>>Yeah, it's the queue covering the virtual vertical Big Data Conference 2020 Brought to You by vertical. >>Welcome back, everybody. My name is Dave Vellante, and you're watching the Cube's coverage of the verdict of Virtual Big Data conference. The Cube has been at every BTC, and it's our pleasure in these difficult times to be covering BBC as a virtual event. This digital program really excited to have Joy King joining us. Joy is the vice president of product and go to market strategy in particular. And if that weren't enough, he also runs marketing and education curve for him. So, Joe, you're a multi tool players. You've got the technical side and the marketing gene, So welcome to the Cube. You're always a great guest. Love to have you on. >>Thank you so much, David. The pleasure, it really is. >>So I want to get in. You know, we'll have some time. We've been talking about the conference and the virtual event, but I really want to dig in to the product stuff. It's a big day for you guys. You announced 10.0. But before we get into the announcements, step back a little bit you know, you guys are riding the waves. I've said to ah, number of our guests that that brick has always been good. It riding the wave not only the initial MPP, but you you embraced, embraced HD fs. You embrace data science and analytics and in the cloud. So one of the trends that you see the big waves that you're writing >>Well, you're absolutely right, Dave. I mean, what what I think is most interesting and important is because verdict is, at its core a true engineering culture founded by, well, a pretty famous guy, right, Dr Stone Breaker, who embedded that very technical vertical engineering culture. It means that we don't pretend to know everything that's coming, but we are committed to embracing the tech. An ology trends, the innovations, things like that. We don't pretend to know it all. We just do it all. So right now, I think I see three big imminent trends that we are addressing. And matters had we have been for a while, but that are particularly relevant right now. The first is a combination of, I guess, a disappointment in what Hadoop was able to deliver. I always feel a little guilty because she's a very reasonably capable elephant. She was designed to be HD fs highly distributed file store, but she cant be an entire zoo, so there's a lot of disappointment in the market, but a lot of data. In HD FM, you combine that with some of the well, not some the explosion of cloud object storage. You're talking about even more data, but even more data silos. So data growth and and data silos is Trend one. Then what I would say Trend, too, is the cloud Reality Cloud brings so many events. There are so many opportunities that public cloud computing delivers. But I think we've learned enough now to know that there's also some reality. The cloud providers themselves. Dave. Don't talk about it well, because not, is it more agile? Can you do things without having to manage your own data center? Of course you can. That the reality is it's a little more pricey than we expected. There are some security and privacy concerns. There's some workloads that can go to the cloud, so hybrid and also multi cloud deployments are the next trend that are mandatory. And then maybe the one that is the most exciting in terms of changing the world we could use. A little change right now is operationalize in machine learning. There's so much potential in the technology, but it's somehow has been stuck for the most part in science projects and data science lab, and the time is now to operationalize it. Those are the three big trends that vertical is focusing on right now. >>That's great. I wonder if I could ask you a couple questions about that. I mean, I like you have a soft spot in my heart for the and the thing about the Hadoop that that was, I think, profound was it got people thinking about, you know, bringing compute to the data and leaving data in place, and it really got people thinking about data driven cultures. It didn't solve all the problems, but it collected a lot of data that we can now take your third trend and apply machine intelligence on top of that data. And then the cloud is really the ability to scale, and it gives you that agility and that it's not really that cloud experience. It's not not just the cloud itself, it's bringing the cloud experience to wherever the data lives. And I think that's what I'm hearing from you. Those are the three big super powers of innovation today. >>That's exactly right. So, you know, I have to say I think we all know that Data Analytics machine learning none of that delivers real value unless the volume of data is there to be able to truly predict and influence the future. So the last 7 to 10 years has been correctly about collecting the data, getting the data into a common location, and H DFS was well designed for that. But we live in a capitalist world, and some companies stepped in and tried to make HD Fs and the broader Hadoop ecosystem be the single solution to big data. It's not true. So now that the key is, how do we take advantage of all of that data? And now that's exactly what verdict is focusing on. So as you know, we began our journey with vertical back in the day in 2007 with our first release, and we saw the growth of the dupe. So we announced many years ago verdict a