(digital music) >> Hello everyone. And welcome to theCUBE where we're covering the recent news from Hewlett Packard Enterprise Making Moves and Storage. And with me are Omer Asad, Vice President and General Manager for Primary Storage, HCI and Data Management at HPE and Sandeep Singh who's the Vice President of Storage Marketing at Hewlett Packard Enterprise. Gentlemen, welcome back to theCUBE. Great to see you both. >> Dave its a pleasure to be here. >> Always a pleasure talking to you Dave thank you so much. >> Oh, it's my pleasure. Hey, so we just watched HPE make a big announcement and I wonder Sandeep, if you could give us a quick recap. >> Yeah, of course Dave. In the world of enterprise storage there hasn't been a moment like this in decades, a point at which everything is changing for data and infrastructure and it's really coming at the nexus of data, cloud and AI that's opening up the opportunity for customers across industries to accelerate their data-driven transformation. Building on that we just unveiled a new vision for data that accelerates the data driving transformation for customers edge to cloud. And to pay that off we introduce a new data services platform that consists of two game-changing innovations. First it's a data services cloud console which is a SaaS based console that delivers cloud operational agility for customers. And it's designed to unify data operations through a suite of cloud services. Though our second announcement is HPE Electra. HPE Electra is a cloud native data infrastructure portfolio to power your data edge to cloud. It's managed natively with data services cloud console and it brings that cloud operational model to customers wherever their data lives. These innovations are really combined with our industry leading AIOPS platform which is HPE InfoSight and combine these innovations radically simplify and bring that cloud operational model to customers our data and infrastructure management. And it gives the opportunity for streamlining data management across the life cycle. These innovations are making it possible for organizations across the industries to unleash the power of data. >> That's kind of cool. There're a lot of the stuff we've been talking about for all these years is sort of this unified layer across all clouds on-prem, AI injected in I could tell you're excited and it sounds like you you can't wait to get these offerings in the hands of customers, but I wonder we get back up a minute. Omer, maybe you could describe the problem statement that you're addressing with this announcement. What are customers really pain points? >> Excellent question, Dave. So in my role, as the General Manager for Data Management and Storage here at HPE I get the wonderful opportunity to talk to hundreds of customers in a year. And, you know, as time has progressed as the amount of data under organizations' management has continued to increase, what I have noticed is that recently there are three main themes that are continuously emerging and are now bubbling at the top. The first one is storage infrastructure management itself is extremely complex for customers. While there have been lots of leaps and down progress in managing a single array or managing two arrays with a lot of simplification of the UI and maybe some modern UIs are present but as the problem starts to get at scale as customers acquire more and more assets to store and manage their data on premise the management at scale is extremely complex. Yes, storage has gotten faster, yes, flash has had a profound effect on performance availability and latency access to the data but infrastructure management and storage management as a whole has become a pain for customers and it's a constant theme as storage lifecycle management comes up storage refresh has come up and deploying and managing storage infrastructure at scale comes up. So that's one of the main problems that I've been seeing as I talk to customers. Now, secondly, a lot of customers are now talking about two different elements. One is storage and storage deployment and life cycle management. And the second is the management of data that is stored on those storage devices. As the amount of data grows the silos continue to grow a single view of life cycle management of data doesn't, you know, customers don't get to see it. And lastly, one of the biggest things that we see is a lot of customers are now asking, how can I extract a value from this data under my management because they can't seem to parse through the silos. So there is an incredible amount of productivity lost when it comes to data management as a whole, which is just fragmented into silos, and then from a storage management. And when you put these two together and especially add two more elements to it which is hybrid management of data or a multicloud management of data the silos and the sprawl just continues and there is nothing that is stitching together this thing at scale. So these are the three main themes that constantly appear in these discussions. Although in spite of these a lot of modern enhancements in storage >> Well, I wonder if I could comment guys 'cause I've been following this industry for a number of years and you're absolutely right, Omer. I mean, if you look at the amount of money and time and energy that's spent into or put into the data architectures people are frustrated they're not getting enough out of it. And I'd note that, you know, the prevailing way in which we've attacked complexity historically is you build a better box. And well, while that system was maybe easier to manage than the predecessor systems all it did is create another silo and then the cloud, despite its impaired simplicity that was another disconnected siloed. So then we threw siloed management solutions at the problem and we're left with this collection of point solutions with data sort of trapped inside. So I wonder if you could give us your thoughts on that and you know, do you agree, what data do you have around this problem statement? >> Yeah, Dave that's a great point. And actually ESG just recently conducted a survey of over 250 IT decision makers. And that actually brings one of the perfect validations of the problems that Omer and you just articulated. What it showed is that 93% of the respondents indicated that storage and data management, that complexity is impeding their digital transformation. On average, the organizations have over 23 different data management tools which just typifies and is a perfect showcase of the fragmentation and the complexity that exists in that data management. And 95% of the respondents indicated that solving storage and data management that complexity is a top 10 business initiative for them. And actually top five for 67% of the respondents. So it's a great validation across the board. >> Well, its fresh in their minds too, because pre pandemic there was probably, you know, a mixed picture, right. It was probably well there's complacency or we're not moving fast enough, we have other priorities, but they were forced into this. Now they know what the real problem is it's front and center. Yeah, I liked that you're putting out there in your announcement this sort of future state that you're envisioning for customers. And I wonder if we could sort of summarize that and share with our listeners that vision that you unveiled what does it look like and how are you making it real? >> Yeah, overall, we feel very strongly that it's time for our customers to reimagine data management. And our vision is that customers need to break down the silos and complexity that plagues the distributed data environments. And they need to experience a new data experience across the board that's going to help them accelerate their data-driven transformation and we call this vision Unified DataOps. Unified DataOps integrates data-centric policies across the board to streamline data management, cloud-native control and operations to bring that agility of cloud and the operational model to wherever data lives. And AI driven insights and intelligence to make the infrastructure invisible. It delivers a whole new experience to customers to radically simplify and bring the agility of cloud to data and data infrastructure, streamlined data management and really help customers innovate faster than ever before. And we're making the promise of Unified DataOps real by transforming the entire HPE storage business to a cloud native software defined data services and that's through introducing a data services platform that expands HPE GreenLake. >> I mean, the key word I take away there Sandeep, is invisible. I mean, as a customer I want you to abstract that complexity away that underlying infrastructure complexity I just don't want to see it anymore. Omer, I wonder if we could start with the first part of the announcement maybe you can help us unpack data services, cloud console. I mean, you know, people are immediately going to think it's just another software product to manage infrastructure. But to really innovate, I'm hoping that it's more than that. >> Absolutely, Dave, it's a lot more than that. What we have done fundamentally at the root of the problem is we have taken the data and infrastructure control away from the hardware and through that, we provided a unified approach to manage the data wherever it lives. It's a full blown SaaS console which our customers get onto and from there they can deploy appliances, manage appliances, lifecycle appliances and then they not only stop at that but then go ahead and start to get context around their data. But all of that (indistinct) available through a SaaS platform, a SaaS console as every customer onboards themselves and their equipment and their storage infrastructure onto this console then they can go ahead and define role-based access for different parts of their organization. They can also apply role-based