 It's theCUBE, covering the Virtual Vertica Big Data Conference 2020 brought to you by Vertica. Hi everybody and welcome to this CUBE special presentation of the Vertica Virtual Big Data Conference. theCUBE is running in parallel with day one and day two of the Vertica Big Data Event. By the way theCUBE has been at every single big data event and it's our pleasure to be here in the virtual slash digital event as well. Gabriel Chapman is here, the director of FlashBlade Product Solutions Marketing at Pure Storage. Gabe, 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, you know, and hopefully we'll be able to meet and accelerate at some point in the future or one of the subshows 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 at the last accelerate, September 2019, Pure and Vertica announced a partnership, I remember Joy King ran up to me and said, hey, you got to check this out. The separation of compute and storage via EON mode now available on FlashBlade. So, and I believe still the only company that can support that separation and independent scaling both on-prem and in the cloud. So Gabe, I want to ask you, what were the trends in analytical database and cloud that led to this partnership? You know, realistically, I think what we're seeing is that there's been 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 and leveraging a lot of direct-to-cache 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 and storage pieces independently and therefore, you know, the FlashBlade platform ended up being a great solution to support Vertica in their transition to EON mode, leveraging it essentially as an S3 object store. Okay, so let's circle back on that. You guys in your announcement of FlashBlade, you make the claim that FlashBlade is the industry's most advanced file and object storage platform ever. That's a bold statement. To defend that, what makes it- It's a bold statement. I mean, I 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 we've developed FlashBlade 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 that FastFile and FastObject 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 FastObject and FastFile storage place. And realistically, when 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 that are, you know, fast matters for us. Simple is smart. 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 falls into our umbrella of what we consider the modern data experience. 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 FlashBlade, and then try to understand the fit for analytic databases generally, but specifically for Vertica. 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 pure sort of, you know, initial products, which were scale up. And so I want to, and as a fabric based system, I want to understand what that all means. So 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. I mean, we call it a FlashBlade because if you look at the actual platform, you have a primarily a chassis with built-in networking components, right? So there's a fabric interconnect with inside the platform that connects to each one of the individual blades. Have their own compute that drives basically a pure storage Flash components inside. It's not like we're just taking SSDs and plugging them into a system and like you would with the traditional commodity off the shelf, a hardware design. This is a very much an engineered solution that is built towards the characteristics that we believe were important with fast file and fast object. Scalability, you know, massive parallelization when it comes to performance and the ability to really kind of grow in scale from essentially seven blades right now to 150. That's the kind of scale that customers are looking for, especially as we start to address these larger analytics pools, you know, multi-pedabyte 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. Yeah, so I interviewed Kaws last September at Accelerate and of course, Pure has been, you know, attacked by some of the competitors is not having a scale out. I asked them his thoughts on that. He said, well, first of all, our FlashBlade is scale out. And he said, look, anything that adds to your complexity, you know, we avoid, but for the workloads that are associated with FlashBlade, scale out is the right sort of approach. Maybe you could talk about why that is. Well, you know, realistically, I think, you know, that that approach is better when we're starting to work with large unstructured data sets. I mean, FlashBlade is uniquely architected to allow customers to achieve, you know, a superior resource utilization for compute and storage while at the same time, you know, reducing significantly the complexity that has arisen around these kind of bespoke or siloed nature of big data and analytics solutions. I mean, we really kind of look at this from a standpoint of you have built and delivered or created applications in the public cloud space that address, you know, object storage and unstructured data. And for some organizations, the importance is bringing that on-prem. I mean, we do see cloud repatriation that coming on, for a lot of organizations as these data egress charges continue to expand and grow. And then organizations that want even higher performance than they were able to get into the public cloud space, they are bringing that data back on-prem. They are looking at from a standpoint 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, you know, our own borders. And so that's, you know, that's one of the bigger pieces of that is we start to look at how do we address cloud characteristics and dynamics and consumption metrics or models, as well as the benefits and efficiencies of scale that they're able to afford but allowing customers to do that with inside their own data center. Yeah, so you were talking about the trends earlier. You had these cloud native databases that allowed the scaling of compute and storage independently. Vertica comes in with Eon. A lot of times we talk about these partnerships as Barney deals, 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 are non-Barney deal like, in other words, any specific, you know, engineering, you know, other go-to-market programs? Could you talk about that a little bit? Yeah, it's more than just, you know, 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 firsts with Vertica that I think are really important. And if they think you look at the architecture and how we've brought this to market together, 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 Vertica, they're very high on, are very excited about the partnership because it opens up a new set of opportunities for their customers to leverage EON mode and, you know, get into some of the nuanced aspects of how they leverage the depot with inside each individual compute node and adjustments with inside there, reach additional performance gains for customers on-prem. And it's the same time for them that's still 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 as two companies? How do we do a joint selling motions? You know, how do we show up and, you know, do white papers and all of the traditional marketing aspects that we bring to the market? And then, you know, joint selling opportunities that exist where they are. And so, yeah, it's realistically, I think like any other organization that's going to market