sequel on that. The idea to be able to deploy vertical on Hadoop nodes and query the data in Hadoop. We wanted to help. Now with Verdict A 10. We are also introducing vertical in eon mode, and we can talk more about that. But Verdict and Ian Mode for HDs, This is a way to apply it and see sequel database management platform to H DFS infrastructure and data in each DFS file storage. And that is a great way to leverage the investment that so many companies have made in HD Fs. And I think it's fair to the elephant to treat >>her well. Okay, let's get into the hard news and auto. Um, she's got, but you got a mature stack, but one of the highlights of append auto. And then we can drill into some of the technologies >>Absolutely so in well in 2018 vertical announced vertical in Deon mode is the separation of compute from storage. Now this is a great example of vertical embracing innovation. Vertical was designed for on premises, data centers and bare metal servers, tightly coupled storage de l three eighties from Hewlett Packard Enterprises, Dell, etcetera. But we saw that cloud computing was changing fundamentally data center architectures, and it made sense to separate compute from storage. So you add compute when you need compute. You add storage when you need storage. That's exactly what the cloud's introduced, but it was only available on the club. So first thing we did was architect vertical and EON mode, which is not a new product. Eight. This is really important. It's a deployment option. And in 2018 our customers had the opportunity to deploy their vertical licenses in EON mode on AWS in September of 2019. We then broke an important record. We brought cloud architecture down to earth and we announced vertical in eon mode so vertical with communal or shared storage, leveraging pure storage flash blade that gave us all the advantages of separating compute from storage. All of the workload, isolation, the scale up scale down the ability to manage clusters. And we did that with on Premise Data Center. And now, with vertical 10 we are announcing verdict in eon mode on HD fs and vertically on mode on Google Cloud. So what we've got here, in summary, is vertical Andy on mode, multi cloud and multiple on premise data that storage, and that gives us the opportunity to help our customers both with the hybrid and multi cloud strategies they have and unifying their data silos. But America 10 goes farther. >>Well, let me stop you there, because I just wanna I want to mention So we talked to Joe Gonzalez and past Mutual, who essentially, he was brought in. And one of this task was the lead into eon mode. Why? Because I'm asking. You still had three separate data silos and they wanted to bring those together. They're investing heavily in technology. Joe is an expert, though that really put data at their core and beyond Mode was a key part of that because they're using S three and s o. So that was Ah, very important step for those guys carry on. What else do we need to know about? >>So one of the reasons, for example, that Mass Mutual is so excited about John Mode is because of the operational advantages. You think about exactly what Joe told you about multiple clusters serving must multiple use cases and maybe multiple divisions. And look, let's be clear. Marketing doesn't always get along with finance and finance doesn't necessarily get along with up, and I t is often caught the middle. Erica and Dion mode allows workload, isolation, meaning allocating the compute resource is that different use cases need without allowing them to interfere with other use cases and allowing everybody to access the data. So it's a great way to bring the corporate world together but still protect them from each other. And that's one of the things that Mass Mutual is going to benefit from, as well, so many of >>our other customers I also want to mention. So when I saw you, ah, last last year at the Pure Storage Accelerate conference just today we are the only company that separates you from storage that that runs on Prem and in the cloud. And I was like I had to think about it. I've researched. I still can't find anybody anybody else who doesn't know. I want to mention you beat actually a number of the cloud players with that capability. So good job and I think is a differentiator, assuming that you're giving me that cloud experience and the licensing and the pricing capability. So I want to talk about that a little >>bit. Well, you're absolutely right. So let's be clear. There is no question that the public cloud public clouds introduced the separation of compute storage and these advantages that they do not have the ability or the interest to replicate that on premise for vertical. We were born to be software only. We make no money on underlying infrastructure. We don't charge as a package for the hardware underneath, so we are totally motivated to be independent of that and also to continuously optimize the software to be as efficient as possible. And we do the exact same thing to your question about life. Cloud providers charge for note indignance. That's how they charge for their underlying infrastructure. Well, in some cases, if you're being, if you're talking about a use case where you have a whole lot of data, but you don't necessarily have a lot of compute for that workload, it may make sense to pay her