HPE Discover Barcelona 2023 | Patrick Osborne | HPE

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Patrick Osborne

SVP & GM, Hybrid Cloud Data Storage

HPE

Data reshaped: HPE’s AI-enhanced storage for the modern enterprise

At the forefront of technological innovation, the convergence of artificial intelligence, storage solutions and hybrid cloud is reshaping the landscape of enterprise computing.As AI disrupts industries and becomes an integral part of modern enterprises, Hewlett Packard Enterprise Co. is leading the way with solutions designed to empower organizations in harnessing the full potential of AI.“AI is disrupting everything at this point,” said Patrick Osborne (pictured), senior vice president and general manager of hybrid cloud data storage at HPE. “What we found is that for the customers that are doing AI native and this type of application development, they need a new level of performance scale

play_circle_outline Unveiling the Game-Changing AI Disruption: An Exclusive Interview with Patrick Osborne

play_circle_outline Importance of data sovereignty and customers wanting control over their data

play_circle_outline Focus on data conversation, performance, scale, and data prominence and security

play_circle_outline Data protection and auditing services for AI workloads

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Patrick Osborne | HPE

Patrick Osborne

SVP & GM, Hybrid Cloud Data Storage HPE

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(bright music) (logo whooshing)

>> Got it. (crowd chattering)

>> Hola, and welcome back everyone to "TheCUBE's" live and ongoing coverage of HPE Discover Barcelona. I'm your host, Rebecca Knight, along with my co-host and analyst Rob Strechay. We are welcoming back to "TheCUBE", Patrick Osborne. He is the SVP and GM Hybrid Cloud Data Storage at HPE. Welcome, direct from Boston Massachusetts.

>> Yes, the center

of the universe. >> All of us.

>> Yes, exactly. >> Yes, exactly. We're here in Barcelona. (Patrick chuckling) How is Boston operating

without us, I don't know. >> Oh, I know, I know, I know. (Rebecca chuckling) Thank you for having me, it's great to be back.

>> So, we heard Antonio Neri on the main

stage this morning. >> Yep.

>> There's a head-spinning number of announcements, a couple of new things from HPE Storage, as well, that he hinted at.

>> Yep. >> But we have you here to tell our viewers more about what you're announcing.

>> Absolutely, so, obviously, AI disrupting everything, right, at this point, it's a huge opportunity for not only the industry, but our enterprise customers. We use AI all the time in our products, right, to provide a differentiated product experience, so if you're not doing that, you're going to be disrupted, but what we found is that for the customers that are doing AI native, and this type of application development, they needed a new level of performance, scale, a lot of attributes in the storage layer that's foundational for you to do all of your data preparation, data analysis, and ultimately, building models, and tuning, and inference, so we made some announcements today around GreenLake for file, right? Which is a unstructured data platform delivered through our Greenlake cloud platform, and provides, you know, a great level of performance and scale for our customers for these workloads.

>> And how are you seeing the adoption of AI on that platform? How's that adoption going?

>> Yeah, it's going really well. So, we have a number of different customers in their life cycle, right? So, when you think about the AI systems-ness, and how you're going to deploy that on-prem, you have, you know, your developers and your data architects, but we have customers that are just beginning their journey, right? So, they're going to start with a set of systems that are going to allow you to do tuning, inference, sort of kick the tires, and we have, you know, customers that'll start, at the, you know, terabyte and petabyte scale, and then we have customers that are really well on their way to using models, and doing inference and training, and they're a little bit more advanced, and so, they're at, you know, the tens of petabytes. And then we have some of our largest customers here, you saw people providing GPUs as a service on stage, and those folks are, we have people regularly coming to us asking for data sets now in the hundreds of petabytes, right? And literally, you know, terabytes per second throughput, so when you think about the maturity curve, most folks start small, right, and begin their journey, and then we have, you know, folks who are fully at scale and really need that performance and scalability.

>> Well, this morning, we heard Antonio talking about sort of the three sets of customers

>> Yes. >> In terms of what their needs are, and then also, as you said, where they are in their journey. How do you think about these customers

>> Yes. >> And approach them in terms of working with them in custom ways, but also in ways that you can give them solutions off the shelf too?