access to HPE GreenLake management personnel so they can come in and do and perform all the operations for the customers via the same console by just being another access control methodology in that. And then in addition to that, as you know, data mobility is extremely important to our customers. How do you make data available in different hyperscaler clouds if the customer's digital transformation requires that? So again, from that single cloud console from that single data console, which we are naming here as data services console customers are able to curate the data, maneuver the data, pre-positioned the data into different hyperscalers. But the beautiful thing is that the entire view of the storage infrastructure, the data with its context that is stored on top of that access control methodologies and management framework is operational from a single SaaS console which the customer can decide to give access to whichever management entity or authority comes into help them. And then what this leads us into is then combining these things into a northbound API. So anybody that wants to streamline operational manageability can then use these APIs to program against a single API which will then control the entire infrastructure on behalf of the customer. So if somebody dare what this is it is bringing that cloud operational model that was so desired by each one of our customers into their data centers and this is what I call an in-place transformation of a management experience for our customer by making them seamlessly available on a cloud operational model for their infrastructure. >> Yeah, and you've turned that into essentially an API with a lot of automation, that's great. So, okay. So that's kind of how you're trying to change the game here you're charting new territory. I want you to talk, you talked to hundreds and hundreds of customers every year I wonder if you could paint a picture from the customer perspective how does their experience actually change? >> Right, that's a wonderful question, Dave. This allows me to break it down into bits and bytes further for you and I love that, right. So the way you look at it is, you know, recently if you look at the storage management, as we talked about earlier, from an array perspective or maybe two arrays perspective has been simplified I mean, it's a solved problem. But when you start to imagine deploying hundreds of arrays and these are large customers, they have massive amounts of data assets, storage management hasn't scaled along as the infrastructure scales. But if you look at the consumer world you can have hundreds of devices but the ownership model is completely (indistinct). So the inspiration for solving this problem for us actually was inspired from consumerization of IT and that's a big trend over here. So now we're changing the customer's ownership model, the customer's deployment model and the customer's data management model into a true cloud first model. So let me give some of the examples of that, right. So first of all, let's talk about deployment. So previously deployment has been a massive challenge for our customers. What does deployment in this new data services console world looks like? Devices show up, you rack them up and then you plug in the power cable, you plug in the network cable and then you walk out of the data center. Data center administrator or the storage of administrator they will be on their iPad, on their data services console, or iPhone or whatever the device of their choice is and from that console, from that point on the device will be registered, onboarded, its initial state will be given to it from the cloud. And if the customer has some predefined States for their previous deployment model already saved with the data console they don't even need to do that we'll just take that and apply that state and induct the device into the fleet that's just one example. It's extremely simple plug in the power cable, plug in the network cable and the data center operational manager just walks out. After that you could be on the beach, you could be at your home, you could be driving in a car and this don't, I advise people not to fiddle with their iPhones when they're driving in a car, but still you could do it if you want to, right. So that's just one part from a deployment methodology perspective. Now, the second thing that, you know, Sandeep and I often bounce ideas on is provisioning of a workload. It's like a science these days. And is this array going to be able to absorb my workload, is the latency going to go South does this workload latency profile match this particular piece of device in my data center? All of this is extremely manual and it literally takes, I mean, if you talk to any of the customers or even analysts, deploying a workload is a massive challenge. It's a guesswork that you have to model and, you know basically see how it works out. I think based on HPE InfoSight, we're collecting hundreds and millions of data points from all these devices. So now to harness that and present that back to a customer in a very simple manner so that we can model on their behalf to the data services console, which is now workload of it, you just describe your workload, hey, I'm going to need these many IOPS and by the way, this happens to be my application. And that's it. On the backend because we're managing your infrastructure the cloud console understands your entire fleet. We are seeing the statistics and the telemetric coming off of your systems and because now you've described the workload for us we can do that matching for you. And what intent based provisioning does is describe your workloads in two or three clicks or maybe two or three API construct formats and we'll do the provisioning, the deployment and bringing it up for you on your behalf on the right pieces of infrastructure that matched it. And if you don't like our choices you can manually change it as well. But from a provisioning perspective I think that took days can now come down to a couple of minutes of the description. And lastly, then, you know, global data management distributed infrastructure from edge to cloud, invisible upgrades, only upgrading the right amount of infrastructure that needs the upgrade. All of that just comes rolling along with it, right. So those are some of the things that this data services console as a SaaS management and scale allows you to. >> And actually, if I can just jump in and add a little bit of what Omer described, especially with intent-based provisioning, that's really bringing a paradigm shift to provisioning. It's shifting it from a LAN-centric to app-center provisioning. And when you combine it with identity management and role-based access what it means is that you're enabling self-service on demand provisioning of the underlying data infrastructure to accelerate the app workload deployments. And you're eliminating guesswork and providing the ability to be able to optimize service level objectives. >> Yeah, it sounds like you've really nailed that in an elegant way that provisioning challenge. I've been saying for years if your primary expertise is deploying logical unit numbers you better find some other scales because the day is coming that that's just going to get automated away. So that's cool. There's another issue that I'm sure you've thought about but I wonder if you could address, I mean, you've got the cloud, the definition of cloud is changing that the cloud is expanding to on-prem on-prem expand to the cloud. It's going out to the edge, it's going across clouds and so, you know, security becomes a big issue that threat surface is expanding, the operating model is changing. So how are you thinking about addressing those security concerns? >> Excellent question, Dave. So, you know, most of the organizations that we talked to in today's modern world, you know almost every customer that I talk to has deployed either some sort of a cloud console where they're either one of the customers were the hyperscalers or you know, buy in for SaaS-based applications or pervasive across the customer base. And as you know, we were the first ones to introduce the automatic telemeter management through HPE InfoSight that's one of the largest storage SaaS services in production today that we operate on behalf of our customers, which has, you know, Dave, about 85% connectivity rate. So from that perspective, keeping customer's data secure, keeping customer's telemetry information secure we're no stranger to that. Again, we follow all security protocols that any cloud operational SaaS service would do. So a reverse handling, the firewall compliancy security audit logs that are published to our customers and published to customers' chief information security officers. So all of those, you know what I call crossing the T's and dotted the I's we do that with security expert and security policies for which each of our customers has a different set of rules. And we have a proper engagement model that we go through that particular audit process for our customers. Then secondly, Dave the data services cloud console is actually built on a fundamental cloud deployment technology that is not sort of that new. Aruba Central which is an Aruba management console which is also an HPE company it's been deployed and it's managing millions of access points in a SaaS framework for our customers. So the fundamental building blocks of the data storage console from a basic enablement perspective come from the Aruba Central console. And what we've taken is we've taken those generic cloud-based SaaS services and then built data and storage centric SaaS services on top of that and made them available to our customers. >> Yeah, I really like the Aruba. You picked that up several years ago and it's same thing with InfoSight the way that you bring it to other parts of the portfolio those are really good signs to watch