note. Then it's unlimited data. But what if you have a huge compute need on a relatively small data set that's not so good? Vertical offers per node and four terabyte for our customers, depending on their use case, we also offer perpetual licenses for customers who want capital. But we also offer subscription for companies that they Nope, I have to have opt in. And while this can certainly cause some complexity for our field organization, we know that it's all about choice, that everybody in today's world wants it personalized just for me. And that's exactly what we're doing with our pricing in life. >>So just to clarify, you're saying I can pay by the drink if I want to. You're not going to force me necessarily into a term or Aiken choose to have, you know, more predictable pricing. Is that, Is that correct? >>Well, so it's partially correct. The first verdict, a subscription licensing is a fixed amount for the period of the subscription. We do that so many of our customers cannot, and I'm one of them, by the way, cannot tell finance what the budgets forecast is going to be for the quarter after I spent you say what it's gonna be before, So our subscription facing is a fixed amount for a period of time. However, we do respect the fact that some companies do want usage based pricing. So on AWS, you can use verdict up by the hour and you pay by the hour. We are about to launch the very same thing on Google Cloud. So for us, it's about what do you need? And we make it happen natively directly with us or through AWS and Google Cloud. >>So I want to send so the the fixed isn't some floor. And then if you want a surge above that, you can allow usage pricing. If you're on the cloud, correct. >>Well, you actually license your cluster vertical by the hour on AWS and you run your cluster there. Or you can buy a license from vertical or a fixed capacity or a fixed number of nodes and deploy it on the cloud. And then, if you want to add more nodes or add more capacity, you can. It's not usage based for the license that you bring to the cloud. But if you purchase through the cloud provider, it is usage. >>Yeah, okay. And you guys are in the marketplace. Is that right? So, again, if I want up X, I can do that. I can choose to do that. >>That's awesome. Next usage through the AWS marketplace or yeah, directly from vertical >>because every small business who then goes to a salesforce management system knows this. Okay, great. I can pay by the month. Well, yeah, Well, not really. Here's our three year term in it, right? And it's very frustrating. >>Well, and even in the public cloud you can pay for by the hour by the minute or whatever, but it becomes pretty obvious that you're better off if you have reserved instance types or committed amounts in that by vertical offers subscription. That says, Hey, you want to have 100 terabytes for the next year? Here's what it will cost you. We do interval billing. You want to do monthly orderly bi annual will do that. But we won't charge you for usage that you didn't even know you were using until after you get the bill. And frankly, that's something my finance team does not like. >>Yeah, I think you know, I know this is kind of a wonky discussion, but so many people gloss over the licensing and the pricing, and I think my take away here is Optionality. You know, pricing your way of That's great. Thank you for that clarification. Okay, so you got Google Cloud? I want to talk about storage. Optionality. If I found him up, I got history. I got I'm presuming Google now of you you're pure >>is an s three compatible storage yet So your story >>Google object store >>like Google object store Amazon s three object store HD fs pure storage flash blade, which is an object store on prim. And we are continuing on this theft because ultimately we know that our customers need the option of having next generation data center architecture, which is sort of shared or communal storage. So all the data is in one place. Workloads can be managed independently on that data, and that's exactly what we're doing. But what we already have in two public clouds and to on premise deployment options today. And as you said, I did challenge you back when we saw each other at the conference. Today, vertical is the only analytic data warehouse platform that offers that option on premise and in multiple public clouds. >>Okay, let's talk about the ah, go back through the innovation cocktail. I'll call it So it's It's the data applying machine intelligence to that data. And we've talked about scaling at Cloud and some of the other advantages of Let's Talk About the Machine Intelligence, the machine learning piece of it. What's your story there? Give us any updates on your embracing of tooling and and the like. >>Well, quite a few years ago, we began building some in database native in database machine learning algorithms into vertical, and the reason we did that was we knew that the architecture of MPP Columbia execution would dramatically improve performance. We also knew that a lot of people speak sequel, but at the time, not so many people spoke R or even Python. And so what if we could give act us to machine learning in the database via sequel and deliver that kind of performance? So that's the journey we started out. And then we realized that actually, machine learning is a lot more as everybody