>> Absolutely, so, I sort of bucketize our customers

into two areas. >> Bucketize?

>> Yeah, yeah. >> I love it. (laughs)

>> We're making new words here,

>> Yeah. >> It's a very Boston thing to do. (Rebecca chuckling) And so, I've got customers who are, they're modernizing their workloads. You think about themes, we talk about private cloud, we talk about modernization, bringing workloads to a hybrid deployment, and then we have all these new workloads, right? Which are essentially based off a new set of application constructs, you know, a lot of heavy on kubernetes, containerized workloads, scale, and so, for the customers that fit in that area, what we want to do, and these are customers struggling with operationalizing these workloads in the data center, right? They're all very new, but they need reliability, they need availability, they need performance, they need things like backup and data protection, they need... Oftentimes, we have customers that say, hey, I've got 40% growth in my kubernetes environment, but I don't know how to map my application developers to how much storage they're using, right? So it's a lot of things that we solved in the mode one application world that are now coming up very quickly in this AI-driven enterprise world as well, too.

>> Yeah, I think that's one of the keys is that the new term of platform engineering, and how people are building it out is what was IT is now transforming into that, and we're seeing that there's a balance, and we've been talking about this, that there's a kind of an equilibrium between where are net new applications going between cloud and on-prem? And it's almost a fifty-fifty split

>> Yep. >> That we're seeing. One of the things that people always ask us about is, you know, they're like, hey, you see the market, what about cost, efficiency, and security? What kind of answers do you give to your, you know, the customers and prospects when they're looking at that, and looking at GreenLake for file and for AI workloads?

>> Yeah, so definitely for AI workloads, the conversation usually starts with a data conversation, because the first step is creating that data lake, and really sort of doing a lot of data prep, understanding what you're doing, and so, that's more of a little bit of an efficiency, so I need to understand where my data is, how to tag it, how to understand it. Then when we start talking about that in the context of performance and scale, right? Then we, you know, start to understand what those parameters are. Do I have a throughput problem? Do I have a capacity problem? And some of the solutions that we talk today address all those areas, right? So it's very efficient architecture in terms of you can start off fairly small and scale into the petascale, not only from a capacity perspective, but from a performance scale. Many of these workloads now are very different in the way that data's laid out. It's a lot of unstructured data. You're talking hundreds of millions, if not billions of small files, right? Constantly reading files, writing out checkpoints, so it's a very data-intensive workload, the entire flow for AI, so you have to make sure that you have, you know, a complete aggregated NVMe-based architecture, so we provide that, and then I think the interesting thing is around data prominence and security as well, too, right? That's a continued topic in this area.

>> Yeah, I was going to say, that whole part of it, the data sovereignty

aspect of it >> Yes.

>> Has to be a huge reason why people are looking at GreenLake for file, and you know, to put their AI workloads on there is, hey, I, you know, I can't go to the cloud with this data, or I don't want to, or it's intellectual property,

>> Absolutely. >> And I'm really worried about the security of it. Is that a lot of the conversations-

>> Yeah, so, many of our mid-range and higher-end enterprise customers struggle with this on a daily basis. So, from an adoption perspective, they want to use their data centers or a well-known colocation environment partner, right? So we made some announcements last year for our private cloud with Equinix, for example, and so, providing those type of solutions where you can have provenance over your data, but it's cloud-adjacent

>> Yes. >> From a networking latency perspective, and being able to use some of that tool chain, but I've met with a ton of customers this week who are, you know, bringing these workloads on-prem, 'cause that's where the data is. So, ultimately, at the end of the day, is that these AI data sets have a huge amount of gravity to them, and you want to bring the workloads to that, and that's what we're providing with GreenLake for file, but while giving them a cloud operational model, right?

>> When it comes to AI, it sounds as though so many customers are wary of making a giant

and expensive mistake in terms >> Mm.

>> Of how they deploy it. >> Yes. How do you walk through cust... How do you walk through the decision tree with customers? You've already laid out the things that are on their minds

in terms of where they >> Yes.