of successful acquisitions. All right, there's a lot here. I want to talk about the second part of the announcement. I know you're a branding team you guys are serious about branding that new product brand. Maybe you could talk about that. >> So again, so delivering the cloud operational model is just the first piece, right. And now the second part of the announcement is delivering the cloud native hardware infrastructure which is extremely performing to go along with this cloud operational model. So what we have done Dave, in this announcement is we've announced HPE Electra. This is our new brand for our cloud native infrastructure to power your data and its appliances from core to the edge, to the cloud, right. And what it does is it takes the cloud operational model and this hardware is powered by that, it's completely wrapped around data. And so HPE Electra is available in two models right now, the HB electron 9,000 which is available for mission critical workloads for those high intensity workloads with a hundred percent availability guarantee where no failure is ever an option. And then it's also available as HPE Electra, 6,000 which is available for general purpose, business critical workloads generally trying to address that mid range of the storage market. And both of these systems are full 100% NBME front and back. And they're powered by the same unified cloud management operational experience that the data cloud console provides. And what it does is it allows our customers to simplify the deployment model, it simplifies their management model and really really allows them to focus on the context, the data and their app diversity whereas data mobility, data connectivity, data management in a multicloud world is then completely obstructed from them. >> Dave: Yeah. >> Sandeep: And Dave. >> Dave: Go ahead, please. >> Just to jump in HPE Electra combined with data services cloud console is delivering a cloud experience that makes deploying and scaling the application workloads as simple as flipping a switch. >> Dave: Nice. >> It really does. And you know, I'm very comfortable in saying this you know, like HPE InfoSight, we were the first in the industry to bring AI-based elementary and support enabled metrics (indistinct). And then here with data services console and the hardware that goes with it we're just completely transforming the storage ownership and a storage management model. And for our customers, it's a seamless non-disruptive upgrade with fully data in place upgrade. And they transform to a cloud operational model where they can manage their infrastructure better where they are through a complete consumer grade SaaS console is again the first of its kind when you look at storage management and storage management at scale. >> And I like how you're emphasizing that management layer, but underneath you got all the modern hardware technologies too which is important because it's a performance got to be, you know, a good price performance. >> Absolutely. >> So now can we bring this back again to the customers what are the outcomes that this is going to enable for them? >> So I think Dave, the first and the foremost thing is as they scale their storage infrastructures they don't have to think it's really as simple as yeah, just send it to the data center, plug in the power cable, plug in the network cable and up it comes. And from that point onwards the life cycle and the device management aspect are completely abstracted by the data services console. All they have to focus is I just have new capacity available to me and when I have an application the system will figure it out for me where they need to deploy. So no more needing the guesswork, the Excel sheets of capacity management, you know the chargeback models, none of that stuff is needed. And for customers that are looking to transform their applications customers looking to refactor their applications into a hyperscaler model or maybe transform from VM to containers, all they need to think about and focus is on that the data will just follow these workloads from that perspective. >> And Dave, just to almost response here as I speak with customers one of the things I'm hearing from IT is that line of business really wants IT to deliver that agility of cloud yet IT also has to deliver all of the enterprise reliability, availability, all of the data services. And what's fantastic here is that through this cloud operational model IT can deliver that agility, that line of business owners are looking for at the same time they've been under pressure to do a lot more with less. And through this agility, IT is able to get time back be able to focus more on the strategic projects at the same time, be able to get time back to spend more time with their families that's incredibly important. >> Omer: Right >> Well, I love the sort of mindset shift that I'm seeing from HPE we're not talking about how much the box weighs (laughing) we're talking about the customer experience. And I wonder, you know, that kind of leads me, Sandeep to how this kind of fits in to this new really, to me, I'm seeing the transformation before our eyes but how does it fit into HPE's overall mission? >> Well, Dave, our mission overall is to be the edge to cloud platform as a service company with HPE GreenLake, being the key to delivering that cloud experience. And as Omer put it, be able to deliver that cloud experience wherever the customer's data lives. And today we're advancing HPE GreenLake as a service transformation of the HPE storage business to a software defined cloud data services business overall. And for our customers, this translates to how to operational and ownership experience that unleashes their agility, their data and their innovation. So we're super excited >> Guys, I can tell you're excited. Thanks so much for coming to theCUBE and summarizing the announcements, congratulations and best of luck to both of you and to HPE and your customers. >> Thank you Dave. It was a pleasure. (digital music)

(upbeat music) >> Hello everyone, and welcome to The CUBE where we're covering the recent news from Hewlett Packard enterprise, making moves and storage. And with me are Omar Assad, vice president and general manager for Primary Storage, HCI and data management at HPE. And Sandeep Singh, who's the vice president of Storage Marketing at Hewlett Packard Enterprise. Gentlemen, welcome back to the CUBE Great to see you both. >> David, it's pleasure to be here. >> Always a pleasure talking to you David, thank you so much. >> Oh, it's my pleasure. Hey, so we just watched HPE make a big announcement and I wonder Sandeep if you could give us a quick recap. >> Yeah, of course, Dave. In the world of enterprise storage, there hasn't been a moment like this in decades upon at which everything is changing for data and infrastructure. And it's really coming at the nexus of data cloud and AI that's opening up the opportunity for customers across industries to accelerate their data-driven transformation. Building on that, we just unveiled a new vision for data that accelerates the data driving transformation for customers edge to cloud. And to pay that off, we introduce a new data services platform that consists of two game-changing innovations. First it's a data services cloud console which is a SAS based console that delivers cloud operational agility for customers. And it's designed to unify data operations through a suite of cloud services. Then our second announcement is HPE Electra. HB Electra is a cloud native data infrastructure portfolio to power your data edge to cloud. It's managed natively with data services cloud console and it brings that cloud operational model to customers wherever their data lives. These innovations are really combined with our industry leading AI ops platform which is HPE in foresight, and combine these innovations radically simplify and bring that cloud operational model to customers or data and infrastructure management. And it gives the opportunity for streamlining data management across the life cycle. These innovations are making it possible for organizations across the industries to unleash the power of data. >> That's kind of cool, I mean, a lot of the stuff we've been talking about for all these years is sort of this unified layer across all clouds on prem, AI injected in, I could tell you're excited and it sounds like you you can't wait to get these offerings in the hands of customers. But I wonder if we get back up a little a minute. Omer, maybe you could describe the problem statement that you're addressing with this announcement. What are customers really, what are their pain points? >> Excellent question, Dave. So in my role, as the general manager for data management and storage here at HPE, I get the wonderful opportunity to talk to hundreds of customers in a year. And as time has progressed, as the amount of data under organizations management has continued to increase. What I have noticed is that recently there are three main themes that are continuously emerging and are now bubbling at the top. The first one is storage infrastructure management itself is extremely complex for customers. While there have been lots of leaps and down progress in managing a single array or managing two arrays with a lot of simplification of the UI and maybe some modern UI are present. But as the problem starts to get at scale, as customers acquire more, and more assets to store and manage their data on premise, the management at scale is extremely complex. Yes, storage has gotten faster. Yes, flash has had a profound effect on performance availability and latency access to the data but infrastructure management and storage management as a whole has become a pain for customers and it's a constant theme as storage lifecycle management comes up, storage refresh has come up and deploying and managing storage infrastructure at