you want command lines. If you want AP ICE will keep it open, all of it to you. In addition, we've got announcements or under Activate Suite on. Recently, we talked about our partnership with Microsoft with the metallic azure sort of combination for customers. So it's ah, it's a left to right set of announcement with simplification threatened right through it. >>Sanjay, you mentioned partnerships. Ah, a little bit before the show, you had, of course, the extended partnership with Microsoft with metallic. Maybe give us just a little bit more color about you know how, Con Volt make sure their position and working closely with those hyper scale >>hours. Yeah, you know, and we work with all the hyper scaler. So, you know, there we are probably the most prevalent data protection technology, if you would in the public cloud. And most of the way we talk about over an exabyte that we've helped customers, right, that the cloud is just one data point we've we've been, you know, seen is from the outside in as being the transport capability across across hybrid cloud scenarios. The partnership, the partnership with Microsoft and Microsoft Azure in particular, is the coming together of these things because customers, when we talk to customers and Microsoft office of customers be here from them, they want the ability to be, if you know, as they get more prevalent in the cloud as their workloads get more more pervasive in the cloud, they want to make sure that the same industrial strength data protection cloud in that they had well while they were on prayer for primarily on Prem. Our solutions are completely hybrid. And so the partnership really brings together again. You know, technology that's engineered better together, our data protection and their their cloud best in class our channels working, working together and making sure that it's easy for customers to work work with us. And we're available on the azure marketplace and our field forces also aligned around it. So it's again a 3 60 kind of conversation that we have with customers as much as much of today's announcements. >>Yeah, Sanjay, you talked about the hyper scale er's. You mentioned that the integration of the Hedwig Solution work with Dev Ops and really the cloud native type solutions. Of course, one of the things everybody's looking at when you were hired to this job is you've got background in the automation in developer world. So you know, how is that scene in the update? The portfolio really that embracing of cloud native and develop our environments? >>Cloud without automation is not a cloud, right? It's just it's just it's just infrastructure that's put somewhere else. It's deep, deep degrees of it off automation that really bring cloud to life. Right? And I was fortunate that have been in the Dev ops world for a while in a market leading with marketing product. And I was very pleasantly surprised when I when I came to convert and sell the deep degrees of automation and work flows that are core technology had, with Hedvig acquisition being a platform layer being the storage layer that is multi protocol and appeals incredibly to Dev Ops engineers because everything in the product you know is call a bill through an A p I for a set of AP eyes. It's it's Richard's got work flows and and it's multi critical. So whether you're using VMC or you're building the next generation container applications or you're just using object storage, it doesn't matter. We can mix and match it across, you know, private and public cloud environments, and it's all culpable and it's all programmable. It's all automated on as much as you want >>it. All right, So, Sanjay, I know we can't talk too much about Financial Piece is where we are in the quarter. But one of the things Dave Volante and I were discussing and looking at Kahn Volt. You know, there's some good data, you know, especially if you look at win rates against some of the some of the newer players in this space that the data that we have from ET R was showing, you know, increased win rates for Con Volt. Just could you give us a little bit of your competitive landscape view you talked about? Customers don't want to take too much risk, you know? How do you balance between being, you know, a company with a large install base? But you want to be, you know, more modern? >>Oh, yeah. And you know, the use cases we're talking about. The cloud that we're seeing those leaders are today's use cases, not yesterday's use cases, and we're winning in the base is the fact that we respect that customers are coming from Okay, There's a lot of stuff that runs that business that is still good. That isn't in the cloud that they're they're working their plants journey from that to something else as well. That's where we're leading in areas where they have it in the public cloud, and we always like to stay 1 to 2 steps ahead of the hard problems our customers going to encounter. So our portfolio is is absolutely cloud ready. Our portfolio is rich in that in that capability, and we're not slowing down. You know, we're winning because we have the breath of technology that we support. Both, You know, source source data that customers want o protect and target scenarios where maybe the hyper scaler or anything else where customers want to take it. And the flexibility, the second thing. And if you heard the interview I did with Run from from Johns Hopkins, it's the optimization off our technology around each of those cloud scenarios that gives our customer's true, you know, true value around the compute and storage decisions they have to make. And we helped them make through deep through deep degrees of AI and ML built in. So so it's not just about moving bits. It's about optimizing all of that on the entire life cycle of that data, from the point it's created to the point. >>Excellent. Well, Sunday. Want to let you have the final word? Give us what you want customers to have as the take away from today's future. Ready event? >>Sure. So, first of all, I wanted to, you know, I want to thank all our our audience here, our customers for being with us. It's being with us as a customer, being looking at us as a prospect for technology. We are investing like, you know, we've invested over a $1,000,000,000 over over a period of time as a company in data protection, and we're taking that to a whole new level with the innovations that we're bringing to the table. So, you know, we truly believe that the journey with as it pertains to data the journey to the cloud requires you to be able to think through the life cycle from storing, protecting, optimizing and using that data all the way through. And our solutions can be used independently. Best of class across each of them or together better together. And, you know, we I I urge you to take a few minutes and look at some of the some of the great innovations we've brought to table and rest assured that everything we're doing eyes with hybrid cloud in mind and is it is completely cloud optimized. >>All right. Well, Sanjay Mirchandani. Thank you so much for joining us. Congratulations to you and the team on the work on the updates. Definitely. Look forward to hearing more in the future. >>Thanks. Too good to be here. >>Alright, stay tuned. We've got more from Con vault Future ready on student a man. And thank you for watching the Cube. Yeah, yeah.

>> From the SiliconANGLE Media office in Boston, Massachusetts. It's theCUBE. (upbeat music) Now, here's your host Dave Vellante. >> Hi everybody, welcome to this Cube Conversation on data protection. You know, I've been reporting for the last several months that spending on storage is reverting back to pre-2018 levels, but at the same time, it's not falling off a cliff. Now, one area of storage that is still very, very strong is the data protection segment. In the past 18 months, we've seen about a half a billion dollars in venture funding come into the market. We've just seen a big multi-billion dollar exit. And backup specifically in data protection, data management generally is where all the action is right now. And one of the leaders in data protection is Dell EMC. The company has the largest share of the market and the new entrants, believe me, want a piece of their pie. But anyone who follows this company knows that the firm is not likely to give up it's turf very easily. So much is changing in the market today. And I want to understand how Dell EMC's data protection division is responding to both the competitive threats and the changing market dynamics. With me are two experts from Dell EMC to address these issues. Nelson Hsu is Director of Solutions, Product Marketing for the data protection division at Dell EMC, and Colm Keegan is Senior Consultant, Product Marketing at Dell EMC. Gents, welcome to theCUBE. Great to see you again. >> Thank you for having us. >> Thanks, Dave. >> So you heard my intro. You guys are the leader. You got the biggest market share. You got all the upstarts coming at ya. What's your response? >> Want me to take that? >> Sure. >> Yeah. It's interesting, so we were talking about this before we came on set, you know and often times they want to poke holes at us 'cause you know we're perceived as being the old timers, or the stodgy ones of the group out there. And play a little jiu jitsu, you move in say you know well time in market counts for something. You know we've been solving data protection challenges for customers for literally decades now. You know and so, water under the boat and knowing the experience that we've derived from that allows us to bring solutions that are mature, that are proven. What we're doing is we're taking those proven solutions and pairing them with modern capabilities. So that, you know we look at it and say, hey, look, Mr. Customer. You have significant data protection challenges today because, as you said, the world's changing. It's changing rapidly. We can help you address those while also sowing the seeds for the foundation for the future. So we think that's a compelling message and we think that while some of our competitors, in particular the upstarts, have had some interesting things to say, big picture-wise, they don't know what they don't know. 