>> Need things to be, and how they need things to work, but how do you sort of hold their hands, and help them, and walk them through this process?

>> Yeah, so, they talked a little bit today in the keynote about, you know, we have a ton of expertise and advisory services, so we do that. We often work with our customers to show them the journey that we've taken, so we use AIOps in our products all the time to make a, you know, a better product experience, and so, we're on, you know, essentially, our fifth version of our data lake, and our AI, and machine learning that we use in the products, so we often give them some of our best practices. I think from a cost in stepping in, you know, what we're here to provide is, you know, essentially, like, reference architecture, a system, the ability to take what they presented on stage and make that very easy for a customer to consume. The two personas that we like to talk to are essentially the AI container developer, right? And then whoever is the application and data architect, right? And they don't really want to care about infrastructure, really, at the end of the day. They want to be able to understand their data, the outcomes they're driving to, and get that intelligence, and so, it's our job to provide that in a very easy package solution, all accessible through APIs, and then we offer it in a GreenLake fashion, right? So if you don't want to lay out, you know, a large amount of CapEx for GPUs, for storage, for the fabric, for, you know, the software, we can go through it in a stepped consumption model for customers starting small, you know, scaling up into the mid, and then, you know, and then beyond, which is very popular.

>> I think that's... I mean. that's how they, you know, consume

in the cloud anyways. >> Yes.

>> And I think that's, when we talk about hybrid, and I talk about Antonio being very early on into hybrid and really leaning into it, it is about that consumption model, and how I get to that, and how I normalize it to be more of a, you know, ARR, I will get that right today, (Rebecca chuckling) model versus, you know, in a subscription model, so, how are you seeing that, and how's been the momentum, also, on the other side with the Altera, or it's...

>> Alletra, yep. >> Alletra. I don't know why I can't get that name right either,

but how's momentum? >> Yeah, it's been great. I mean, so, the model validates itself, so we're trying to bring the predictable to the unpredictable, really, and that's what happens a lot in storage, in general, right? Especially when in the context of AI is a form of workloads that are being deployed on some of these new application paradigms driven by Kubernetes, and you know, there's a number of different parts of it, but that's super unpredictable, right? And so, for us, storage as a service has been super popular, so we've grown that in essence, you know, 45% year-over-year, you know, for us, and so, the customers really like that model where they can provide a sort of a minimum commitment level, right? And then be able to burst, expand, depending on their needs, 'cause if, for example, as you say, they dip their toes in the water of AI, and all of a sudden, now, you've got a larger model you're dealing with, you know, a lot of results, machine language, and machine learning produce, you know, quite a bit of data, and so that unpredictable piece they can deal with, you know, sort of ad hoc during their journey, and so, for us, and then also providing all of the, I guess you'd say, unsexy infrastructure capabilities around that, which is how do I DR it, how do I back it up? How do I map consumption of the developers to how much storage they're using, right? I mean, these are classic problems that need to get solved for our customers.

>> Your colleague just called those the dirty jobs of AI.

>> Yes, exactly. >> So the ones no one

wants to do, but that really >> Yeah.

>> Need to get done. >> 100%, yeah. And it's often, too, because when we talk to our customers, and these environments become business and mission critical really fast, right?

>> Yeah. >> So they move from sandbox and testing into full-blown, you know, a part of their enterprise workflow, right? It's very, very important, so we need to support them as such and, you know, and give them the ability to scale.

>> Yeah, and I think even Antonio touched on it earlier today, it also becomes a component of corporate governance, because, you know, if you don't know where your data's coming from, how the models are built, and how all of this is, and where did you, how did you then train the models on what data?

And you can't prove that, >> Yeah.

>> Especially over here where, you know, PII, if you're using anything PII to go and train a model, you're quickly going to run afoul with GDPR and other things here, you know, locally. How are you seeing people, like, lean in towards you and say, hey, this is really, you know, this is the performance we need in these different places? Because to your point, you know, with being cloud-adjacent, or being more towards the edge, and what are some of the demands you're seeing in that more hybrid model?