knows and just algorithms. So we then built in the full end to end machine learning functions from data preparation to model training, model scoring and evaluation all the way through to fold the point and all of this again sequel accessible. You speak sequel. You speak to the data and the other advantage of this approach was we realized that accuracy was compromised if you down sample. If you moved a portion of the data from a database to a specialty machine learning platform, you you were challenged by accuracy and also what the industry is calling replica ability. And that means if a model makes a decision like, let's say, credit scoring and that decision isn't anyway challenged, well, you have to be able to replicate it to prove that you made the decision correctly. And there was a bit of, ah, you know, blow up in the media not too long ago about a credit scoring decision that appeared to be gender bias. But unfortunately, because the model could not be replicated, there was no way to this Prove that, and that was not a good thing. So all of this is built in a vertical, and with vertical 10. We've taken the next step, just like with with Hadoop. We know that innovation happens within vertical, but also outside of vertical. We saw that data scientists really love their preferred language. Like python, they love their tools and platforms like tensor flow with vertical 10. We now integrate even more with python, which we have for a while, but we also integrate with tensorflow integration and PM ML. What does that mean? It means that if you build and train a model external to vertical, using the machine learning platform that you like, you can import that model into a vertical and run it on the full end to end process. But run it on all the data. No more accuracy challenges MPP Kilometer execution. So it's blazing fast. And if somebody wants to know why a model made a decision, you can replicate that model, and you can explain why those are very powerful. And it's also another cultural unification. Dave. It unifies the business analyst community who speak sequel with the data scientist community who love their tools like Tensorflow and Python. >>Well, I think joy. That's important because so much of machine intelligence and ai there's a black box problem. You can't replicate the model. Then you do run into a potential gender bias. In the example that you're talking about there in their many you know, let's say an individual is very wealthy. He goes for a mortgage and his wife goes for some credit she gets rejected. He gets accepted this to say it's the same household, but the bias in the model that may be gender bias that could be race bias. And so being able to replicate that in and open up and make the the machine intelligence transparent is very, very important, >>It really is. And that replica ability as well as accuracy. It's critical because if you're down sampling and you're running models on different sets of data, things can get confusing. And yet you don't really have a choice. Because if you're talking about petabytes of data and you need to export that data to a machine learning platform and then try to put it back and get the next at the next day, you're looking at way too much time doing it in the database or training the model and then importing it into the database for production. That's what vertical allows, and our customers are. So it right they reopens. Of course, you know, they are the ones that are sort of the Trailblazers they've always been, and ah, this is the next step. In blazing the ML >>thrill joint customers want analytics. They want functional analytics full function. Analytics. What are they pushing you for now? What are you delivering? What's your thought on that? >>Well, I would say the number one thing that our customers are demanding right now is deployment. Flexibility. What? What the what the CEO or the CFO mandated six months ago? Now shout Whatever that thou shalt is is different. And they would, I tell them is it is impossible. No, what you're going to be commanded to do or what options you might have in the future. The key is not having to choose, and they are very, very committed to that. We have a large telco customer who is multi cloud as their commit. Why multi cloud? Well, because they see innovation available in different public clouds. They want to take advantage of all of them. They also, admittedly, the that there's the risk of lock it right. Like any vendor, they don't want that either, so they want multi cloud. We have other customers who say we have some workloads that make sense for the cloud and some that we absolutely cannot in the cloud. But we want a unified analytics strategy, so they are adamant in focusing on deployment flexibility. That's what I'd say is 1st 2nd I would say that the interest in operationalize in machine learning but not necessarily forcing the analytics team to hammer the data science team about which tools or the best tools. That's the probably number two. And then I'd say Number three. And it's because when you look at companies like Uber or the Trade Desk or A T and T or Cerner performance at scale, when they say milliseconds, they think that flow. When they say petabytes, they're like, Yeah, that was yesterday. So performance at scale good enough for vertical is never good enough. And it's why we're