'Cause they just don't have the time in the market. Their solutions are also largely absent upmarket, you know, when you look at the enterprise. So we're comfortable. We think we're in a very good spot right now. >> So cloud obviously was the huge mega trend of the past decade. You guys said from the beginning, it's going to be a hybrid world. Some of that was we hope it's going to be a hybrid world. Well you were right, it's a hybrid world. So how is cloud, hybrid cloud affecting your customer decisions around data protection, and how are you responding? >> Well, you know, there's no doubt that the growth in cloud and the growth in hybrid cloud is real. And it's there today. As we look, and as Colm mentioned, we've been protecting data across the enterprise, across the edge and in the cloud, and that growth continues. So today, we have over 1,000 customers that we're protecting their data in the cloud. To the tone of over 2.7 exabytes of data protected in the cloud by Dell EMC data protection. So there is absolutely no doubt that that growth is there. We have a lot of innovation that we're driving on, both in various ares of cloud native, cyber security and deep integration. >> Okay, so that's good, 1,000 customers. That's a pretty good observation space. But when you think about hybrid, what I think when I talk to customers is they want that same exact cloud experience. They don't want to have to context switch. They don't want to have to buy different platforms. So how are you specifically addressing that customer requirement? >> So there's a couple ways we look at that, right? For our customers, simplicity is very key in ease of use. So that's one of our core tenants as we go across both the edge, the core and the cloud. And the other aspect of that is consistency. So giving them and allowing them to use the tools that they know today to be able to protect their data, wherever that data resides. So with the cloud, with cloud native, your data becomes very, very distributed. And you have to be able to see all that data, and control and manage that data. So the whole aspect around cloud data management has now risen to the top as a major concern. We do that in a great way in a sense that we both have a hybrid strategy and a lot of that is working with Dell Technologies cloud. And it's based upon VMware. And so we have a very good deep relationship with VMware to utilize their tools that our customers use today. Whether it be vSphere or vcontrol that they can manage their data protection from one console, from one environment itself. >> Yeah, Dave, I think when you look at the split today, the latest cut of research is that roughly 52% of VM's are in the cloud, and 48 percent are on-prems so it's already hybrid, and as Nelson said, it's largely predicated on VMware. So as organizations start consuming cloud they're going to go with the platform that they've been operating under for years now. So it'll be VMware. We've always had very tight integration with VMware. We have a very strong partnership with them. And that's both on the existing portfolio as well as the agile portfolio that we're building out today under PowerProtect. So as that hybrid world evolves for the customers obviously we want to make sure they're protected from a virtual machine standpoint. And make that, as Nelson said, very simple for them because the last thing customers need is complexity particularly as their environments are becoming inherently more complex. Because now you look at most enterprises today, they're going to have a mix of workloads. It's physical, it's virtual, containers are unaccounted for. It's cloud native apps, it's SaaS. You know we were talking earlier about multi-clouds. Oftentimes it just kind of came up organically and now you've got this huge distribution of workloads and oftentimes, customers have been just sort of reactive to that. In other words, let me find a way to protect that and I'll worry about the details later. We're looking at that and saying, we have the portfolio to help you protect all your workloads, and as importantly, we'll help consolidate the management in that environment. It's going to start with VMware, but then longer term we're planning for things like a SaaS control plane so that we can give you a complete view of that environment and allow you to assign the policies you need in terms of SLA's, in terms of compliance. You're basically hitting all the security, hitting all the key things that you need and so directionally we think starting with VMware and building from there is probably the most realistic way we can get customers protected from a hyper cloud. >> So the vision is a single point of control that is SaaS based that lives in the cloud or lives wherever you want it to live? >> Right, it can be either. >> So one of our core tendencies here, right, is that we want and deliver the ability to protect our customer's data wherever it resides. Whether it's edge, core or cloud. >> So sticking on cloud for a second, and then sort of segue into the VMware conversation that I want to have is VMware is the sort of linchpin of your multi-cloud strategy. That makes a lot of sense. VMware is going to be a leader, if not the leader in multi-cloud. We'll see how that all shakes out. It's kind of jump ball right now but VMware is in pretty good position with 500,000 customers. But your perspective on cloud is different than say, take an AWS cloud provider, it's a place. Put your data in my cloud. You guys are talking about the experience. And that's really what you're trying to drive with VMware, whether is Ron-prem, whether it's in Google, Azure, AWS, wherever. The cloud, you name it. Is that the right way to think about your strategy? Specifically as it relates to multi-cloud. >> Yeah, so I think on the area of multi-cloud, it is a multi-cloud world. Years ago I was in a SaaS startup and we had customers that were looking to deploy to the cloud. And then that was the question. Okay, do we hedge on multi-cloud or not? As a SaaS provider, we actually implemented on both AWS and Azure at the time. Which became relevant, because now our customers are asking us, yes, my primary is with this particular hyper scaler. But do you also support this second hyper scaler? So the reality started to evolve. And so for us, yes, VMware is a very strategic aspect and partner with us, especially with Dell Technologies cloud. But we also have a multi-cloud relationship with AWS, with Azure and with Google. >> Yes, so the compatibility matrix, if you will, applies now to the cloud. >> Absolutely, absolutely. So now it's having that feature and functionality across multiple clouds. >> One of the things we obviously paid attention to is Project Tanzu with inside of VMware. All around bringing kind of Kubernetes and VMware together. How does that affect data protection? >> Well, I think it affects data protection in the sense that addressing the entire aspect of still your data is distributed now. And it's going to grow that way. I think that we've seen numbers upwards of 70% of applications will be container based. Some of that will be going forward to 2022 where there'll be multiple production applications that will be container based. I think what Tanzu will bring to the table is a cohesive way to manage and control that environment itself. >> Okay, and so maybe we could sort of drill into that a little bit. Containers, it's becoming more obvious that people want to persist some of that data. It's largely stateless, but you've got to figure out how to recover. So do you have solutions in that space, is that sort of more road mapping? You can talk about that a little bit. >> No, absolutely. So definitely we have concrete solutions with our Dell EMC PowerProtect data manager for Kubernetes. It's actually one of the first that was in the market to support cloud native environments. >> It is the first. >> Yeah, the first offering out there to support Kubernetes. And so the aspect there is that as cloud native has moved from DevOps, and now into production in the mission critical applications, now becomes the aspect of originally the DNA of DevOps was my data doesn't have to be persistent. Now when you move into a mission critical environment, you're entire environment needs to be protected. And to be able to bring those workloads back up should anything happen and to be able to protect that data that is critical to those workloads. >> Okay, and so you're saying you're first, and you see this as a differentiator in the marketplace, or is everybody going to have this, or it's one of these confusing ice cream cone of solutions. So why you guys? What's your big differentiation? Let's stick to containers. I have the same questions sort of overall come back to that. >> So great question, and the matter of fact is that with our experience across the edge, core and cloud, Kubernetes and containers will be prevalent throughout. And it'll be the way that applications will be developed. It's meeting the demands of the business and being agile. And I think that with our ability internally that would move to that agile emotion. We have that ability to address the customer's needs especially in the cloud native Kubernetes space. >> I think going back to what you said too about VMware, certainly our partnership there is differentiated. We even heard some echos of that during Vmworld. Pat Gelsinger usually doesn't give call outs on the main stage very frequently. And he said that they were working with us as a best-in-class partner for data protection with Tanzu. And so there is a very tight partnership there, so if I'm a customer and I'm looking at containers, I'm probably going to want to do it within the framework of VMware to start with. But it's important to point out that we're also not dependent on VMware. So we can still deliver protection for Kubernetes containers outside of say the VMware management domain. But I would say from a differentiation standpoint there are some real tight partnering going on to make these capabilities mature. >> Well it helps that your CEO owns 80% of the company. (laughing) But it's an interesting point you're making because again, dial back 10 years ago, VMware had much more of a Switzerland strategy under Maritz, almost to, at the time, EMC's detriment. I think Michael Dell is very clearly, as is Jeff Clarke, said look, we're going to do more integration. And Pat Gelsinger has been, look, I love all my partners. It's true but we're entering sort of a new era. And that integration is key, you know, again, because of the ownership structure, and your long history there. It's got to confer some advantages in the marketplace. >> Yeah, and he's also got to remove some of the headwinds to adoption of VMware cloud. And data protection, as we discussed often times can be a headwind if customers are concerned that they're not going to be able to protect their data, chances are they're going to stand pat for a while. So I mean you need to find ways to take some of those objections off the table. >> Yeah, and not to take anything away from your competitors. Look, it's an open API world, and again, people are going to compete. But at the end of the day this stuff is still really complex and if you can do some core engineering together it's definitely an advantage. Let's talk a little bit about cyber. I often say it's become a board level topic. It's not a matter of if, it's a matter of when. SecOps teams are overtaxed. I think I put