>> Yeah, so a couple of things. I think one of the areas we see a lot of growth is definitely on the edge, right? And so, there's more applications being deployed in places outside of the traditional data center, colo cloud, right? And they're doing some really interesting things. We've met, we have a number of customers here talking today about, you know, retail, grocery stores, you know, all these places, you know, new fan experiences, all those use modern containerized applications. They use video for inference, and security, and buying patterns, and you know, in the grocery store, they do, you know, real-time, just-in-time, you know, inventory and analytics, and so, there's a lot of really complex things that are happening outside, so you've got that edge-to-core model, which is really interesting. And then, you know, from a hybrid perspective, I think one of the big things for us is being able to allow customers to place the data where they need it, depending on the workloads, right? And so, the workloads are definitely, in the case of AI coming to the data, right? And so, allowing our customers to choose whether they want that on-prem or, you know, up in the cloud is, you know, super important, so that's why we provide all of these data services, right, that are abstracted from the physical storage to be able to provide movement of data, placement of it, and so, that's been very successful for us.

>> Sustainability is a real theme at this conference.

>> Yes. >> And we heard Antonio talk about it, a lot of people on "TheCUBE" have talked about it. AI is an energy-greedy technology. How does your organization, HPE Storage approach this issue? And are you hearing this more and more from customers as a key concern as they are deploying AI?

>> Yeah, so especially here, in the EMEA theater, right? I get the luxury of being a exec sponsor for a number of customers, both in, you know, specific sectors like financial services, or, but definitely in the service provider market, like, very, very concerned with sustainability. It's usually on the first one to two pages of their quarterly or yearly prospectus, and so, we need to give them visibility into our infrastructure and how it's operating, right? So, power, cooling, and so, we have our own sustainability dashboard that we provide for all of our storage and data management products. We also have a ton of observability, right? We think about OpsRamp, and providing that level of observability that they can, you know, gain data out of that, and understand how to map application usage and data usage to the infrastructure. And then there's a lot of onus on us to make sure that our upstream suppliers, right, that we work with are providing sustainable products, not only in the way they're manufactured, how they're recycled, being in the Boston area, one of our big offices in that area for HPE does the circular economy, so, recycling of equipment, and so, now, we have customers that are asking us for a 7 to 10-year TCO on storage, right? And so, that means, you know, making sure that we come and sustainably upgrade equipment, right? Recycle that in a responsible manner. So it's all part of a, you know, a big ecosystem that HPE provides for our customers.

>> Yeah, I think that's... I think one of the big keys is how do you bring it all together? And I think one of the other things that people worry about, especially in AI, is data protection.

>> Yes. >> How are you addressing that from those workloads and being able to protect them?

>> Yeah, so a couple of the things that have really, that our customers like about GreenLake cloud platform, specifically the data services that we provide, so, it'll give you a view when you drop into the cloud console, 'cause really, at the end of the day, people think about GreenLake sometimes as a consumption model, but for me, on the product side, it's a product experience, right? So you go, this is our hybrid cloud console, so you start in at greenlake.hpe.com, and then you go drop right into your data services cloud console, right? And so, every workload that you provision, all of the storage, and you know, file shares, you know, any type of application provision that happens, you get to automatically build in all this templating of SLA. So, what's my RTO and RPO objective? How fast do I want to back up? Is this a platinum level app? Is this a, you know, silver level app? And so what we can do is when those are deployed, that gets automatically applied, and then we can view the adherence to that SLA, and it's all audited, so when you talk about, you know, provenance, and, you know, making sure you have a data signature, we can also audit and show customers that everything is being backed up, it has a, you know, it's recoverable in the case of, you know, a disaster, either, you know, you have a disruption on-prem, or you know, and god forbid, you know, you have a ransomware issue, so, all this is services that we provide in the platform that customers can set up. It's easy to use and it's super automated.

>> Excellent, well, Patrick, thank you so much for-

>> Well, I was going to say, >> Oh.