scale comes up. So that's one of the main problems that I've been seeing as I talk to customers. Now, secondly, a lot of customers are now talking about two different elements. One is storage and storage deployment and life cycle management. And the second is the management of data that is stored on those storage devices. As the amount of data grows the silos continue to grow, a single view of life cycle management of data customers don't get to see it. And lastly, one of the biggest things that we see is a lot of customers are now asking, how can I extract a value from this data under my management because they can't seem to parse through the silos. So there is an incredible amount of productivity lost when it comes to data management as a whole, which is just fragmented into silos and then from a storage management. And when you put these two together and especially add two more elements to it which is hybrid management of data or a multi-cloud management of data, the silos and the sprawl just continues and there is nothing that is stitching together this thing at scale. So these are the three main themes that constantly appear in these discussions. Although in spite of these, a lot of modern enhancements in storage. >> Well, I wonder if I could comment guys. Is I've been following this industry for a number of years and you're absolutely right Omer. I mean, if you look at the amount of money and time and energy that's spent into or put into the data architectures, people are frustrated, they're not getting enough out of it. And I'd note that, the prevailing way in which we've attacked complexity historically is you build a better box. And well, that system was maybe easier to manage than the predecessor systems. All it did is create another silo and then the cloud, despite its inherent simplicity, that was another disconnected siloed. So then we threw siloed management solutions at the problem, and we're left with this collection of point solutions with data sort of trapped inside. So I wonder if you could give us your thoughts on that and do you agree? What data do you have around this problem statement? >> Yeah, Dave that's a great point. And actually ESG just recently conducted a survey of over 250 IT decision makers. And that actually brings one of the perfect validations of the problems that Omer, and you just articulated. What it showed is that 93% of the respondents indicated that storage and data management, that complexity is impeding their digital transformation. On average, the organizations have over 23 different data management tools which just typifies and is a perfect showcase of the fragmentation and the complexity that exists in that data management. And 95% of the respondents indicated that solving storage and data management, that complexity is a top 10 business initiative for them. And actually top five for 67% of the respondents. So it's a great validation across the board. >> Well, it's fresh in their minds too, because pre pandemic there was probably a mixed picture, right? It was probably well there's complacency or we're not moving fast enough, we have other priorities, but they were forced into this. Now they know what the real problem is. It's front and center. I liked that you're putting out there in your announcement, this sort of future state that you're envisioning for customers. And I wonder if we could sort of summarize that and share with our listeners that vision that you unveiled, what does it look like and how are you making it real? >> Yeah, overall, we feel very strongly that it's time for our customers to reimagine data management. And our vision is that customers need to break down the silos and complexity that plagues the distributed data environments. And they need to experience a new data experience across the board. That's going to help them accelerate their data-driven transformation. And we call this vision unified data ops. Unified data ops integrates data centric policies across the board to streamline data management, cloud native control and operations, to bring that agility of cloud and the operational model to wherever data lives. And AI driven insights and intelligence to make the infrastructure invisible. It delivers a whole new experience to customers to radically simplify and bring the agility of talent to data and data infrastructure. Streamlined data management, and really help customers innovate faster than ever before. And we're making the promise of unified data ops real by transforming the entire HPE storage business to a cloud native software defined data services. And that's through introducing a data services platform that expands HPE GreenLake. >> I mean, the key word I take away there Sandeep is invisible. I mean, as a customer I want you to abstract that complexity away that underlying infrastructure complexity is. I just don't want to see it anymore over. Omer, I wonder if we could start with the first part of the announcement. Maybe you can help us unpack data services, cloud console. I mean, people are immediately going to think it's just another software product to manage infrastructure. But to really innovate, I'm hoping that it's more than that. >> Absolutely, David, it's a lot more than that. What did we have done fundamentally at the root of the problem is we have taken the data and infrastructure control away from the hardware and through that, we provided a unified approach to manage the data wherever it lives. It's a full blown SAS console, which our customers get onto. And from there they can deploy appliances, manage appliances, lifecycle appliances and then they're not only stop at that, but then go ahead and start to get context around their data. But all of that is available through a SAS platform, a SAS console. 'Cause as every customer onboard themselves and their equipment and their storage infrastructure onto this console, then they can go ahead and define role-based access for different parts of their organization. They can also apply role-based access to HPE GreenLake management personnel, so they can come in and do and perform all the operations for the customers. We at the same console, by just being another access control methodology in that. And then in addition to that, data mobility is extremely important to our customers. How do you make data available in different hyperscaler clouds? If the customer's digital transformation requires that. So again, from that single cloud console, from that single data console, which we are naming here as data services console, customers are able to curate the data, maneuver the data, pre-position the data into different hyperscalers. But the beautiful thing is that the entire view of the storage infrastructure, the data with its that is stored on top of that access control methodologies and management framework is operational from a single SAS console which the customer can decide to give access to whichever management entity or authority comes into help them. And then what this leads us into is then combining these things into a northbound API. So anybody that wants to streamline operational manageability can then use these APIs to program against a single API which will then control the entire infrastructure on behalf of the customer. So if somebody, they, what this is, is it is bringing that cloud operational model that was so desired by each one of our customers into their data centers. And this is what I call an in-place transformation of a management experience for our customer by making them seamlessly available on a cloud operational model for their infrastructure. >> Yeah, and you've turned that into essentially an API with a lot of automation, that's great. So, okay. So that's kind of how you're trying to change the game here. You're charting new territory. You've talked to hundreds and hundreds of customers every year. I wonder if you could paint a picture from the customer perspective. How does their experience actually change? >> Wonderful, Dave. This allows me to break it down into bits and bites further for you. And I love that, right? So the way you look at it is, recently, the storage management from an, as we talked about earlier from an array perspective or maybe two arrays perspective has been simplified. I mean, it's a solved problem. But when you start to imagine deploying hundreds of arrays and these are large customers, they have massive amounts of data assets, storage management hasn't scaled along as the infrastructure scales. But if you look at the consumer world, you can have hundreds of devices, but the ownership model is completely set. So the inspiration for solving this problem for us actually lied, was inspired from consumerization of IT. And that's a big trend over here. So now we're changing the customer's ownership model, the customer's deployment model and the customer's data management model into a true cloud first model. So let me give some of the examples of that, right? So first of all, let's talk about deployment. So previously deployment has been a massive challenge for our customers. What does deployment in this new data services console world looks like? Devices show up, you rack them up and then you plug in the power cable, you plug in the network cable, and then you walk out of the data center. Data center administrator or the storage administrator, they will be on their iPad, on their data services console or iPhone or whatever the device of their choices, and from that console, from that point on, the device will be registered, onboarded. Its initial state will be given to it from the cloud. And if the customer has some predefined states for their previous deployment model already saved with the data console, they don't even need to do that. We'll just take that and apply that state and induct the device into the fleet. That's just one example. It's extremely simple. Plug in the power cable, plug in