out a stat lately, I got it from Robert Herjavec actually. He said think about this. The worldwide economy is 86 trillion and we spend .014% on cyber, that's it. We're barely scratching the surface. And that's part of the problem. Okay, but with that limited resource we have to be as smart as possible. You've got this ransomware coming in. So what are your customers asking you for and how are you responding? >> So it's interesting, right, because it is top of mind, cyber and cyber attacks, and it takes many forms. The attacks can be malware, they could be encryption, they could be deletion. Which is ultimately the worst case scenario. And I think as you go forward and you look at it cyber is the number one concern for any CIO, CISO or anyone that's worried about their security infrastructure. >> Which is everybody >> Which is everybody, right, exactly. I think that we have delivered for the cloud data protection area a first and best offering with an air gap data protection solution. So inherently, we can insulate and protect our customer's data from cyber threats. So when a ransom event occurs you can recover your data without having to pay that ransom. Or not be concerned that in most severe cases your data gets deleted. I think most recently there was a healthcare provider who was threatened about their data being deleted. And that was the worst case. We were able to protect their data in the sense that with our cyber recovery offering they protected their data in an air gap vaulted solution. And they didn't have to pay for that ransom. >> So what I'm hearing from you guys is okay, cloud, very important. Hybrid cloud, multi-cloud, fundamental to our strategy. VMware, they say bet on sure things. VMware is pretty much a sure thing. Large customer base, leader in the space. And then cyber as a key concern of customers, you want to expand the notion of backup and data protection to really point it at cyber as well. >> Absolutely, in fact with this recent research, it's called the Global Data Protection Index Survey and we just refreshed it. And what customers identified as the most compelling reasons to adopt cloud is for better performance, better data protection, and better security. Not necessarily in that order but those were the top three. So we look at that and say, you know we've got plays there. Certainly we have capabilities protecting workloads in the cloud whether they be virtual machines, cloud native, containers. But the security aspect of it is huge. Because oftentimes customers, and Dave, you and I were talking about this, they make some broader assumptions about once data is in the clouds they can kind of wash their hands and walk away. Not so fast, because certainly there is a shared responsibility model that extends not only to data protection, but also to security. Look, don't get me wrong, the cloud service providers have fantastic security capabilities, have a great perimeter. But as you said, it's not a question of if, it's a question of when. And when something happens, are you ready for it? So these solutions extend not only to on-prem but into the cloud. So it's that ability wherever the workload lives that you can get the right protection and what we're really now referring to as safeguarding data. Because it's a combination of data protection and security that's embedded and doing it wherever the workload resides. >> I'm glad you brought that up Colm. I have a follow up on that, but Nelson, did you want to add something? >> Well, I just want to mention that one of the biggest concerns is making sure that that data you vaulted is actually clean and safe. So we have a cyber sense capability within our cyber recovery product, that when you vault that data it does about 100 analytics on that data to make sure that there's no malware. That it's not infected. And it does it automatically and even on incremental using machine learning. >> That's really important because mistakes happen really fast. (laughing) So if you're vaulting corrupted data, >> What do you do? >> Oops. >> Yeah, exactly. >> I want to come back, I think the shared responsibility model is not well understand and there's a lot of confusion in the industry. At a conference this year, AWS' CISO Stephen Schmidt was saying, look all this talk about security is broken it's not really productive. The state of security in the cloud is actually really good and to your point Colm, yeah, he's right about that. Then you hear Pat Gelsinger saying, he's told me many times in theCUBE security is a do-over. To my point, you know the 86 trillion. And so I kind of lean, when I talk to IT people what Pat is saying. So you say okay, where is the dissidence there? Well, the reality is is the cloud service providers and the shared security model, they'll secure the physical infrastructure. But it's up to the customer to be responsible for everything else. You know, the edicts of the organization are applied. We were talking to the CISO of a large insurance company and she said to us, oh no, shared responsibility means it's our responsibility. So you're not going to go after the cloud service provider, you're going to go after the insurance company, or the financial service institution. Their brand is the one that's going to get hurt. So that's misunderstood. My question, very long winded rant, but what role do you guys play in that shared responsibility model? >> Well, ultimately it comes down to the customer. And the shared responsibility model really is admissible, as you mentioned, right? And so at the end of the day, you as the customer own and are responsible to protect that data. So your data protection strategy, your cyber resilience strategy has to be sound. And it has to be secured by those that can actually do it across multiple distribution models and platforms, whether it's edge, core or cloud. Whether it's VM's, containers. It doesn't change. You're still ultimately responsible for it. >> I think maybe what you might be driving at the question, Dave, is empowering the customers to maintain control of their data. And having the tools in place so that they feel comfortable. And part of it too is moving more towards automation. Because as their applications grow, and as Nelson said, become more distributed, as the data grows exponentially, this just fundamentally isn't a task that humans can manage very much longer. >> I'm glad you brought that up, because you ask a CISO, what's your number one problem? And he or she will tell you the skill sets to keep up with all this complexity. And that's where automation comes in. >> Correct, it does. So that's where we're taking it. Is trying to make things more automated and take tasks away from humans that they just can't keep up with. >> All right guys, I'll give you the last word. We go back a decade or so ago and backup was a whole different situation. And we saw the rise of virtualization and now cloud and all these other things that we have been talking about. Edge, the cyber threats, et cetera. So bring us home, where do you see the future and how does Dell EMC data protection fit in? >> It's an exciting time, it really is. It's kind of like the coming of that second storm as you mentioned. Businesses have that demand of needing more services to load more quickly in an agile fashion. And as they pair that with the growth of their data which is distributed, they really have that challenge overall of how do I manage this environment? So you have to have the observability to understand where your data is and to be able to monitor it. You have to be able to orchestrate your workloads so that they're automated, and the data protection of those workloads are automated as well. And so the imperative that aspects like Tanzu are addressing with cloud native, that Kubernetes brings to the table to deliver containerized applications. That's really quite honestly is the biggest evolution I've seen in my last 20 to 30 years. This is definitely a different paradigm shift. >> Yeah, you know, six months ago I was with a competitor and was taking a look at EMC, sorry, I should say Dell EMC, and I was wondering, should I make a move over here? And really what convinced me was the fact that the company was willing to basically solve internally the innovator's dilemma. You're making so much money on your existing portfolio, now you're going to start investing in what appears to be almost internal competition to your portfolio. It's not, it's complimentary. So that's what drove the decision for me to come here, but I will also say it's great to be a part of an organization that has a long-term vision. You remember, I think the phrase that was being used, being held captive to the 90-day shot clock. You know, the earnings reports and stuff. And that drives behavior. Well, if your organization is looking at decade-long goals, that means that you can actually plan to do things that over time are going to actually bring real value to customers. So I think we're doing the right things. We're obviously innovating, we're on this agile software development cadence gives us the ability to solve the problems incrementally over time so customers can see that value instead of waiting for large batch releases. But is also gives us the ability to say, hey, when we've made mistakes or when we hadn't seen certain things come around the corner, we're agile enough to change with that. So I think the combination of having that vision and putting in the investments, and we've kind of likened ourselves to the biggest startup in the industry with the backing of a Fortune 50. And so from a customer standpoint you got to look at that and think, you know, that's interesting, because I need to solve my current problems today. I need to have a path forward for the future. And who am I betting on to deliver that? And the other thing I'll leave on is customers are trying to work with fewer suppliers, not more suppliers. Because they want to reduce the complexity. Well who has the ability to not only bring data protection to bear, but a whole portfolio of technology is really end to end. That can snap into those environments to again reduce complexity and drive more business value. >> That's a really interesting point you make about consolidations. Ever since I've been in this industry people want to deal with less suppliers and reduce the complexity. But you still see startups and VC's funding things. And what's happened is this consolidation, the big guys, you guys are the biggest consolidator. And I always say the rich get richer. There's always this tension between sort of, do I go out and buy the spoke, best of breed tools, or do I get them from somebody who can help me across the portfolio? That's really where your strength is. Guys, thank you so much. This is really a very important topic. Data protection is one of the most important areas that we've been covering. I've been reporting on it a lot. As I said, a lot of venture money has been flowing in. So I really appreciate you guys coming in, sharing your perspectives. And best of luck in the marketplace. >> Appreciate it, Dave. >> Thanks, this was great. >> You're welcome. All right, and thank you for watching, everybody. This is Dave Vellante for theCUBE. We'll see you next time. (upbeat music)

