>>Yeah, it's the queue covering the virtual vertical Big Data Conference 2020. Brought to you by vertical. >>Hi, everybody. And welcome to this cube special presentation of the vertical virtual Big Data conference. The Cube is running in parallel with Day One and day two of the vertical of Big Data event. By the way, the Cube has been every single big data event in It's our pleasure to be here in the virtual slash digital event as well. Gabriel Chapman is here. He's the director of Flash Blade Products Solutions Marketing at Pure Storage. Great to see you. Thanks for coming on. >>Great to see you too. How's it going? >>It's going very well. I mean, I wish we were meeting in Boston at the Encore Hotel, but, uh, you know, and hopefully we'll be able to meet it, accelerate at some point, future or one of the sub shows that you guys are doing the regional shows, but because we've been covering that show as well. But I really want to get into it. And the last accelerate September 2019 pure and vertical announced. Ah, partnership. I remember a joint being ran up to me and said, Hey, you got to check this out. The separation of compute and storage by EON mode now available on Flash Blade. So, uh and and I believe still the only company that can support that separation and independent scaling both on Prem and in the cloud. So I want to ask, what were the trends and analytical database and cloud led to this partnership? You know, >>realistically, I think what we're seeing is that there's been a kind of a larger shift when it comes to modern analytics platforms towards moving away from the traditional, you know, Hadoop type architecture where we were doing on and leveraging a lot of directors that storage primarily because of the limitations of how that solution was architected. When we start to look at the larger trends towards you know how organizations want to do this type of work on premises, they're looking at solutions that allow them to scale the compute storage pieces independently and therefore, you know, the flash blade platform ended up being a great solution to support America in their transition Tian mode. Leveraging essentially is an S three object store. >>Okay, so let's let's circle back on that you guys in your in your announcement of the flash blade, you make the claim that Flash Blade is the industry's most advanced file and object storage platform ever. That's a bold statement. So defend that What? >>I would like to go beyond that and just say, you know, So we've really kind of looked at this from a standpoint of, you know, as as we've developed Flash Blade as a platform and keep in mind, it's been a product that's been around for over three years now and has been very successful for pure storage. The reality is, is that fast file and fast object as a combined storage platform is a direction that many organizations are looking to go, and we believe that we're a leader in that fast object best file storage place in realistically, which we start to see more organizations start to look at building solutions that leverage cloud storage characteristics. But doing so on Prem for a multitude of different reasons. We've built a platform that really addresses a lot of those needs around simplicity around, you know, making things this year that you know, fast matters for us. Ah, simple is smart. Um we can provide, you know, cloud integrations across the spectrum. And, you know, there's a subscription model that fits into that as well. We fall that that falls into our umbrella of what we consider the modern day takes variance. And it's something that we've built into the entire pure portfolio. >>Okay, so I want to get into the architecture a little bit of flash blade and then understand the fit for, uh, analytic databases generally, but specifically for vertical. So it is a blade, so you got compute and network included. It's a key value store based system. So you're talking about scale out. Unlike, unlike, uh, pure is sort of, you know, initial products which were scale up, Um, and so I want on It is a fabric based system. I want to understand what that all means to take us through the architecture. You know, some of the quote unquote firsts that you guys talk about. So let's start with sort of the blade >>aspect. Yeah, the blade aspect of what we call the flash blade. Because if you look at the actual platform, you have, ah, primarily a chassis with built in networking components, right? So there's ah, fabric interconnect with inside the platform that connects to each one of the individual blades. Individual blades have their own compute that drives basically a pure storage flash components inside. It's not like we're just taking SSD is and plugging them into a system and like you would with the traditional commodity off the shelf hardware design. This is very much an engineered solution that is built towards the characteristics that we believe were important with fast filing past object scalability, massive parallel ization. When it comes to performance and the ability to really kind of grow and scale from essentially seven blades right now to 150 that's that's the kind of scale that customers are looking for, especially as we start to address these larger analytics pools. They are multi petabytes data sets, you know that single addressable object space and, you know, file performance that is beyond what most of your traditional scale up storage platforms are able to deliver. >>Yes, I interviewed cause last September and accelerate, and Christie Pure has been attacked by some of the competitors. There's not having scale out. I asked him his thoughts on that, he said Well, first of all, our flash blade is scale out. He said, Look, anything that adds complexity, you know we avoid. But for the workloads that are associated with flash blade scale out is the right sort of approach. Maybe you could talk about why that is. Well, >>realistically, I think you know that that approach is better when we're starting to work with large, unstructured data sets. I mean, flash blade is unique. The architected to allow customers to achieve superior resource utilization for compute and storage, while at the