constantly building at the core the next generation execution engine, database designer, optimization engine, all that stuff >>I wanna also ask you. When I first started following vertical, we covered the cube covering the BBC. One of things I noticed was in talking to customers and people in the community is that you have a community edition, uh, free addition, and it's not neutered ais that have you maintain that that ethos, you know, through the transitions into into micro focus. And can you talk about that a little bit >>absolutely vertical community edition is vertical. It's all of the verdict of functionality geospatial time series, pattern matching, machine learning, all of the verdict, vertical neon mode, vertical and enterprise mode. All vertical is the community edition. The only limitation is one terabyte of data and three notes, and it's free now. If you want commercial support, where you can file a support ticket and and things like that, you do have to buy the life. But it's free, and we people say, Well, free for how long? Like our field? I've asked that and I say forever and what he said, What do you mean forever? Because we want people to use vertical for use cases that are small. They want to learn that they want to try, and we see no reason to limit that. And what we look for is when they're ready to grow when they need the next set of data that goes beyond a terabyte or they need more compute than three notes, then we're here for them, and it also brings up an important thing that I should remind you or tell you about Davis. You haven't heard it, and that's about the Vertical Academy Academy that vertical dot com well, what is that? That is, well, self paced on demand as well as vertical essential certification. Training and certification means you have seven days with your hands on a vertical cluster hosted in the cloud to go through all the certification. And guess what? All of that is free. Why why would you give it for free? Because for us empowering the market, giving the market the expert East, the learning they need to take advantage of vertical, just like with Community Edition is fundamental to our mission because we see the advantage that vertical can bring. And we want to make it possible for every company all around the world that take advantage >>of it. I love that ethos of vertical. I mean, obviously great product. But it's not just the product. It's the business practices and really progressive progressive pricing and embracing of all these trends and not running away from the waves but really leaning in joy. Thanks so much. Great interview really appreciate it. And, ah, I wished we could have been faced face in Boston, but I think it's prudent thing to do, >>I promise you, Dave we will, because the verdict of BTC and 2021 is already booked. So I will see you there. >>Haas enjoyed King. Thanks so much for coming on the Cube. And thank you for watching. Remember, the Cube is running this program in conjunction with the virtual vertical BDC goto vertical dot com slash BBC 2020 for all the coverage and keep it right there. This is Dave Vellante with the Cube. We'll be right back. >>Yeah, >>yeah, yeah.

(jazzy music) >> From our studios in the heart of Silicon Valley, Palo Alto, California, this is a CUBE conversation. >> Hello and welcome to theCUBE studios in Palo Alto, California for another CUBE conversation, where we go in-depth with thought leaders driving innovation across the tech industry. I'm your host, Peter Burris. Digital business is forcing companies to rethink what data means to them, and that means we have to rethink how we're going to manage, use, and take care of our data. A lot of companies are still thinking that we can use old data practices to solve new data requirements, and that disconnect is causing tension in a lot of businesses. So how do they overcome that gap? How do they modernize their data practices and approaches to ensure that they have the options and the flexibility and the capabilities they need as they drive their businesses forward? To have that conversation, we're joined by Matt Kixmoeller, who's the vice president of strategy at Pure Storage. Matt, welcome back to theCUBE. >> Thanks so much. Glad to be here. >> All right, let's start with the obvious. Give us a quick update on Pure. >> Oh, it's a super fun time at Pure right now. We just rounded our 10th birthday, so a lot of celebration going around at the company, and we're just back from our Accelerate conference, where we launched some new products and had quite a good time in Austin. >> Well, tell us a little bit about what was the big story from the Accelerate conference in Austin? >> Well a couple big things. First off, we announced the GA of our cloud block store product. You know, this is where we really take the core Pure value proposition and bring it to run natively on the public cloud. We GAed on the AWS platform, and we actually also just announced a tech preview on Azure. So that was a big part of it. You know, that product's all about helping customers take their tier one applications and transparently move them to the cloud. >> So I mentioned upfront this notion of an impedance mismatch, a disconnect between the requirements or the drive to use data differently, and that's a major feature in digital business transformation. And traditional practices