from our studios in the heart of Silicon Valley Palo Alto California this is a cute conversation hey welcome back here ready Jeff Rick here with the cube we're in our Palo Alto studios today to have a cube conversation with a really exciting company they've actually been around for a while but they've raised a ton of money and they're doing some really important work in the world in which we live today which is a lot different than the world was when they started in 2010 so we're excited to welcome to the studio he's been around before Mohit lad he is the CEO and co-founder of thousand ice mode great to see you great to see you as well thrilled to be here yeah welcome back but for people that didn't see the last video or not that familiar with thousand ice tell them a little bit kind of would a thousand eyes all about absolutely so in today's world the cloud is your new data center the Internet is your new network and SAS is your new application stack and thousand eyes is built to be the the only thing that can really help you see across all three of these like it's your own private environment I love that I love that kind of setup and framing because those are the big three things and as you said all those things have moved from inside your control to outside of your control so in 2010 is that was that division I mean when you guys started the company UCLA I guess a while ago now what was that the trend what did you see what yes what kind of started it so it's really interesting right so our background is a founding company with two founders we did our PhD at UCLA in computer science and focused on Internet and we were fascinated by the internet because it was just this complex system that nobody understood but we knew even then that it would meaningfully change our lives not just as consumers but even as enterprise companies so we had this belief that it's gonna be the backbone of the modern enterprise and nobody quite understood how it worked because everyone was focused on your own data center your own network and so our entire vision at that point was we want people to feel the power of seeing the internet like your network that's sort of where we started and then as we started to expand on that vision it was clear to us that the internet is what brings companies together what brings the cloud closer to the enterprise what brings the SAS applications closer to the enterprise right so we expanded into into cloud and SAS as well so when you had that vision you know people had remote offices and they would set up they would you know set up tunnels and peer-to-peer and all kinds of stuff why did you think that it was going to go to that next step in terms of the Internet you know just kind of the public Internet being that core infrastructure yes so we were at the at the very early stages of this journey to cloud right and at the same time you had companies like Salesforce you had office 365 they were starting to just make it so much easier for companies to deploy a CRM you don't have to stand up these massive servers anymore its cloud-based so it was clear to us that that was gonna be the new stack and we knew that you had to build a fundamentally different technology to be able to operate in that stack and it's not just about visibility it's about making use of collective information as well because you're going from a private environment with your own data center your own private network your own application stack to something that's sitting in the cloud which is a shared environment going over the Internet which is the same network that carries cat videos that your kids watch it's carrying production traffic now for your core applications and so you need a different technology stack and you need to really sort of benefit from this notion of collective intelligence of knowing what everybody sees together as one view so I'm curious force was such an important company in terms of getting enterprises to trust a SAS application for really core function with just sales right I think that was a significant moment in moving the dial was there a killer app for you guys that was you know for your customers the one where they finally said wait you know we need a different level of visibility to something that we rely on that's coming to us through an outside service so it's interesting right when we started the company we had a lot of advisors that said hey your position should be you're gonna help enterprises enforce SLA with Salesforce and we actually took a different position because what we realized was Salesforce did all the right stuff on their data centers but the internet could mess things up or enterprise companies that were not ready to move the cloud didn't have the right architectures would have some bottlenecks in their own environment because they are backhauling traffic from their London office to New York and then exiting from New York they're going back to London so all this stuff right so we took the position of really presenting thousand eyes as a way to get transparency into this ecosystem and we we believe that if we take this position if we want to help both sides not just the enterprise companies we want to help sales force we want to have enterprise companies and just really present it as a means of finding a common truth of what is actually going on it works so much better right so there wasn't really sort of one killer application but we found that anything that was real-time so if you think about video based applications or any sort of real-time communications based so the web access of the world they were just very sensitive to network conditions and internet conditions same with things that are moving a lot of data back and forth so these applications like Salesforce office 365 WebEx they just are demanding applications on the infrastructure and even if they're run great if the infrastructure doesn't it doesn't give you a great experience right and and and you guys made a really interesting insight to its and it's an all your literature it's it's a really a core piece of what you're about and you know when you owned it you could diagnose it and hopefully you could fix it or call somebody else to fix it but when you don't own it it's a very different game and as you guys talked about it's really about finding the evidence or everyone's not pointing fingers back in and forth a to validate where the actual problem is and then to also help those people fix the problem that you don't have direct control of so it's a very different you know kind of requirement to get things fixed when they have to get fixed yeah and the first aspect of that is visibility so as an example right you generally don't have a problem going from one part of your house to another part of your house because you own the whole place you know exactly what sits between the two rooms that you're trying to get to you don't you don't have run into surprises but when you're going from let's say Palo Alto to San Francisco and you have two options you can take 101 or 280 you need to know what you expect to see before you get on one of those options right and so the Internet is very similar you have these environments that you have no idea what to expect and if you don't see that with the right level of granularity that you would in your own environments you would make decisions that you have you know you have no control over right the visibility is really important but it's giving that lens like making it feel like a google maps of the internet that gives you the power to look at these environments like it's your private network that's the hard part right and then so what you guys have done as I understand is you've deployed sensors basically all over the Internet all at an important pops yeah and a point in public clouds and important enterprises etc so that you now have a view of what's going on it I can have that view inside my enterprise by leveraging your infrastructures that accurate correct and so this is where the notion of being able to set up this sort of data collection environment is really difficult and so we have created all of this over years so enterprise companies consumer companies they can leverage this infrastructure to get instant results so there's zero implementation in what right but the key to that is also understanding the internet itself and so this is where a research background comes in play because we studied we did years of research on actually modeling the Internet so we know what strategic locations to put these probes that to give good coverage we know how to fill the gaps and so it's not just a numbers game it's how you deploy them where you deploy them and knowing that connectivity we've created this massive infrastructure now that can give you eyes on the internet and we leverage all of their data together so if let's say hypothetically you know AT&T has an issue that same issue is impacting multiple customers through all our different measurements so it's like ways if you're using ways to get from point A to point B if Waze was just used by your family members and nobody else it would give you completely useless information values in that collective insight right and then now you also will start to be able to leverage ml and AI and you know having all that data and apply just more machine learning to it to even better get in get out in front of problems I imagine as much as as is to be able to identify so that's a really interesting point right so the first thing we have to tackle is making a complex data set really accessible and so we have a lot of focus into essentially getting insights out of it using techniques that are smarter than the brute-force techniques you get insights out and then present it in manners that it's accessible and digestible and then as we look into the next stages we're going to bring more and more things like learning and so on to take it even further right it's funny the accessible and digestible piece I was just had a presentation the other day and there was a woman from a CSO at a big bank and she talked about you know the problem of false positives and in in early days I mean their biggest issues was just too much data coming in from too many sensors and and too many false positives to basically bury people so they didn't have time to actually service the things that are a priority so you know a nice presentation of a