with a partner or an ISV that they have a strong partnership with, you'll continue to see us, you know, talking about our chose mutually beneficial relationships and the solutions that we're bringing to the market. Well, Dave, you know, of course, you used to be a Gartner analyst and you will go over to the vendor side now. But as a Gartner analyst, you're obviously objective, you see it all and you know well, there's a lot of ways to skin a cat. There are strengths, weaknesses, opportunities, threats, et cetera, for every vendor. So you have Vertica 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 make sense. It's not just the economics of scaling compute and storage independently. I want to talk more about that. There's flexibility aspects as well. But Vertica really has to play its trump card, which is look, we've got a big on-prem estate and we're going to bring that Eon capability both on-prem and we're embracing the cloud. Now they're obviously you have to, they had 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, you know, 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 what's the sweet spot for this partnership? You know, we've been really excited to build the partnership with Vertica and we're really proud to provide pretty much the only on-prem storage platform that's validated with the Vertica Eon mode to deliver a modern data experience for our customers together. You know, it's that partnership that allows us to go into customers, that on-prem space where I think that there's still, you know, not to say that not everybody wants to go to the cloud. I think there's aspects and solutions that work very well there, but for the vast majority, I still think that there's, you know, your data center is not going away and you do want to have control over some of the, many of the different facets with inside the operational confines. So therefore we start to look at how do we can do the best of what cloud offers, but on-prem. And that's realistically where we start to see the stronger push for those customers who still want to manage their data locally, as well as maybe even work around some of the restrictions that they might have around cost and complexity, hiring, you know, the different types of 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 forward with. And realistically, I think they're taking a look at the pros and cons. And we've been doing cloud long enough for people to 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, in realistic as we move forward, that's that better option when it comes to a modern architecture that can, you know, we can deliver and 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 FlashWid was built for. It was built for a platform that can address small files or large files or high throughput, low latency, scale to petabytes in a single name space and a single rack as we like to put it in there. I mean, we see customers that have put, you know, 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 Vertica have delivered an experience that can address that to a wide range of customers that are implementing, you know, the Vertica EON 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. And, but take us through kind of what the discuss, the customer discussions are like. Obviously you've got in a big customer base, you and Vertica that's on-prem. That's 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. So take us through that sort of a primary use case or use cases. Yeah, I mean, I can give you a couple of customer examples. And we have a large SaaS analyst company which uses Vertica on flash blade to authenticate the quality of digital media in real time. And for them, it makes a big difference is they're doing their streaming and whatnot that they can fine tune and granularly control that. So that's one aspect that we get to address. We have a multinational car company which uses Vertica on flash blade to make thousands of decisions per second for autonomous vehicle decision-making trees. That's what really these new modern analytics platforms were built for. There's another healthcare organization that uses Vertica on flash blade to enable healthcare providers to make decisions in real time that impact lives, especially when we start to look at the current state of affairs with COVID and the coronavirus. Those types of technologies are really going to help us kind of get above and help lower and bend that curve downward. So there's all these different areas where we can address the goals and the achievements that we're trying to look forward with real-time analytic decision-making tools like Vertica. And realistically as we have these conversations with customers, they're looking to get beyond the ability of just a data scientist or a data architect looking to just kind of derive an information. We were 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 spinning up compute and storage, a little bit of networking in there. Seems like a new workload that's clearly emerging is you've got, and it started with the cloud native databases but then bringing in AI and machine learning tooling on top of that and then being able to really drive these new types of insights. And it's really about taking data, these bogs, this bog of data that we've collected over the last 10 years. A lot of that, driven by Hadoop, bringing machine intelligence into the equation, scaling it with either cloud, public cloud or bringing that cloud experience on-prem scale across your organizations and across your partner network. That really is a new emerging workload. Do you see that and maybe talk a little bit about what you're seeing with customers? Yeah, I mean, really as we see several trends, one of those is the ability to take this approach to move it out of the lab but into production. 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 real, derived significant value from that, they do want to be able to control more characteristics like that. We know machine learning needs to learn from a massive amounts of data to provide accuracy. There's just too much data retrieve in the cloud for every training job at the same time, predictive analytics without accuracy is not going to deliver the business advantage of what everyone is seeking. We see this, the visualization of data analytics is traditionally deployed as being on a continuum with the things that we've been doing in the past, with data warehousing, data lakes, AI on the other end, but this way we're starting to manifest it in 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 up silos of bespoke AI environments, they're looking to leverage a platform that can allow them to do AI for one thing, machine learning for another, leverage multiple protocols to access that data because the tools are so much different. It is a growing diversity of use cases that you can put on a single platform. I think organizations are looking for as they try to scale these environments. I think there's going to be a big growth area in the coming years. Gabe, I wish we were in Boston together, you would have painted your little corner of Boston orange, I know that, but you guys operate, but really appreciate you coming on theCUBE. Wall-to-wall coverage, two days of the vertical, vertical, virtual big data conference. Keep it right there, but right back, right after this short break.