the network cable and the data center operational manager just walks out. After that you could be on the beach, you could be at your home, you could be driving in a car and this don't, I advise people not to fiddle with their I-phones when they're driving in a car, but still you could do it if you want to. So that's just one part from a deployment methodology perspective. Now, the second thing that Sandeep and I often bounce ideas bond is is it is provisioning of a workload. It's like a science these days. And is this array going to be able to absorb my workload? Is the latency going to go South? Does this workload latency profile match this particular piece of device in my data center? All of this is extremely manual. And it literally takes, I mean, if you talk to any of the customers or even analysts, deploying a workload is a massive challenge. It's a guesswork that you have to model and basically see how it works out. I think based on HPE info site, we're collecting hundreds and millions of data points from all these devices. So now to harness that and present that back to a customer in a very simple manner so that we can model on their behalf to the data services console, which is now workload of it. you just describe your workload. Hey, I'm going to need these many IOPS. And by the way, this happens to be my application. And that's it. On the backend, because we're managing your infrastructure, the cloud console understands your entire fleet. We are seeing the statistics and the telemetric coming off of your systems. And because now you've described the workload for us we can do that matching for you. And what intent based provisioning does is, describe your workloads in two or three clicks or maybe two or three API construct formats and we'll do the provisioning, the deployment and bringing it up for you on your behalf on the right pieces of infrastructure that matched it. And if you don't like our choices, you can manually change it as well. But from a provisioning perspective, a thing that took days can now come down to a couple of minutes of the description. And lastly then, global data management, distributed infrastructure from edge to cloud, invisible upgrades, only upgrading the right amount of infrastructure that needs the upgrade. All of that just comes rolling along with it, right? So those are some of the things that this data services console as a SAS management and scale allows you to do. >> And actually, if I can just jump in and add a little bit. What Omer described, especially with intent based provisioning, that's really bringing a paradigm shift to provisioning. It's shifting it from a long centric to app centric provisioning. And when you combine it with identity management and role-based access, what it means is that you're enabling self-service on demand provisioning of the underlying data infrastructure to accelerate the app workload deployment. And you're eliminating guesswork and providing the ability to be able to optimize service level objectives. >> Yeah, it sounds like you've really nailed in an elegant way that provisioning challenge. I've been saying for years if your primary expertise is deploying logical unit numbers you better find some other skills because the day is coming that that's just going to get automated away. So that's cool. There's another issue that I'm sure you've thought about, but I wonder if you could address. I mean, you've got the definition of cloud is changing, the cloud is expanding to on prem expand. The cloud is going out to the edge, it's going across clouds. And so security becomes a big issue that threat surface is expanding. The operating model is changing. So how are you thinking about addressing those security concerns? >> Excellent question, Dave. So most of the organizations that we've talked to... In today's modern world, almost every customer that I've talked to has deployed either some sort of a cloud console where they're either one of the customers for the hyperscalers or buy in for SAS based applications are pervasive across the customer base. And as you know, we were the first ones to introduce the automatic telemeter management through HPE info site. That's one of the largest storage SAS services in production today that we operate on behalf of our customers, which has, Dave, about 85% connectivity rate. So from that perspective, keeping customers data secure, keeping customers telemetry information secure, we're no stranger to that. Again, we follow all security protocols that any cloud operational SAS service would do so. Reverse tunneling, the firewall compliancy security audit logs that are published to our customers and published to customers chief information security officers. So all of those, what I call crossing the T's and dotted the I's, we do that with security expert and security policies for which each of our customers has a different set of rules. And we have a property engagement model that we go through that particular audit process for our customers. Then secondly, Dave, the data services cloud console is actually built on a fundamental cloud deployment technology that is not, sort of new. Aruba central, which is an Aruba management console which is also an HPE company it's been deployed, it's managing millions of access points in a SAS framework for our customers. So the fundamental building blocks of the data storage console from a basic enablement perspective come from the Aruba central console. And what we've taken is we've taken those generic cloud-based SAS services and then built data and storage centric SAS services on top of that and made them available to our customers. >> Yeah, I really like the Aruba. You picked that up several years ago . And it's same thing with, with info site, the way that you bring it to other parts of the portfolio. Those are really good signs to watch of successful acquisitions. All right, there's a lot here. I want to talk about the second part of the announcement. I know your branding team, you guys are serious about branding. That new product brand, maybe you could talk about that. >> So again, so delivering the cloud operational model is just the first piece, right? And now the second part of the announcement is delivering the cloud native hardware infrastructure which is extremely performance to go along with this cloud operational model. So what we have done Dave, in this announcement is we've announced HPE Electra. This is our new brand for our cloud native infrastructure to power your data and its appliances from core to the edge, to the cloud. And what it does is it takes the cloud operational model and this hardware is powered by that. It's completely wrapped around that. And so HPE Electra is available in two models right now, the HB electron 9,000, which is available for mission critical workloads, for those high intensity workloads with a hundred percent availability guarantee where no failure is ever an option. And then it's also available as HPE Electra 6,000, which is available for general purpose, business critical workloads, generally trying to address that mid range of the storage market. And both of these systems are full 100% NBME front and back. And they're powered by the same unified cloud management operational experience that the data cloud console provides. And what it does is it allows our customers to simplify the deployment model. It simplifies their management model and really, really allows them to focus on the context, the data and the app diversity, whereas data mobility, data connectivity, data management in a multi-cloud world is then completely obstructed from them. >> [Sandeep And Dave-- >> Go ahead, please. >> Just to jump in. HPE Electra combined with data services cloud console is delivering a cloud experience that makes deploying and scaling the application workloads as simple as flipping a switch. >> It really does. It really does. And I'm very comfortable in saying this, like HPE in foresight, we were the first in the industry to bring AI based elementary and support enabled metrics to work. And then here with data services console and the hardware that goes with it, we're just completely transforming the storage ownership and a storage management model. And for our customers, it's a seamless, non-disruptive upgrade with fully data in place upgrade. And they transform to a cloud operational model where they can manage their infrastructure better where they are through a complete consumer grade SAS console, is again the first of its kind, when you look at storage management and storage management at scale. >> And I like how you're emphasizing that management layer, but underneath you've got all the modern hardware technologies too which is important, because it's a performance it's got to be, a good price performance. So now can we bring this back again to the customers? What are the outcomes that this is going to enable for them? >> So I think Dave, the first and the foremost thing is as they scale their storage infrastructures, they don't have to think. It's really as simple as yeah, just send it to the data center, plug in the power cable, plug in the network cable and up it comes. And from that point onwards, the life cycle and the device management aspect are completely abstracted by the data services console. All they have to focus is I just have new capacity available to me and when I have an application, the system will figure it out for me where they need to deploy. So no more needing the guesswork, the Excel sheets of capacity management, the charge back models, none of that stuff is needed. And for customers that are looking to transform their applications, customers looking to refactor their applications into a hyperscaler model, or maybe transform from VM to containers, all they need to think about and focus is on that. The data will just follow these workloads from that perspective. >> And