whole lot of data makes a big difference to make it action it is absolutely true and now that the example I'll give you is oftentimes when you think about companies that operate with a strong network core like we do they're in the weeds right which is important but what is really important is tying that intelligence to business impact and so the entire product portfolio we've built it's all about business impact user experience and then going into connecting the dots or the network side so we've seen some really interesting events and as much as we know the internet every day I wake up and I see something that surprises me right we've had customers that have done migrations to cloud that have gone horribly wrong right so we the latest when I was troubleshooting with the customer was where we saw they migrated from there on from data center to Amazon and the user experience was 10x worse than what it was on their own data of the app once they moved to Amazon okay and what had happened there was the whole migration to Amazon included the smart sort of CDN where they were fronting your traffic at local sites but the traffic was going all over the place so from if a user was in London instead of going to the London instance of Amazon they were going to Atlanta or they were going to Los Angeles and so the whole migration created a worse user experience and you don't have that lens because you don't see that in a net portion of that right that's why we like we caught it instantly and we were able to showcase that hey this is actually a really bad migration and it's not that Amazon is bad it's just it's been implemented incorrectly right so yeah fix these things and those are all configurations all Connecticut which is so very easy all the issues you hear about with with Amazon often go back to miss configuration miss settings suboptimal leaving something open so to have that visibility makes a huge impact and it's more challenging because you're trying to configure different components of this environment right so you have a cloud component you have the Internet component your own network you have your own firewalls and you used to have this closed environment now it's hybrid it involves multiple parties multiple skill sets so a lot of things can really go wrong I think I think you guys you guys crystallized very cleanly is kind of the inside out and outside in approach both you know a as as a service consumer yeah right I'm using Salesforce I'm using maybe s3 I'm using these things that I need and I want to focus on that and I want to have a good experience I want my people to be able to get on their Salesforce account and book business but but don't forget the other way right because as people are experiencing my service that might be connecting through and aggregating many other services along the way you know I got to make sure my customer experience is big and you guys kind of separate those two things out and really make sure people are focusing on both of them correct and it's the same technology but you can use that for your production services which are revenue generating or you can use that for your employee productivity the visibility that you provide is is across a common stack but on the production side for example because of the way the internet works right your job is not just to ensure a great performance in user experience your job is also to make sure that people are actually reaching your site and so we've seen several instances where because of the way internet works somebody else could announce that their google.com and they could suck a bunch of traffic from the internet and this happens quite routinely in the notion of what is now known as DP hijacks or sometimes DNS hijacks and the the one that I remember very well is when there was the small ISP in Nigeria that announced the identity of the address block for Google and that was picked up by China Telecom which was picked up by a Russian telco and now you have Russia China and Nigeria in the path for traffic to Google which is actually not even going to Google's right those kinds of things are very possible because of the way the internet how fast those things kind of rise up and then get identified and then get shut off is this hours days weeks in this kind of example so it really depends because if you are let's say you were Google in this situation right you're not seeing a denial of service attack to your data centers in fact you're just not seeing traffic running in because somebody else is taking it away right it's like identity theft right like I somebody takes your identity you wouldn't get a mail in your inbox saying hey your identity has been taken back so easy you have to find it some other way and usually it's the signal by the time you realize that your identity has been stolen you have a nightmare ahead of you alright so you got some specific news a great great conversation you know it's super insightful to talk to people that are in the weeds of how all the stuff works but today you have a new a new announcement some new and new offering so tell us about what's going on so we have a couple of announcements today and coming back to this notion of the cloud being a new data center the internet your new network right two things were announcing today is one we're announcing our second version of the cloud then benchmark performance comparison and what this is about is really helping people understand the nuances the performance difference is the architecture differences between Amazon Google as your IBM cloud and Alibaba cloud so as you make decisions you actually understand what is the right solution for me from a performance architecture standpoint so that's one it's a fascinating report we found some really interesting findings that surprised us as well and so we're releasing that we're also touching on the internet component by releasing a new product which we call as internet insights and that is giving you the power to actually look at the internet more holistically like you own the entire internet so that is really something we're all excited about because it's the first time that somebody can actually see the Internet see all these connections see what is going on between major service providers and feel like you completely owned the environment so are people using information like that to dynamically you know kind of reroute the way that they handle their traffic or is it more just kind of a general health you know kind of health overview you know how much of it do I have control over how much should I have control over and how much of I just need to know what's going on so yeah so it just me great question so the the best way I can answer that is what I heard CIO say in a CIO forum we were presenting at where they were a customer it's a large financial services customer and somebody asked the CIO what was the value of thousand I wasn't the way he explained it which was really fascinating was phase one of thousand eyes when we started using it was getting rid of technical debt because we would keep identifying issues which we could fix but we could fix the underlying root cause so it doesn't happen again and that just cleared the technical debt that we had made our environment much better and then we started to optimize the environments to just get better get more proactive so that's a good way to think about it when you think about our customers most of the times they're trying to just not have their hair on fire right that's the first step right once we can help them with that then they go on to tuning optimizing and so on but knowing what is going on is really important for example if you're providing a.com sir is like cube the cube comm right it's its life and you're providing it from your data center here you have two up streams like AT&T and Verizon and Verizon is having issues you can turn off that connection and let all your customers back live having a full experience if you know that's the issues right right the remediation is actually quite quite a few times it's very straightforward if you know what you're trying to solve right so do you think on the internet insights this is going to be used just more for better remediation or do you think it's it's kind of a step forward and getting a little bit more proactive and a little bit more prescriptive and getting out ahead of the issues or or can you because these things are kind of ephemeral and come and go so I think it's all of the about right so one the things that the internet insights will help you is with planning because as you expand into new geo so if you're a company that's launching a service in a new market right that immediately gives you a landscape of who do you connect with where do you host right as now you can actually visualize the entire network how do you reach your customer base the best right so that's the planning aspect and if you plan right you would actually reduce a lot of the trouble that you see so we had this customer of ours that was deploying Estevan Software Defined one in there a she offices and they used thousand eyes to evaluate two different ISPs that they were looking at one of them had this massive time-of-day congestion so every time every day at nine o'clock the latency would get doubled because of congestion it's common in Asia the other did not have time of day congestion and with that view they could implement the entire Estevan on the ice pea that actually worked well for them so planning is important part of this and then the other aspect of this is the thing that folks often don't realize is Internet is not static it's constantly changing so you know AT&T might connect to Verizon this way it connects it differently it connects to somebody else and so having that live map as you're troubleshooting customer experience issues so let's say you have customers from China that are having a ton of issues all of a sudden or you see a drop of traffic from China now you can relate that information of where these customers are coming from with our view of the health of the Chinese Internet and which specific ISPs are having issues so that's the kind of information merger that simply doesn't happen today right promote is a fascinating discussion and we could go on and on and on but unfortunately do not have all day but I really like what you guys are doing the other thing I just want to close on which which I thought was really interesting is you know a lot of talk about digital transformation we always talk about digital transformation everybody wants the digital transfer eyes it but you really boiled it down into really three create three critical places that you guys play the digital experience in terms of what what the customers experience you know getting to cloud everybody wants to get to cloud someone can argue how much and what percentage but everybody's going to cloud and then as you said in this last example the MA when as you connect all these remote sites and you guys have a play in all of those places so whatever you thought about in 2010 that worked out pretty well thank you and we had a really strong vision but kudos to the team that we have in place that has stretched it and really made the most out of that so excited good job and thanks for for stopping by sharing the story thank you for hosting always a fun to be here absolutely all right well he's mo and I'm Jeff you're watching the cube when our power out the studio's having a cute conversation thanks for watching we'll see you next time [Music]