>> Next time, if we're in Spain again, we'll make you do it in Spanish, 'cause I know you used to live here as well, so... (Patrick laughing)

>> I did, I did, that'll be (Rob chuckling)

my next trick, right, yeah. >> Very cool, yes. We will absolutely do that,

yes. >> Yeah, awesome.

>> You can bet on it. Patrick, thank you so much for

coming on "TheCUBE". >> Thank you for having me.

>> Returning to "TheCUBE". I'm Rebecca Knight for Rob Strachay. Stay tuned for more of "TheCUBE's" live coverage of HPE Discover Barcelona. We'll be back right after this. You're watching "TheCUBE", the leader in high-tech technology enterprise coverage. (bright music)

Patrick Osborne | HPE

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(bright music) (logo whooshing)

>> Got it. (crowd chattering)

>> Hola, and welcome back everyone to "TheCUBE's" live and ongoing coverage of HPE Discover Barcelona. I'm your host, Rebecca Knight, along with my co-host and analyst Rob Strechay. We are welcoming back to "TheCUBE", Patrick Osborne. He is the SVP and GM Hybrid Cloud Data Storage at HPE. Welcome, direct from Boston Massachusetts.

>> Yes, the center

of the universe. >> All of us.

>> Yes, exactly. >> Yes, exactly. We're here in Barcelona. (Patrick chuckling) How is Boston operating

without us, I don't know. >> Oh, I know, I know, I know. (Rebecca chuckling) Thank you for having me, it's great to be back.

>> So, we heard Antonio Neri on the main

stage this morning. >> Yep.

>> There's a head-spinning number of announcements, a couple of new things from HPE Storage, as well, that he hinted at.

>> Yep. >> But we have you here to tell our viewers more about what you're announcing.

>> Absolutely, so, obviously, AI disrupting everything, right, at this point, it's a huge opportunity for not only the industry, but our enterprise customers. We use AI all the time in our products, right, to provide a differentiated product experience, so if you're not doing that, you're going to be disrupted, but what we found is that for the customers that are doing AI native, and this type of application development, they needed a new level of performance, scale, a lot of attributes in the storage layer that's foundational for you to do all of your data preparation, data analysis, and ultimately, building models, and tuning, and inference, so we made some announcements today around GreenLake for file, right? Which is a unstructured data platform delivered through our Greenlake cloud platform, and provides, you know, a great level of performance and scale for our customers for these workloads.

>> And how are you seeing the adoption of AI on that platform? How's that adoption going?

>> Yeah, it's going really well. So, we have a number of different customers in their life cycle, right? So, when you think about the AI systems-ness, and how you're going to deploy that on-prem, you have, you know, your developers and your data architects, but we have customers that are just beginning their journey, right? So, they're going to start with a set of systems that are going to allow you to do tuning, inference, sort of kick the tires, and we have, you know, customers that'll start, at the, you know, terabyte and petabyte scale, and then we have customers that are really well on their way to using models, and doing inference and training, and they're a little bit more advanced, and so, they're at, you know, the tens of petabytes. And then we have some of our largest customers here, you saw people providing GPUs as a service on stage, and those folks are, we have people regularly coming to us asking for data sets now in the hundreds of petabytes, right? And literally, you know, terabytes per second throughput, so when you think about the maturity curve, most folks start small, right, and begin their journey, and then we have, you know, folks who are fully at scale and really need that performance and scalability.

>> Well, this morning, we heard Antonio talking about sort of the three sets of customers

>> Yes. >> In terms of what their needs are, and then also, as you said, where they are in their journey. How do you think about these customers

>> Yes. >> And approach them in terms of working with them in custom ways, but also in ways that you can give them solutions off the shelf too?

>> Absolutely, so, I sort of bucketize our customers

into two areas. >> Bucketize?