same time, you know, reducing significantly the complexity that has arisen around this kind of bespoke or siloed nature of big data and analytics solutions. I mean, we're really kind of look at this from a standpoint of you have built and delivered are created applications in the public cloud space of dress, you know, object storage and an unstructured data. And for some organizations, the importance is bringing that on Prem. I mean, we do see about repatriation coming on a lot of organizations as these data egress, charges continue to expand and grow, um, and then organizations that want even higher performance and what we're able to get into the public cloud space. They are bringing that data back on Prem They are looking at from a stamp. We still want to be able to scale the way we scale in the cloud. We still want to operate the same way we operate in the cloud, but we want to do it within control of our own, our own borders. And so that's, you know, that's one of the bigger pieces to that. And we start to look at how do we address cloud characteristics and dynamics and consumption metrics or models? A zealous the benefits and efficiencies of scale that they're able to afford but allowing customers to do that with inside their own data center. >>So you're talking about the trends earlier. You have these cloud native databases that allowed of the scaling of compute and storage independently. Vertical comes in with eon of a lot of times we talk about these these partnerships as Barney deals of you know I love you, You love me. Here's a press release and then we go on or they're just straight, you know, go to market. Are there other aspects of this partnership that they're non Barney deal like, in other words, any specific engineering. Um, you know other go to market programs? Could you talk about that a little bit? Yeah, >>it's it's It's more than just that what we consider a channel meet in the middle or, you know, that Barney type of deal. It's realistically, you know, we've done some first with Veronica that I think, really Courtney, if they think you look at the architecture and how we did, we've brought to market together. Ah, we have solutions. Teams in the back end who are, you know, subject matter experts. In this space, if you talk to joy and the people from vertical, they're very high on our very excited about the partnership because it often it opens up a new set of opportunities for their customers to leverage on mode and get into some of the the nuance task specs of how they leverage the depot depot with inside each individual. Compute node in adjustments with inside their reach. Additional performance gains for customers on Prem and at the same time, for them, that's still tough. The ability to go into that cloud model if they wish to. And so I think a lot of it is around. How do we partner is to companies? How do we do a joint selling motions? How do we show up in and do white papers and all of the traditional marketing aspects that we bring to the market? And then, you know, joint selling opportunities exist where they are, and so that's realistically. I think, like any other organization that's going to market with a partner on MSP that they have, ah, strong partnership with. You'll continue to see us, you know, talking about are those mutually beneficial relationships and the solutions that we're bringing to the market. >>Okay, you know, of course, he used to be a Gartner analyst, and you go to the vendor side now, but it's but it's, but it's a Gartner analyst. You're obviously objective. You see it on, you know well, there's a lot of ways to skin the cat There, there their strengths, weaknesses, opportunities, threats, etcetera for every vendor. So you have you have vertical who's got a very mature stack and talking to a number of the customers out there who are using EON mode. You know there's certain workloads where these cloud native databases makes sense. It's not just the economics of scaling and storage independently. I want to talk more about that. There's flexibility aspect as well. But Vertical really has to play its its trump card, which is Look, we've got a big on premise state, and we're gonna bring that eon capability both on Prem and we're embracing the cloud now. There obviously have been there to play catch up in the cloud, but at the same time, they've got a much more mature stack than a lot of these other cloud native databases that might have just started a couple of years ago. So you know, so there's trade offs that customers have to make. How do you sort through that? Where do you see the interest in this? And and what's the sweet spot for this partnership? You know, we've >>been really excited to build the partnership with vertical A and provide, you know, we're really proud to provide pretty much the only on Prem storage platform that's validated with the yang mode to deliver a modern data experience for our customers together. You know, it's ah, it's that partnership that allows us to go into customers that on Prem space, where I think that there's still not to say that not everybody wants to go there, but I think there's aspects and solutions that worked very well there. But for the vast majority, I still think that there's, you know, the your data center is not going away. And you do want to have control over some of the many of the assets with inside of the operational confines. So therefore, we start to look at how do we can do the best of what cloud offers but on prim. And that's realistically, where we start to see the stronger push for those customers. You still want to manage their data locally. A swell as maybe even worked around some of the restrictions that they might have around cost and complexity hiring. You know, the different types of skills skill sets that are required to bring applications purely cloud native. It's still that larger part of that digital transformation that many organizations are going for going forward with. And realistically, I think they're taking a look at the pros and cons, and we've been doing cloud long enough where people recognize that you know it's not perfect for everything and that there's certain things