>> Narrator: Live from San Diego, California it's theCUBE covering Cisco Live, US, 2019. Brought to you by Cisco and its eco system partners. >> Welcome back to San Diego everybody. This is theCUBE the leader in live tech coverage, My name is Dave Vellante, Stu Miniman is here, Lisa Martin as well but we've got a very special guest now Ken O'Reilly my good friend is here. He's the director of customer experience for Cisco Stealthwatch. Kenny great to see you thanks for coming on. >> Well, thanks for having me, Dave. Good seeing you as well. >> Yes so customer experience, people think about customer experience and security it's not always great right? It's a challenging environment they're constantly sort of chasing their tails it's like the arms race with the bad guy so what is customer experience all about in the context of security? >> So our number one goal for our security customers is to accelerate their value realization so our challenge is to make sure that they get the value out of the product that they're buying because every minute of every day the bad guys are trying to get their assets and their IP and when they buy a technology the quicker you can get it up and running and protect the better it is for our customer. >> So how do you measure like value? It's like reducing the amount of data that you're exposed to losing? Is it increasing the cost of the bad guys getting in? 'cause if I'm a bad guy and it costs me more to get in I would maybe go somewhere else, how do you measure that? >> Right so, you're right, so our whole product strategy is to increase the cost for the bad guy to get the IP or the assets and so for us we have to understand what the value proposition is for our product so that the customers can realize that value, so whether it's tryna help them with the use cases or operationalize the product or in our case what we try to do we have both network users and security users we try to get both groups to adopt the technology and then expand it from there, operation centers to the guys that are doing the thread hunting to the investigations et cetera. So that's how we sort of gauge the value is the number of people that are using the technology and the number of use cases that are actually implemented. >> So we've been talking about security all week Stealthwatch obviously you know one of the flagship products Cisco security business grew 21% last quarter so that's kind of an interesting stat services is 25% of the companies revenue so you're the intersection of two pretty important places for Cisco so specifically when you come into a customer engagement who are you engaging with is it a multidisciplinary are you primarily dealing with the SecOps group or do you touch other parts of the organization? >> Yeah, so typically when a company's looking, it's usually they're looking for network visibility so we're dealing with the network architecture teams and they typically bring in the security architects 'cause today they're working hand in hand, and then from there that's where we say preach the gospel of Stealthwatch we always say you can never have enough Stealthwatch okay? Because you can never have enough visibility 'cause once you turn the lights on and they can see what's going on in their network it's very illuminating for them and then they realize the challenges that they have and what they have to do to protect their assets. >> Yeah I joked at Google Cloud Next it's like the cockroaches all scrambling you know for the corners when you turn the lights on and Stealthwatch at its core is you don't need a lot of fancy AI even though you can apply fancy AI but you start with the basics right? What do ya got, where are the gaps okay, so now once it's exposed what do you do with that information is the customer experience group come in and help implement it faster? That's part of the value so time to value to that? >> So time to value with our experts of course we understand the space we understand our product we understand the challenge and of course our network and security customers are overwhelmed you know the stat that they throw out there is that our large customers have anywhere from 50-100 security products so how do you stand out? So as a vendor our number one goal is to build that relationship with the customer to become the trusted security advisor so we know better than anybody how to get that value how to get it quickly and you know the number one problem that they have Dave is how to operationalize all these tools 'cause Stealthwatch sits in the middle we're a big integration platform we take data, telemetry, NetFlow from a lot of different products and we bring that data together to figure out, to help that customer figure out how to make sense of it update their policies create better policies and really tighten up their security posture. >> Okay so they might like to reduce the number of tools but they really can't right? 'cause their using 'em and so what you do is you bring in a layer to help manage that. >> Absolutely. >> But you're also solving a problem just in terms of exposing gaps and then do you also have tooling to fill those gaps? Or is that partners tools is that Stealthwatch? >> So we have our own what we call integration platform where we have a platform that helps integrate other, not only other Cisco security technologies into our platform but other security technologies as well outside of Cisco so you know it's a platform that we've built it's part of our customer experience sort of tool set but it's a tool set unlike anybody else ever has so that along with what we do with the DevNet group we've built our own set of API's to integrate in with the product API's so we can pump data out to data lakes we can pump data out to SIMS like Splunk and some of the others so you know that's where we are we're a solutions group that's what we do we work on the solutions, long term value you know we work on the lifecycle sort of value chain with customers. We're there with 'em the whole time you know our goal; retention, we want them to renew which means they're investing in us again and of course as Cloud, as their infrastructure is moving the the Cloud and our technologies are moving to the Cloud we have to be there to help them get through all those technology challenges. >> So the pricing model is a subscription model is that right? >> Yeah. >> Or can be or? >> Yes, well we call it term all right? But it's essentially subscription we have switched over the last 18 months from a perm to a term based model. >> Which I mean Chuck Robbins in the conference calls in the earnings calls talks about the importance of you know increasingly having a rateable model and recognizing subscription, so when you say a term so I got to what, sign up for a year, two years, three years or something like that? >> We like three yep. >> So who doesn't right? Okay so you sign up for three years but the price book says monthly I'm sure right so you (laughs) make it look smaller, but it makes sense though because you're not going to start stop, start stop with your security, you really want to get success out of it so you got to have some kind of commitment, let's talk a little bit more about the analytics side of it and how you're applying machine intelligence I mean there's always been some form of analytics largely for reporting and things of that nature but now it's getting more automated so take us on that analytics journey Stealthwatch has been around for what five years? >> 15 yeah over 15 years. >> 15? >> Ken: Yes, yes, yes. >> Oh wow maybe I just found out about it five years ago. >> (laughs) right yeah, not but I mean-- >> Dave: Take us back five years. >> Five years? So the big thing for us in the data that we collect is context. Right so you've talked to TK about the more context you can add to that data the better you are at analyzing that data so for us that's one of the things that we do we add a lot of context to that data through ICE so identity information, what kind of assets they are and that's where we get to through our tools add more context so that our analytical engines so like the cognitive thread analytics, the encrypted thread analytics that we have, that they're able to analyze that data a lot better and that's what we've been doing now for the past three plus years since we were acquired by Cisco is to find a way to add more context to the data so that helps our analytics become much more effective. >> And you can interact with through API's say for instance Splunk you mentioned that so you got that data that you can operate on do you see a point where the machines are actually going to plug the holes? I mean are we on the cusp of that? In other words you see a gap >> Right. >> Dave: Today a human has to take action correct? >> Yes, right, right, right. >> Do you see a point maybe it's two, three, five 10 years but are we going to get to that point? >> I think so down the line I mean because we've seen as we've been able to get better visibility and better context about that data we can make better decisions through the machine all right? So it doesn't take an army of people to read the matrix right, we're getting better at you know synthesizing that matrix down you take our network segmentation capabilities that we've built as part of the Stealthwatch customer experience team we can get to well over 90% identification of the assets on the network which is a lot better than anybody else in the industry all right? So we're getting there and through sort of the final stages of reading that metrics, reading the matrix we're getting to the point where we understand a lot more what's on peoples networks what those assets are. >> So as a security practitioner how do you think we're doing as an industry? I mean I used to go back every year and say okay how much was spent on security? are we more secure, less secure? And it felt like you know as data grew it felt like we were getting more and more and more exposed you've seen the stats where when a company gets infiltrated it takes on average you know 250 days for them to realize they've been infiltrated is