>> Yeah, yeah. >> I love it. (laughs)

>> We're making new words here,

>> Yeah. >> It's a very Boston thing to do. (Rebecca chuckling) And so, I've got customers who are, they're modernizing their workloads. You think about themes, we talk about private cloud, we talk about modernization, bringing workloads to a hybrid deployment, and then we have all these new workloads, right? Which are essentially based off a new set of application constructs, you know, a lot of heavy on kubernetes, containerized workloads, scale, and so, for the customers that fit in that area, what we want to do, and these are customers struggling with operationalizing these workloads in the data center, right? They're all very new, but they need reliability, they need availability, they need performance, they need things like backup and data protection, they need... Oftentimes, we have customers that say, hey, I've got 40% growth in my kubernetes environment, but I don't know how to map my application developers to how much storage they're using, right? So it's a lot of things that we solved in the mode one application world that are now coming up very quickly in this AI-driven enterprise world as well, too.

>> Yeah, I think that's one of the keys is that the new term of platform engineering, and how people are building it out is what was IT is now transforming into that, and we're seeing that there's a balance, and we've been talking about this, that there's a kind of an equilibrium between where are net new applications going between cloud and on-prem? And it's almost a fifty-fifty split

>> Yep. >> That we're seeing. One of the things that people always ask us about is, you know, they're like, hey, you see the market, what about cost, efficiency, and security? What kind of answers do you give to your, you know, the customers and prospects when they're looking at that, and looking at GreenLake for file and for AI workloads?

>> Yeah, so definitely for AI workloads, the conversation usually starts with a data conversation, because the first step is creating that data lake, and really sort of doing a lot of data prep, understanding what you're doing, and so, that's more of a little bit of an efficiency, so I need to understand where my data is, how to tag it, how to understand it. Then when we start talking about that in the context of performance and scale, right? Then we, you know, start to understand what those parameters are. Do I have a throughput problem? Do I have a capacity problem? And some of the solutions that we talk today address all those areas, right? So it's very efficient architecture in terms of you can start off fairly small and scale into the petascale, not only from a capacity perspective, but from a performance scale. Many of these workloads now are very different in the way that data's laid out. It's a lot of unstructured data. You're talking hundreds of millions, if not billions of small files, right? Constantly reading files, writing out checkpoints, so it's a very data-intensive workload, the entire flow for AI, so you have to make sure that you have, you know, a complete aggregated NVMe-based architecture, so we provide that, and then I think the interesting thing is around data prominence and security as well, too, right? That's a continued topic in this area.

>> Yeah, I was going to say, that whole part of it, the data sovereignty

aspect of it >> Yes.

>> Has to be a huge reason why people are looking at GreenLake for file, and you know, to put their AI workloads on there is, hey, I, you know, I can't go to the cloud with this data, or I don't want to, or it's intellectual property,

>> Absolutely. >> And I'm really worried about the security of it. Is that a lot of the conversations-

>> Yeah, so, many of our mid-range and higher-end enterprise customers struggle with this on a daily basis. So, from an adoption perspective, they want to use their data centers or a well-known colocation environment partner, right? So we made some announcements last year for our private cloud with Equinix, for example, and so, providing those type of solutions where you can have provenance over your data, but it's cloud-adjacent

>> Yes. >> From a networking latency perspective, and being able to use some of that tool chain, but I've met with a ton of customers this week who are, you know, bringing these workloads on-prem, 'cause that's where the data is. So, ultimately, at the end of the day, is that these AI data sets have a huge amount of gravity to them, and you want to bring the workloads to that, and that's what we're providing with GreenLake for file, but while giving them a cloud operational model, right?

>> When it comes to AI, it sounds as though so many customers are wary of making a giant

and expensive mistake in terms >> Mm.

>> Of how they deploy it. >> Yes. How do you walk through cust... How do you walk through the decision tree with customers? You've already laid out the things that are on their minds

in terms of where they >> Yes.

>> Need things to be, and how they need things to work, but how do you sort of hold their hands, and help them, and walk them through this process?