David, just to almost response here. As I speak with customers, one of the things I'm hearing from IT is that line of business really wants IT to deliver that agility of cloud. Yet IT also has to deliver all of the enterprise reliability, availability, all of the data services. And what's fantastic here is that through this cloud operational model, IT can deliver that agility, that line of business owners are looking for. At the same time they're been under pressure to do a lot more with less. And through this agility, IT is able to get time back, be able to focus more on the strategic projects, at the same time, be able to get time back to spend more time with our families. That's incredibly important. >> Well, I love the sort of mindset shift I'm seeing from HPE. We're not talking about how much the box weighs, we're talking about the customer experience. And I wonder, you know, that kind of leads me, Sandeep to how this kind of fits in to this new. Really to me, I'm seeing the transformation before our eyes but how does it fit into HPE's overall mission? >> Well Dave, our mission overall is to be the edge to cloud platform as a service company with HPE GreenLake, being the key to delivering that cloud experience. And as Omer put it, be able to deliver that cloud experience wherever the customer data lives. And today we're advancing HPE GreenLake as a service transformation of the HPE storage business to a software defined cloud data services business overall. And for our customers, this translates to our operational and ownership experience that unleashes their agility, their data and their innovation. So we're super excited. >> Guys, I can tell you're excited. Thanks so much for coming to the CUBE and summarizing the announcements, congratulations and best of luck to both of you and to HPE and your customers. >> Thank you, Dave. It was a pleasure. >> Thanks, Dave. (upbeat music)

(upbeat music) >> Edge computing is projected to be a multi-trillion dollar business. It's hard to really pinpoint the size of this market let alone fathom the potential of bringing software, compute, storage, AI and automation to the edge and connecting all that to clouds and on-prem systems. But what is the edge? Is it factories? Is it oil rigs, airplanes, windmills, shipping containers, buildings, homes, race cars. Well, yes and so much more. And what about the data? For decades we've talked about the data explosion. I mean, it's a mind-boggling but guess what we're going to look back in 10 years and laugh what we thought was a lot of data in 2020. Perhaps the best way to think about Edge is not as a place but when is the most logical opportunity to process the data and maybe it's the first opportunity to do so where it can be decrypted and analyzed at very low latencies. That defines the edge. And so by locating compute as close as possible to the sources of data to reduce latency and maximize your ability to get insights and return them to users quickly, maybe that's where the value lies. Hello everyone and welcome to this CUBE conversation. My name is Dave Vellante and with me to noodle on these topics is Omer Asad, VP and GM of Primary Storage and Data Management Services at HPE. Hello Omer, welcome to the program. >> Thanks Dave. Thank you so much. Pleasure to be here. >> Yeah. Great to see you again. So how do you see the edge in the broader market shaping up? >> Dave, I think that's a super important question. I think your ideas are quite aligned with how we think about it. I personally think enterprises are accelerating their sort of digitization and asset collection and data collection, they're typically especially in a distributed enterprise, they're trying to get to their customers. They're trying to minimize the latency to their customers. So especially if you look across industries manufacturing which has distributed factories all over the place they are going through a lot of factory transformations where they're digitizing their factories. That means a lot more data is now being generated within their factories. A lot of robot automation is going on, that requires a lot of compute power to go out to those particular factories which is going to generate their data out there. We've got insurance companies, banks, that are creating and interviewing and gathering more customers out at the edge for that. They need a lot more distributed processing out at the edge. What this is requiring is what we've seen is across analysts. A common consensus is this that more than 50% of an enterprises data especially if they operate globally around the world is going to be generated out at the edge. What does that mean? New data is generated at the edge what needs to be stored. It needs to be processed data. Data which is not required needs to be thrown away or classified as not important. And then it needs to be moved for DR purposes either to a central data center or just to another site. So overall in order to give the best possible experience for manufacturing, retail, especially in distributed enterprises, people are generating more and more data centric assets out at the edge. And that's what we see in the industry. >> Yeah. We're definitely aligned on that. There's some great points and so now, okay. You think about all this diversity what's the right architecture for these multi-site deployments, ROBO, edge? How do you look at that? >> Oh, excellent question, Dave. Every customer that we talked to wants SimpliVity and no pun intended because SimpliVity is reasoned with a simplistic edge centric architecture, right? Let's take a few examples. You've got large global retailers, they have hundreds of global retail stores around the world that is generating data that is producing data. Then you've got insurance companies, then you've got banks. So when you look at a distributed enterprise how do you deploy in a very simple and easy to deploy manner, easy to lifecycle, easy to mobilize and easy to lifecycle equipment out at the edge. What are some of the challenges that these customers deal with? These customers, you don't want to send a lot of IT staff out there because that adds cost. You don't want to have islands of data and islands of storage and promote sites because that adds a lot of states outside of the data center that needs to be protected. And then last but not the least how do you push lifecycle based applications, new applications out at the edge in a very simple to deploy manner. And how do you protect all this data at the edge? So the right architecture in my opinion needs to be extremely simple to deploy so storage compute and networking out towards the edge in a hyper converged environment. So that's we agree upon that. It's a very simple to deploy model but then comes how do you deploy applications on top of that? How do you manage these applications on top of that? How do you back up these applications back towards the data center, all of this keeping in mind that it has to be as zero touch as possible. We at HPE believe that it needs to be extremely simple, just give me two cables, a network cable, a power cable, fire it up, connect it to the network, push it state from the data center and back up it state from the edge back into the data center, extremely simple. >> It's got to be simple 'cause you've got so many challenges. You've got physics that you have to deal, you have latency to deal with. You got RPO and RTO. What happens if something goes wrong you've got to be able to recover quickly. So that's great. Thank you for that. Now you guys have heard news. What is new from HPE in this space? >> Excellent question, great. So from a deployment perspective, HPE SimpliVity is just gaining like it's exploding like crazy especially as distributed enterprises adopted as it's standardized edge architecture, right? It's an HCI box has got storage computer networking all in one. But now what we have done is not only you can deploy applications all from your standard V-Center interface from a data center, what have you have now added is the ability to backup to the cloud right from the edge. You can also back up all the way back to your core data center. All of the backup policies are fully automated and implemented in the distributed file system that is the heart and soul of the SimpliVity installation. In addition to that, the customers now do not have to buy any third-party software. Backup is fully integrated in the architecture and it's then efficient. In addition to that now you can backup straight to the client. You can back up to a central high-end backup repository which is in your data center. And last but not least, we have a lot of customers that are pushing the limit in their application transformation. So not only, we previously were one-on-one leaving VMware deployments out at the edge site now evolved also added both stateful and stateless container orchestration as well as data protection capabilities for containerized applications out at the edge. So we have a lot of customers that are now deploying containers, rapid manufacture containers to process data out at remote sites. And that allows us to not only protect those stateful applications but back them up back into the central data center. >> I saw in that chart, it was a line no egress fees. That's a pain point for a lot of CIOs that I talked to. They grit their teeth at those cities. So you can't comment on that or? >> Excellent question. I'm so glad you brought that up and sort of at the point that pick that up. So along with SimpliVity, we have the whole Green Lake as a service offering as well, right? So what that means Dave is, that we can literally provide our customers edge as a service. And when you compliment that with with Aruba Wired Wireless Infrastructure that goes at the edge, the hyperconverged infrastructure as part of SimpliVity that goes at the edge. One of the things that was missing with cloud