that changing, are we getting better as an industry? >> I think in Cisco we are because of the products that we have in that integrated architecture so when we first joined three years ago that was the drum beat and now today we integrate with ICE we're going to integrate with next generation firewall through the integration of the sort of analytics that we've got in the Cloud that's happening right? And we're trying to integrate with other products but you know you go down on the floor and you see the number of point products that is a nightmare for our customers so for us through the customer experience in our organization we're there to take that complexity out and bring all of those technologies together and when you get to that point then you're really making progress with a customer, a customer that's got 50-100 products in the mix that's a recipe for disaster and if it's still like that five years from now customers are still going to be challenged. >> So a big part of your customer experience mission is simplification, speed time, time to value. >> Yes. >> Raise the cost to the bad guys and then do it all over again. >> Yeah, yeah it's just rinse and repeat and that's a life cycle journey and that's what we take our customers through right. >> Now I noticed you have on your phone you got the Bruins logo. >> That's right, right here proud. >> So big game tomorrow any predictions? >> 4-3 in overtime Bruins. >> Oh my God I don't think my heart could take that. >> Could you not take that Dave? It's going to be an overtime game. >> Well it's you know it's rare to have a game seven in any, at the very final one, a lot of game sevens but not to win it all I think the last time at Boston was 1984. >> Ken: Is that right? >> Yeah it's been a long time, so you know I'm excited. >> I know you are (laughs) that's right. >> Warriors fans too we got that thing going out I mean I don't know for all you hoop fans out there so, >> Hopefully there's a game seven for that as well. >> Yeah let's go right, why not? >> Why not, game seven all round. >> All right so Chara is going to play with his broken jaw or whatever's going on. >> Matt Grzelcyk I hope is back. >> Dave: Yeah that would be key. >> That would be key yeah so, >> Dave: sure up the defense >> That's right. (crosstalk) >> Ken: He's a plus minus leader Chara. >> Oh yeah. >> That's right all time. >> Even though we give him a lot of grief. (laughter) he may look slow but he's all time plus minus leader. >> All right Kenny hey thanks so much-- >> All right Dave thanks for having me on all right go Bruins. >> All right keep it right there everybody go Bruins we will be right back Dave Vellante, Stu Miniman and Lisa Martin we're live from Cisco Live in San Diego you're watching theCUBE. (electronic jingle)

>> live from Miami Beach, Florida It's the que covering demon 2019. Brought to you, by the way, >> Welcome back to Vima on 2019 in Miami. Everybody, we're here at the Fountain Blue Hotel. This is Day two of our coverage of the Cube, the leader in live Tech. And I'm David Dante with Peter Bors. Pippen. Jay Raj is here. He's the vice president and CEO of Make A Wish America. Just that awesome foundation nonprofit people. Thanks for coming on the Cube. >> Thank you for having me appreciate it. >> So make a wish. Children with wishes and have terminal illnesses. You guys make them come true. It's just a great organizations. Been around for a long time, I think, since the early eighties, right, >> 39 years and going >> years and hundreds of thousands of wishes made. So just how did you get Teo make a wish that all come about >> it? It wasn't interesting journey. I was consulting in I t for multiple big companies. And, you know, two years back, it was through a recruiting channel that I got an opportunity to start some conversations as the CIA and make a wish. Uh, the thing that got me in the opportunity was predominately about enterprises and just to give you a little bit off, make official operations. Make a Wish was Founded and Phoenix, Arizona. And but we also operate a 60 chapters across the United States that it is 60 chapters each of the chapter there 501 C three companies themselves with the CEO and abort. Essentially, it is 60 plus one. The national team kind of managing. All of the chapters are helping the chapters. National does not do any wish. Granting all the wish planning happens to the chapters. But National helps the chapters with the distribution of funding models brand. And thanks for That's a couple of years back in the national board talked about in our dream and mission, which is granting every eligible child the notion ofthe enterprise. You know, working as an enterprise came into four and it being a great piece off providing shared services and thanks for that. So I was brought on board and we took on I would call as the leader today said and dashes dream off. Bringing together all the 60 chapters and the city chapter's essentially are split across 120 locations. So Wade took on a project off. You know, combining our integrating all of their infrastructure needs into one place. And Phoenix without ada, sent a provider. You know, we worked with a partner. Phoenix. Now fantastic partners >> there. We had them on the other day. >> Yep, yep. Yeah, MacLaren. I mean, and the team, they did a great job. And, you know, when we had to move all of the data, everything from the 60 chapters applications everything into a centralized data center, locations that we managed right now from Make a Wish National office and provide a service back to the chapters That gives you a little bit off. You know, from behind the scenes. What happened? >> You provide the technical overview framework for all the 60 chapters. >> It almost sounds like a franchise model. >> It's what we call a Federated model back in the nonprofit. >> But but but but because make a wish is so driven by information. Yep. Both in the application as well as the programs to deliver thie brand promise. And the brand execution has got to be very, very closely tied to the quality of a shared services you provide >> exactly. Exactly. And like I said, the reason I talked about them being a separate companies themselves is you know, as I always say to my 60 CEOs, Ah, I should be able to provide the services because they wanted, because they have a choice to go outside and have their own partner. Another thing for that which they can. But they would want to work with the national team and get my, you know, work through our services rather than having have to because of the very it's A. It's a big difference when it comes to, but I've been lucky on privileged to you have these conversations with the CEO's. When I start talking to them about the need for centralization, the enterprise society assed much, there are questions when he start leading with the mission and the business notion of why we need to do that, it's It's fantastic. Everybody is in line with that. I mean, there's no question, then, as toe Hey, guys, uh, let me do all the Operation Manisha fight and leave it to me and I'll in a handler for you, and I let you guys go to what you do best. which is granting wishes. So then it becomes it doesn't become a question off, you know, should be a shouldn't way. And of course, to back that up. But I was talking to the dean, folks, It just solutions. Like VMware, Veeam. It makes it much simpler even from a cost prospect. You not for me to manage a bigger team s so that I can take those dollars and give it back to the business to grant another wish. So it's it's pretty exciting that >> way. So you set the standards. Okay, here's what you know, we recommend and then you're you're saying that adoption has been quite strong. Yeah, I remember Peter. Don't say easy. I used to run Kitty Sports in my local town in which is small town. And there was, you know, a lot of five or six or seven sports, and I was the sort of central organization I couldn't get six sports to agree that high man is 60 different CEO's. But that's okay. So not easy. But so how were you able to talk leadership or leading as we heard from Gino Speaker today? How were you able to get those guys, you know, aligned with your vision. >> Uh, it's it's been fantastic. I've had a lot ofthe good support from our executive came from a leadership team because leadership is always very important to these big initiatives are National board, which comprises off some of the that stuff best leaders in America and I have the fortune toe be mentored by Randy Sloan, who used to be the CEO of Southwest. And before that, you see a global CEO for, uh, you know, Popsicle. You know, he always told me, but but I mean CIA job. One thing is to no the technology, but completely another thing. Toe building relationships and lead with the business conversation. And so a typical conversation with the CEO about Hey, I need to take the data that you have all the I t things that you have and then me doing it. And then there are questions about what about my staff and the's conversations. Because you know, it's a nonprofit is a very noble, nice feeling, and you wouldn't want the conversations about, you know, being rift and things like that are being reduced producing the staff and thinks of that. But you know as he walked through that and show the benefits of why we doing it. They get it. And they've been able to repurpose many off the I. D functions back in tow, revenue generation model or ofhis granting in our team. And in many cases, I've been ableto absolve some off their folks from different places, which has worked out fine for me, too, because now I have kind of a power user model across the United States through which I can manage all these 120 locations. It's very interesting, >> you know, site Reliable and Engineering Dev Ops talks about thie error budget or which is this notion of doo. You're going tohave errors. You're going to have challenges. Do you want it in the infrastructure you wanted the functions actually generating value for the business? I don't know much about Make a wish. I presume, however, that the mission of helping really sick kids achieve make achieve a wish is both very rewarding, very stressful. He's gotta be in a very emotional undertaking, and I imagine it part of your message them has got to be let's have the stress or that emotional budget be dedicated to the kids and not to the technology >> completely agree. That's that. That's been one of my subjects, as you asked about How is it going about? It's about having the conversation within the context of what we talked about business and true business. Availability of data. You know, before this enterprise project data was probably not secure enough, which is a big undertaking that we're going down the path with cyber security. And you know, that is a big notion, misplaced notion