>> Yeah, so, they talked a little bit today in the keynote about, you know, we have a ton of expertise and advisory services, so we do that. We often work with our customers to show them the journey that we've taken, so we use AIOps in our products all the time to make a, you know, a better product experience, and so, we're on, you know, essentially, our fifth version of our data lake, and our AI, and machine learning that we use in the products, so we often give them some of our best practices. I think from a cost in stepping in, you know, what we're here to provide is, you know, essentially, like, reference architecture, a system, the ability to take what they presented on stage and make that very easy for a customer to consume. The two personas that we like to talk to are essentially the AI container developer, right? And then whoever is the application and data architect, right? And they don't really want to care about infrastructure, really, at the end of the day. They want to be able to understand their data, the outcomes they're driving to, and get that intelligence, and so, it's our job to provide that in a very easy package solution, all accessible through APIs, and then we offer it in a GreenLake fashion, right? So if you don't want to lay out, you know, a large amount of CapEx for GPUs, for storage, for the fabric, for, you know, the software, we can go through it in a stepped consumption model for customers starting small, you know, scaling up into the mid, and then, you know, and then beyond, which is very popular.

>> I think that's... I mean. that's how they, you know, consume

in the cloud anyways. >> Yes.

>> And I think that's, when we talk about hybrid, and I talk about Antonio being very early on into hybrid and really leaning into it, it is about that consumption model, and how I get to that, and how I normalize it to be more of a, you know, ARR, I will get that right today, (Rebecca chuckling) model versus, you know, in a subscription model, so, how are you seeing that, and how's been the momentum, also, on the other side with the Altera, or it's...

>> Alletra, yep. >> Alletra. I don't know why I can't get that name right either,

but how's momentum? >> Yeah, it's been great. I mean, so, the model validates itself, so we're trying to bring the predictable to the unpredictable, really, and that's what happens a lot in storage, in general, right? Especially when in the context of AI is a form of workloads that are being deployed on some of these new application paradigms driven by Kubernetes, and you know, there's a number of different parts of it, but that's super unpredictable, right? And so, for us, storage as a service has been super popular, so we've grown that in essence, you know, 45% year-over-year, you know, for us, and so, the customers really like that model where they can provide a sort of a minimum commitment level, right? And then be able to burst, expand, depending on their needs, 'cause if, for example, as you say, they dip their toes in the water of AI, and all of a sudden, now, you've got a larger model you're dealing with, you know, a lot of results, machine language, and machine learning produce, you know, quite a bit of data, and so that unpredictable piece they can deal with, you know, sort of ad hoc during their journey, and so, for us, and then also providing all of the, I guess you'd say, unsexy infrastructure capabilities around that, which is how do I DR it, how do I back it up? How do I map consumption of the developers to how much storage they're using, right? I mean, these are classic problems that need to get solved for our customers.

>> Your colleague just called those the dirty jobs of AI.

>> Yes, exactly. >> So the ones no one

wants to do, but that really >> Yeah.

>> Need to get done. >> 100%, yeah. And it's often, too, because when we talk to our customers, and these environments become business and mission critical really fast, right?

>> Yeah. >> So they move from sandbox and testing into full-blown, you know, a part of their enterprise workflow, right? It's very, very important, so we need to support them as such and, you know, and give them the ability to scale.

>> Yeah, and I think even Antonio touched on it earlier today, it also becomes a component of corporate governance, because, you know, if you don't know where your data's coming from, how the models are built, and how all of this is, and where did you, how did you then train the models on what data?

And you can't prove that, >> Yeah.

>> Especially over here where, you know, PII, if you're using anything PII to go and train a model, you're quickly going to run afoul with GDPR and other things here, you know, locally. How are you seeing people, like, lean in towards you and say, hey, this is really, you know, this is the performance we need in these different places? Because to your point, you know, with being cloud-adjacent, or being more towards the edge, and what are some of the demands you're seeing in that more hybrid model?

>> Yeah, so a couple of things. I think one of the areas we see a lot of growth is definitely on the edge, right? And so, there's more applications being deployed in places outside of the traditional data center, colo cloud, right? And they're doing some really interesting things. We've met, we have a number of customers here talking today about, you know, retail, grocery stores, you know, all these places, you know, new fan experiences, all those use modern containerized applications. They use video for inference, and security, and buying patterns, and you know, in the grocery store, they do, you know, real-time, just-in-time, you know, inventory and analytics, and so, there's a lot of really complex things that are happening outside, so you've got that edge-to-core model, which is really interesting. And then, you know, from a hybrid perspective, I think one of the big things for us is being able to allow customers to place the data where they need it, depending on the workloads, right? And so, the workloads are definitely, in the case of AI coming to the data, right? And so, allowing our customers to choose whether they want that on-prem or, you know, up in the cloud is, you know, super important, so that's why we provide all of these data services, right, that are abstracted from the physical storage to be able to provide movement of data, placement of it, and so, that's been very successful for us.