that we still want to keep inside our own data center. So I mean, realistically, as we move forward, that's, Ah, that better option when it comes to a modern architecture that can do, you know, we can deliver an address, a diverse set of performance requirements and allow the organization to continue to grow the model to the data, you know, based on the data that they're actually trying to leverage. And that's really what Flash was built for. It was built for a platform that could address small files or large files or high throughput, high throughput, low latency scale of petabytes in a single name. Space in a single rack is we like to put it in there. I mean, we see customers that have put 150 flash blades into production as a single name space. It's significant for organizations that are making that drive towards modern data experience with modern analytics platforms. Pure and Veronica have delivered an experience that can address that to a wide range of customers that are implementing uh, you know, particularly on technology. >>I'm interested in exploring the use case. A little bit further. You just sort of gave some parameters and some examples and some of the flexibility that you have, um, and take us through kind of what the customer discussions are like. Obviously you've got a big customer base, you and vertical that that's on Prem. That's the the unique advantage of this. But there are others. It's not just the economics of the granular scaling of compute and storage independently. There are other aspects of take us through that sort of a primary use case or use cases. Yeah, you >>know, I mean, I could give you a couple customer examples, and we have a large SAS analyst company which uses vertical on last way to authenticate the quality of digital media in real time, You know, then for them it makes a big difference is they're doing their streaming and whatnot that they can. They can fine tune the grand we control that. So that's one aspect that that we address. We have a multinational car car company, which uses vertical on flash blade to make thousands of decisions per second for autonomous vehicle decision making trees. You know, that's what really these new modern analytics platforms were built for, um, there's another healthcare organization that uses vertical on flash blade to enable healthcare providers to make decisions in real time. The impact lives, especially when we start to look at and, you know, the current state of affairs with code in the Corona virus. You know, those types of technologies, we're really going to help us kind of get of and help lower invent, bend that curve downward. So, you know, there's all these different areas where we can address that the goals and the achievements that we're trying to look bored with with real time analytics decision making tools like and you know, realistically is we have these conversations with customers they're looking to get beyond the ability of just, you know, a data scientist or a data architect looking to just kind of driving information >>that we're talking about Hadoop earlier. We're kind of going well beyond that now. And I guess what I'm saying is that in the first phase of cloud, it was all about infrastructure. It was about, you know, uh, spin it up. You know, compute and storage is a little bit of networking in there. >>It >>seems like the next new workload that's clearly emerging is you've got. And it started with the cloud native databases. But then bringing in, you know, AI and machine learning tooling on top of that Ah, and then being able to really drive these new types of insights and it's really about taking data these bog this bog of data that we've collected over the last 10 years. A lot of that is driven by a dupe bringing machine intelligence into the equation, scaling it with either cloud public cloud or bringing that cloud experience on Prem scale. You know, across organizations and across your partner network, that really is a new emerging workloads. You see that? And maybe talk a little bit about what you're seeing with customers. >>Yeah. I mean, it really is. We see several trends. You know, one of those is the ability to take a take this approach to move it out of the lab, but into production. Um, you know, especially when it comes to data science projects, machine learning projects that traditionally start out as kind of small proofs of concept, easy to spin up in the cloud. But when a customer wants to scale and move towards a riel you know, derived a significant value from that. They do want to be able to control more characteristic site, and we know machine learning, you know, needs toe needs to learn from a massive amounts of data to provide accuracy. There's just too much data retrieving the cloud for every training job. Same time Predictive analytics without accuracy is not going to deliver the business advantage of what everyone is seeking. You know, we see this. Ah, the visualization of Data Analytics is Tricia deployed is being on a continuum with, you know, the things that we've been doing in the long in the past with data warehousing, data Lakes, ai on the other end. But this way, we're starting to manifest it and organizations that are looking towards getting more utility and better elasticity out of the data that they are working for. So they're not looking to just build apps, silos of bespoke ai environments. They're looking to leverage. Ah, you know, ah, platform that can allow them to, you know, do ai, for one thing, machine learning for another leverage multiple protocols to access that data because the tools are so much Jeff um, you know, it is a growing diversity of of use cases that you can put on a single platform I think organizations are looking for as they try to scale these environment. >>I think it's gonna be a big growth area in the coming years. Gable. I wish we were in Boston together. You would have painted your little corner of Boston orange. I know that you guys have but really appreciate you coming on the cube wall to wall coverage. Two days of the vertical vertical virtual big data conference. Keep it right there. Right back. Right after this short break, Yeah.