out there that in a non profits are less vulnerable. Nobody. But that's completely untrue, because people have found out that nonprofits do not probably have the securing of walls and were much more weight being targeted nonprofits as a whole, targeted for cyber security crimes and so on and so forth. So some of these that I used to, you know, quote unquote help or help the business leaders understand it, And once they understand they get it, they ableto, you know, appreciate why we doing it and it becomes the conversation gets much more easier. Other What's >> the scope of the size of the chapters is that is a highly variable or there is. >> It is highly variable, and I should probably said, That's Thesixty chapters. We look at it as four categories, so the cat ones are what we call the Big Ice, the Metro New Yorkers and Francisco Bay Area. They're called Category one chapters anywhere between 4 1 60 to 70 staff. Grant's close to around 700 wishes you so as Make a Wish America, we ran close toe 15,600 wishes a year, and cat ones do kind of close to 700 15,600 400 to 700. And then you get into care to scare threes and cat for scat force are anywhere between, you know, given example Puerto Rico or Guam territory there. Cat Force New Mexico is a cat for three staff members Gammas operated by two staff members and 20 volunteers. They grant about 3 2 20 12 to 15 which is a year, so it's kind of highly variable. And then, you know, we talk about Hawaii chapter. It's a great example. They cat once predominate because of the fact that you know, they they do. There's not a lot ofthe wishes getting originated from how I but you know, Florida, California and how your three big chapters with a grand are a vicious ist with a lot of grant, you know, wish granting. So there's a lot off, you know, traffic through those chapters >> so so very distributed on diverse. What's the relationship between data and the granting of wishes? Talk about the role of data. >> Should I? I was say this that in a and I probably race a lot of fibrosis and my first introductory session a couple of years back when I John make a wish with the CEO's uh, when we had the CEO meeting and talk to them about I leaders the days off making decisions based on guts are gone. It has to be a data driven decision because that's where the world is leading to be. Take anything for that matter. So when we talk about that, it was very imperative going back to my project that the hall we had all of the data in one place or a semblance off one single place, as opposed to 60 different places to make decisions based on wish forecast, for example, how many wishes are we going to do? How many wishes are coming in? How's the demand? Was the supply matching up one of the things that we need to do. Budget purposes, going after revenue. And thanks for that. So data becomes very important for us. The other thing, we use data for the wish journeys. Essentially, that's a storytelling. You know, when I you know, it was my first foray into for profit Sorry, nonprofit. And me coming from a full profit is definitely a big culture shock. And one of the things they ask us, what are we selling? Its emotions and story. And that's our data. That is what you know. That's huge for us if we use it for branding and marketing purposes. So having a good semblance off data being ableto access it quickly and being available all the time is huge for us. >> Yeah, and you've got videos on the site, and that's another form of data. Obviously, as we as we know here, okay. And then, from a data protection standpoint, how do you approach that? Presume you're trying to standardize on V maybe is way >> are actually invested in veeam with them for a couple of years right now, as we did the consolidation of infrastructure pieces Veeam supporters with all of the backup and stories replication models. Uh, we're thinking, like Ratmir talked about act one wi be a part of the journey right now, and we're looking at active. What that brings to us. One of the things that you know, dream does for us is we have close to 60 terabytes of data in production and close to another 400 terabytes in the back of things. And, uh, it's interesting when they look about look at me equation, you think about disaster recovery back up. Why do you need it? What? The business use cases case in point. This classic case where we recently celebrated the 10th anniversary ofthe back wish bad kid in San Francisco, we have to go back and get all the archives you know, in a quick fashion, because they're always often requests from the media folks to access some of those. They don't necessarily come in a planned manner. We do a lot of things, a lot of planning around it, but still there are, you know, how How did that come about? What's the story behind? So you know, there are times we have to quickly go back. That's one second thing is having having to replicate our data immediately. Another classic case was in Puerto Rico. There was a natural disaster happened completely. Shut off. All the officers work down. We had to replicate everything what they had into a completely different place so that they could in a vpn, into an access that other chapters and our pulled in to help. They were close to 10 wish families close to 10 which families were stranded because of that. So, you know, gaining that data knowledge of where the family is because the minute of his journey starts. Everything is on us till the witch's journey ends. So we need to make sure everything is proper. Everything goes so data becomes very crucial from those pants >> you're tracking us. I mean, if you haven't been on the make a Wish site is some amazing stories. There I went on the other day. There's a story of ah, of 13 year old girl who's got a heart condition. Who wanted to be a ballerina. A kid with leukemia five years old wants to be a You want to be a chef. My two favorites, I'll share What? It was this kid Brandon a 15 year old with cystic fibrosis. I wanted to be a Navy seal. You guys made that happen. And then there was this child. Colby was 12 years old and a spinal muscular issue. You want to be a secret agent so very creative, you know, wishes that you ran >> way had another wish a couple of years last year in Georgia, where they wish kid wanted to go to Saturn. Yes, yes, it was huge. I mean, and you know the best part about us once we start creating those ideas, it's amazing how much public support we get. The community comes together to make them wish granting process. Great. Now. So I got involved in that. They gave the wish Kato training sessions to make sure that he is equipped when he goes into. And we had a bushel reality company create the entire scene. It was fabulous. So, you know, the way you talk about data and the technology is now some of the things I'm very excited about us usage off thes next Gen technology is like our winter reality to grant a wish. I mean, how cool would that be for granting a wish kid who is not able to get out of the bed. But having able to experience a the Hawaii is swimming. Are being in Disney World enough a couple of days? That's That's another use case that we talked about. That other one is to put the donors who pay the money in that moment off granting, you know, they are big major gift, uh, donors for make a wish. Sometimes we were not able to be part of a fish, but that would be pretty cool if you can bring the technology back to them and you know not going for them. You know pretty much everybody and make the ass through that rather than a PowerPoint or a storytelling, when the storytelling has to evolve to incorporate all of that so pretty excited >> and potentially make a participatory like, say, the virtual reality and then even getting in more into the senses and the that the smells. And I mean this is the world that we're entering the machine intelligence, >> which you still have to have, But you still have to be a functioning, competent, operationally sound organization. There've been a number of charities, make a wish is often at the top of the list of good charities. But there were a number of charities where the amount of money that's dedicated to the mission is a lot less an amount of money, dedicated administration of fundraising, and they always blame it. Systems were not being able to track things. So no, it's become part of the mission to stay on top of how information's flowing because it's not your normal business model. But the services you provide is really useful. Important. >> Sure, let me percent you the business conundrum that I have personally as a 90 leader. It takes close to $10,400 on an average to grant a wish. Uh, and, uh, partly because of me. But being part of the mission, plus me as a 90 leader wanting to understand the business more, I signed up. I'm a volunteer at the local Arizona chapter. I've done couple of expanding myself, and, uh, the condom is, if asked, if you want to go, uh, you know, do the latest and greatest network upgrade for $10,400 are what do you want to, uh, you know and make the network more resilient cyber security and all that stuff. What do you want to go grant? Another wish as a 90 leader probably picked the former. But as a volunteer, I would be like, No, it needs to go to the kid. It's Ah, it's It's an interesting kind of number, you know? You have to find the right balance. I mean, you cannot be left behind in that journey because at many points of time s I talked about it being a cost center. It being a back office. I think those days have clearly gone. I mean, we we evolved to the point where it is making you steps to be a participant b A b a enabler for the top line to bring in more revenues, tow no augment solutions for revenue and things. For that sofa >> rattles the experience or exact role citizens. And in your case, it's the experience is what's being delivered to the degree that you can improve the experience administratively field by making operations cheaper. Great. But as you said, new digital technologies, they're going to make it possible to do things with the experience that we could even conceive of. Five >> wears a classic example. Williams and Beam. I couldn't have taken the data from 60 chapters 120 locations into one single location manageable, and it reduced the cost literally reduce the cost of the 60 instances in one place without technology is like, you know what Sharia virtual machines. And and then to have a backup robust backup solution in a replication off it. It's fantastic. It's amazing >> there. And that's against here. You could give back to the dash chapters and backing, But thanks so much for sharing your story. You Thank you. Thank you. You're welcome. Alright, keep it right there. Buddy. Peter and I were back with our next guest. You watching the Cube live from V mon from Miami? 2019. We're right back. Thank you.