>> Sustainability is a real theme at this conference.

>> Yes. >> And we heard Antonio talk about it, a lot of people on "TheCUBE" have talked about it. AI is an energy-greedy technology. How does your organization, HPE Storage approach this issue? And are you hearing this more and more from customers as a key concern as they are deploying AI?

>> Yeah, so especially here, in the EMEA theater, right? I get the luxury of being a exec sponsor for a number of customers, both in, you know, specific sectors like financial services, or, but definitely in the service provider market, like, very, very concerned with sustainability. It's usually on the first one to two pages of their quarterly or yearly prospectus, and so, we need to give them visibility into our infrastructure and how it's operating, right? So, power, cooling, and so, we have our own sustainability dashboard that we provide for all of our storage and data management products. We also have a ton of observability, right? We think about OpsRamp, and providing that level of observability that they can, you know, gain data out of that, and understand how to map application usage and data usage to the infrastructure. And then there's a lot of onus on us to make sure that our upstream suppliers, right, that we work with are providing sustainable products, not only in the way they're manufactured, how they're recycled, being in the Boston area, one of our big offices in that area for HPE does the circular economy, so, recycling of equipment, and so, now, we have customers that are asking us for a 7 to 10-year TCO on storage, right? And so, that means, you know, making sure that we come and sustainably upgrade equipment, right? Recycle that in a responsible manner. So it's all part of a, you know, a big ecosystem that HPE provides for our customers.

>> Yeah, I think that's... I think one of the big keys is how do you bring it all together? And I think one of the other things that people worry about, especially in AI, is data protection.

>> Yes. >> How are you addressing that from those workloads and being able to protect them?

>> Yeah, so a couple of the things that have really, that our customers like about GreenLake cloud platform, specifically the data services that we provide, so, it'll give you a view when you drop into the cloud console, 'cause really, at the end of the day, people think about GreenLake sometimes as a consumption model, but for me, on the product side, it's a product experience, right? So you go, this is our hybrid cloud console, so you start in at greenlake.hpe.com, and then you go drop right into your data services cloud console, right? And so, every workload that you provision, all of the storage, and you know, file shares, you know, any type of application provision that happens, you get to automatically build in all this templating of SLA. So, what's my RTO and RPO objective? How fast do I want to back up? Is this a platinum level app? Is this a, you know, silver level app? And so what we can do is when those are deployed, that gets automatically applied, and then we can view the adherence to that SLA, and it's all audited, so when you talk about, you know, provenance, and, you know, making sure you have a data signature, we can also audit and show customers that everything is being backed up, it has a, you know, it's recoverable in the case of, you know, a disaster, either, you know, you have a disruption on-prem, or you know, and god forbid, you know, you have a ransomware issue, so, all this is services that we provide in the platform that customers can set up. It's easy to use and it's super automated.

>> Excellent, well, Patrick, thank you so much for-

>> Well, I was going to say, >> Oh.

>> Next time, if we're in Spain again, we'll make you do it in Spanish, 'cause I know you used to live here as well, so... (Patrick laughing)

>> I did, I did, that'll be (Rob chuckling)

my next trick, right, yeah. >> Very cool, yes. We will absolutely do that,

yes. >> Yeah, awesome.

>> You can bet on it. Patrick, thank you so much for

coming on "TheCUBE". >> Thank you for having me.

>> Returning to "TheCUBE". I'm Rebecca Knight for Rob Strachay. Stay tuned for more of "TheCUBE's" live coverage of HPE Discover Barcelona. We'll be back right after this. You're watching "TheCUBE", the leader in high-tech technology enterprise coverage. (bright music)