backups is that every time you back up to the cloud, which is a great thing by the way, anytime you restore from the cloud there is that egress fee, right? So as a result of that, as part of the GreenLake offering we have cloud backup service natively now offered as part of HPE, which is included in your HPE SimpliVity edge as a service offering. So now not only can you backup into the cloud from your edge sites, but you can also restore back without any egress fees from HPE's data protection service. Either you can restore it back onto your data center, you can restore it back towards the edge site and because the infrastructure is so easy to deploy centrally lifecycle manage, it's very mobile. So if you want to deploy and recover to a different site, you could also do that. >> Nice. Hey, can you, Omer, can you double click a little bit on some of the use cases that customers are choosing SimpliVity for particularly at the edge and maybe talk about why they're choosing HPE? >> Excellent question. So one of the major use cases that we see Dave is obviously easy to deploy and easy to manage in a standardized form factor, right? A lot of these customers, like for example, we have large retailer across the US with hundreds of stores across US, right? Now you cannot send service staff to each of these stores. Their data center is essentially just a closet for these guys, right? So now how do you have a standardized deployment? So standardized deployment from the data center which you can literally push out and you can connect a network cable and a power cable and you're up and running and then automated backup, elimination of backup and state and DR from the edge sites and into the data center. So that's one of the big use cases to rapidly deploy new stores, bring them up in a standardized configuration both from a hardware and a software perspective and the ability to backup and recover that instantly. That's one large use case. The second use case that we see actually refers to a comment that you made in your opener, Dave, was when a lot of these customers are generating a lot of the data at the edge. This is robotics automation that is going up in manufacturing sites. These is racing teams that are out at the edge of doing post-processing of their cars data. At the same time there is disaster recovery use cases where you have campsites and local agencies that go out there for humanity's benefit. And they move from one site to the other. It's a very, very mobile architecture that they need. So those are just a few cases where we were deployed. There was a lot of data collection and there was a lot of mobility involved in these environments, so you need to be quick to set up, quick to backup, quick to recover. And essentially you're up to your next move. >> You seem pretty pumped up about this new innovation and why not. >> It is, especially because it has been taught through with edge in mind and edge has to be mobile. It has to be simple. And especially as we have lived through this pandemic which I hope we see the tail end of it in at least 2021 or at least 2022. One of the most common use cases that we saw and this was an accidental discovery. A lot of the retail sites could not go out to service their stores because mobility is limited in these strange times that we live in. So from a central recenter you're able to deploy applications. You're able to recover applications. And a lot of our customers said, hey I don't have enough space in my data center to back up. Do you have another option? So then we rolled out this update release to SimpliVity verse from the edge site. You can now directly back up to our backup service which is offered on a consumption basis to the customers and they can recover that anywhere they want. >> Fantastic Omer, thanks so much for coming on the program today. >> It's a pleasure, Dave. Thank you. >> All right. Awesome to see you, now, let's hear from Red Bull Racing an HPE customer that's actually using SimpliVity at the edge. (engine revving) >> Narrator: Formula one is a constant race against time Chasing in tens of seconds. (upbeat music) >> Okay. We're back with Matt Cadieux who is the CIO Red Bull Racing. Matt, it's good to see you again. >> Great to see you Dave. >> Hey, we're going to dig in to a real world example of using data at the edge in near real time to gain insights that really lead to competitive advantage. But first Matt tell us a little bit about Red Bull Racing and your role there. >> Sure. So I'm the CIO at Red Bull Racing and at Red Bull Racing we're based in Milton Keynes in the UK. And the main job for us is to design a race car, to manufacture the race car and then to race it around the world. So as CIO, we need to develop, the IT group needs to develop the applications use the design, manufacturing racing. We also need to supply all the underlying infrastructure and also manage security. So it's really interesting environment that's all about speed. So this season we have 23 races and we need to tear the car apart and rebuild it to a unique configuration for every individual race. And we're also designing and making components targeted for races. So 23 and movable deadlines this big evolving prototype to manage with our car but we're also improving all of our tools and methods and software that we use to design make and race the car. So we have a big can-do attitude of the company around continuous improvement. And the expectations are that we continue to say, make the car faster. That we're winning races, that we improve our methods in the factory and our tools. And so for IT it's really unique and that we can be part of that journey and provide a better service. It's also a big challenge to provide that service and to give the business the agility of needs. So my job is really to make sure we have the right staff, the right partners, the right technical platforms. So we can live up to expectations. >> And Matt that tear down and rebuild for 23 races, is that because each track has its own unique signature that you have to tune to or are there other factors involved? >> Yeah, exactly. Every track has a different shape. Some have lots of straight, some have lots of curves and lots are in between. The track surface is very different and the impact that has on tires, the temperature and the climate is very different. Some are hilly, some have big curbs that affect the dynamics of the car. So all that in order to win you need to micromanage everything and optimize it for any given race track. >> COVID has of course been brutal for sports. What's the status of your season? >> So this season we knew that COVID was here and we're doing 23 races knowing we have COVID to manage. And as a premium sporting team with Pharma Bubbles we've put health and safety and social distancing into our environment. And we're able to able to operate by doing things in a safe manner. We have some special exceptions in the UK. So for example, when people returned from overseas that they did not have to quarantine for two weeks, but they get tested multiple times a week. And we know they're safe. So we're racing, we're dealing with all the hassle that COVID gives us. And we are really hoping for a return to normality sooner instead of later where we can get fans back at the track and really go racing and have the spectacle where everyone enjoys it. >> Yeah. That's awesome. So important for the fans but also all the employees around that ecosystem. Talk about some of the key drivers in your business and some of the key apps that give you competitive advantage to help you win races. >> Yeah. So in our business, everything is all about speed. So the car obviously needs to be fast but also all of our business operations need to be fast. We need to be able to design a car and it's all done in the virtual world, but the virtual simulations and designs needed to correlate to what happens in the real world. So all of that requires a lot of expertise to develop the simulations, the algorithms and have all the underlying infrastructure that runs it quickly and reliably. In manufacturing we have cost caps and financial controls by regulation. We need to be super efficient and control material and resources. So ERP and MES systems are running and helping us do that. And at the race track itself. And in speed, we have hundreds of decisions to make on a Friday and Saturday as we're fine tuning the final configuration of the car. And here again, we rely on simulations and analytics to help do that. And then during the race we have split seconds literally seconds to alter our race strategy if an event happens. So if there's an accident and the safety car comes out or the weather changes, we revise our tactics and we're running Monte-Carlo for example. And use an experienced engineers with simulations to make a data-driven decision and hopefully a better one and faster than our competitors. All of that needs IT to work at a very high level. >> Yeah, it's interesting. I mean, as a lay person, historically when I think about technology in car racing, of course I think about the mechanical aspects of a self-propelled vehicle, the electronics and the light but not necessarily the data but the data's always been there. Hasn't it? I mean, maybe in the form of like tribal knowledge if you are somebody who knows the track and where the hills are and experience and gut feel but today you're digitizing it and you're processing it and close to real time. Its amazing. >> I think exactly right. Yeah. The car's instrumented with sensors, we post process and we are doing video image analysis and we're looking at our car, competitor's car. So there's a huge amount of very complicated models that we're using to optimize our performance and to continuously improve our car. Yeah. The data and the applications that leverage it are really key and that's a critical success factor for us. >> So let's talk about your data center at the track, if you will. I mean, if I can call it that. Paint a picture for us what does that look like? >> So we have to send a lot of equipment to the track at the edge. And even though we have really a great wide area network link back to the factory and there's cloud resources a lot of the tracks are very old. You don't have hardened infrastructure, don't have ducks that protect cabling, for example and you can lose connectivity to remote locations. So the applications we need to operate the car and to make really critical decisions all that needs to be at the edge where the car operates. So historically we had three racks of equipment like I said infrastructure and it was really hard to manage, to make changes, it was too flexible. There were multiple panes of glass and it was too slow. It didn't run our applications quickly. It was also too heavy and took up too much space when you're cramped into a garage with lots of environmental constraints. So we'd introduced hyper convergence into the factory and seen a lot of great benefits. And when we came time to refresh our infrastructure at the track, we stepped back and said, there's a lot smarter way of operating. We can get rid of all the slow and flexible expensive legacy and introduce hyper convergence. And we saw really excellent benefits for doing that. We saw up three X speed up for a lot of our applications. So I'm here where we're post-processing data. And we have to make decisions about race strategy. Time is of the essence. The three X reduction in processing time really matters. We also were able to go from three racks of equipment down to two racks of equipment and the storage efficiency of the HPE SimpliVity platform with 20 to one ratios allowed us to eliminate a rack. And that actually saved a $100,000 a year in freight costs by shipping less equipment. Things like backup mistakes happen. Sometimes the user makes a mistake. So for example a race engineer could load the wrong data map into one of our simulations. And we could restore that DDI through SimpliVity backup at 90 seconds. And this enables engineers to focus on the car to make better decisions without having downtime. And we sent two IT guys to every race, they're managing 60 users a really diverse environment, juggling a lot of balls and having a simple management platform like HPE SimpliVity gives us, allows them to be very effective and to work quickly. So all of those benefits were a huge step forward relative to the legacy infrastructure that we used to run at the edge. >> Yeah. So you had the nice Petri dish in the factory so it sounds like your goals are obviously number one KPIs speed to help shave seconds, awesome time, but also cost just the simplicity of setting up the infrastructure is-- >> That's exactly right. It's speed, speed, speed. So we want applications absolutely fly, get to actionable results quicker, get answers from our simulations quicker. The other area that speed's really critical is our applications are also evolving prototypes and we're always, the models are getting bigger. The simulations are getting bigger and they need more and more resource and being able to spin up resource and provision things without being a bottleneck is a big challenge in SimpliVity. It gives us the means of doing that. >> So did you consider any other options or was it because you had the factory knowledge? It was HCI was very clearly the option. What did you look at? >> Yeah, so we have over five years of experience in the factory and we eliminated all of our legacy infrastructure five years ago. And the benefits I've described at the track we saw that in the factory. At the track we have a three-year operational life cycle for our equipment. When in 2017 was the last year we had legacy as we were building for 2018, it was obvious that hyper-converged was the right technology to introduce. And we'd had years of experience in the factory already. And the benefits that we see with hyper-converged actually mattered even more at the edge because our operations are so much more pressurized. Time is even more of the essence. And so speeding everything up at the really pointy end of our business was really critical. It was an obvious choice. >> Why SimpliVity, why'd you choose HPE SimpliVity? >> Yeah. So when we first heard about hyper-converged way back in the factory, we had a legacy infrastructure overly complicated, too slow, too inflexible, too expensive. And we stepped back and said there has to be a smarter way of operating. We went out and challenged our technology partners, we learned about hyperconvergence, would enough the hype was real or not. So we underwent some PLCs and benchmarking and the PLCs were really impressive. And all these speed and agility benefits we saw and HPE for our use cases was the clear winner in the benchmarks. So based on that we made an initial investment in the factory. We moved about 150 VMs and 150 VDIs into it. And then as we've seen all the benefits we've successfully invested and we now have an estate in the factory of about 800 VMs and about 400 VDIs. So it's been a great platform and it's allowed us to really push boundaries and give the business the service it expects. >> Awesome fun stories, just coming back to the metrics for a minute. So you're running Monte Carlo simulations in real time and sort of near real-time. And so essentially that's if I understand it, that's what ifs and it's the probability of the outcome. And then somebody got to make, then the human's got to say, okay, do this, right? Was the time in which you were able to go from data to insight to recommendation or edict was that compressed and you kind of indicated that. >> Yeah, that was accelerated. And so in that use case, what we're trying to do is predict the future and you're saying, well and before any event happens, you're doing what ifs and if it were to happen, what would you probabilistic do? So that simulation, we've been running for awhile but it gets better and better as we get more knowledge. And so that we were able to accelerate that with SimpliVity but there's other use cases too. So we also have telemetry from the car and we post-process it. And that reprocessing time really, is it's very time consuming. And we went from nine, eight minutes for some of the simulations down to just two minutes. So we saw big, big reductions in time. And ultimately that meant an engineer could understand what the car was doing in a practice session, recommend a tweak to the configuration or setup of it and just get more actionable insight quicker. And it ultimately helps get a better car quicker. >> Such a great example. How are you guys feeling about the season, Matt? What's the team's sentiment? >> I think we're optimistic. Thinking our simulations that we have a great car we have a new driver lineup. We have the Max Verstapenn who carries on with the team and Sergio Cross joins the team. So we're really excited about this year and we want to go and win races. And I think with COVID people are just itching also to get back to a little degree of normality and going racing again even though there's no fans, it gets us into a degree of normality. >> That's great, Matt, good luck this season and going forward and thanks so much for coming back in theCUBE. Really appreciate it. >> It's my pleasure. Great talking to you again. >> Okay. Now we're going to bring back Omer for quick summary. So keep it right there. >> Narrator: That's where the data comes face to face with the real world. >> Narrator: Working with Hewlett Packard Enterprise is a hugely beneficial partnership for us. We're able to be at the cutting edge of technology in a highly technical, highly stressed environment. There is no bigger challenge than Formula One. (upbeat music) >> Being in the car and driving in on the limit that is the best thing out there. >> Narrator: It's that innovation and creativity to ultimately achieves winning of this. >> Okay. We're back with Omer. Hey, what did you think about that interview with Matt? >> Great. I have to tell you, I'm a big formula One fan and they are one of my favorite customers. So obviously one of the biggest use cases as you saw for Red Bull Racing is track side deployments. There are now 22 races in a season. These guys are jumping from one city to the next they got to pack up, move to the next city, set up the infrastructure very very quickly. An average Formula One car is running the thousand plus sensors on, that is generating a ton of data on track side that needs to be collected very quickly. It needs to be processed very quickly and then sometimes believe it or not snapshots of this data needs to be sent to the Red Bull back factory back at the data center. What does this all need? It needs reliability. It needs compute power in a very short form factor. And it needs agility quick to set up, quick to go, quick to recover. And then in post processing they need to have CPU density so they can pack more VMs out at the edge to be able to do that processing. And we accomplished that for the Red Bull Racing guys in basically two of you have two SimpliVity nodes that are running track side and moving with them from one race to the next race to the next race. And every time those SimpliVity nodes connect up to the data center, collect up to a satellite they're backing up back to their data center. They're sending snapshots of data back to the data center essentially making their job a whole lot easier where they can focus on racing and not on troubleshooting virtual machines. >> Red bull Racing and HPE SimpliVity. Great example. It's agile, it's it's cost efficient and it shows a real impact. Thank you very much Omer. I really appreciate those summary comments. >> Thank you, Dave. Really appreciate it. >> All right. And thank you for watching. This is Dave Volante for theCUBE. (upbeat music)