of how data storage and management is conducted, as you talk to customers, how is that challenge manifesting itself in practical as well as strategic ways? >> Yeah, I mean, if you look at our average customer at Pure, they're in the journey of understanding digital transformation, and it's obvious to say, perhaps, but data's at the core of that. And so let's look at, you know, we do a lot of work, for example, in the audio industry. And you might think, okay, the auto industry, kind of a traditional space. They've been around forever manufacturing big, expensive things. But if you look at a modern car company, number one, they're a software developer. There's an amazing amount of software inside cars. And this is similar with everybody that's in digital business. They're now having to build their own software, get it to market quickly. That's a key part of their differentiation. Number two, they're increasingly IoT companies, and so they're having to learn how to harvest all this data that's coming off of their cars, bring it back to the core, analyze it, use it in real-time, and use it in much post-real time to design the next car and get smarter about how they do their work. And then number three, they're operating huge technology environments to run these platforms, and so they need to drive down their cost of data, their cost of goods, if you will, to be able to operate successfully and have an edge and be able to develop more. >> So build software faster, manage storage more efficiently, and move more rapidly and quickly. >> Absolutely. And then mine all that for insights and do more with that data to build the next product every year, every cycle. >> So what is it about the old practices that don't lend themselves to being able to be more efficient, faster, and more productive in how they deliver systems? >> Well, the problem with storage today, if you look at storage just as a layer within the data center, it's probably the least cloud-like of any part of IT. You know, the cloud model, I don't mean cloud the destination, I mean the operating model, has really been taken well to the virtualization and servers and networking layer, but storage, you still have a land of lots of bespoke infrastructure, dedicated silos for each chunk of data, and a lot of manual management. And so when we talk to our typical customer, they're not doing exciting things with data. They're in the drudgery of running the factory of data down there, spending all their time just keeping it working, and they're horribly inefficient in terms of infrastructure they have to use, because it's so bespoke. You know, the term snowflakes is often used in the cloud world. We've just got a million snowflakes in storage. >> So I've always thought that, well, it's not just what I think, but there's a general recognition that every business organizes itself, institutionalizes its work, establishes value propositions around what it thinks are its core differentiating assets. A digital business, increasingly that's data. But I think what you just said sounds like that in the storage world, the assets remain the devices. They remain the LUNs. They remain the physical things. They remain the administrative practices. And we have to find a way to make more of that go away so we can focus more on the data that's being delivered out of the storage. Have I got that right? >> Absolutely. I mean, I think it's just putting data at the core of the strategy and having people actually build an architecture around it. Today what we see is a lot of people build their data strategy piecemeal by project, not having an opportunity to step back and just really think about it from the core. And, you know, at Pure, one of the things we talked about at Accelerate was our vision that we call the modern data experience, and this is just really rethinking the entire experience of storage, hitting the reset button, and trying to bring the lessons of the cloud to how you manage data in enterprise. >> So let's talk about it. If we think about the modern data experience, give us a couple of kind of highlights of what are the two or three things that you absolutely must do differently? >> Well, the first thing is just cloud everywhere. And again, this is cloud the model, not cloud the place. And so the first thing we do is talk about the lessons of cloud with customers. Standardization of services. Not having bespoke infrastructure. You know, designing a set of tiers of storage and delivering that, and then really working on automation. Standardization and automation so that customers can be self-serviced. It's easy to say, but when we go into most storage environments today, they just don't operate like this, right? It's still very bespoke. And so giving customers the tools to be able to design their storage layers, their tiers, if you will, and deliver those services in an on-demand fashion. >> So one of the things that we've uncovered when we talk to customers is as they try to do more exciting and advanced types of workloads in clouds, and discovering that the range of data services provided by the cloud are not as robust, they're not as numerous, they're not as usable as some of the data services that you historically were able to get out of on-premise technology. Now, you mentioned that you are bringing your core management infrastructure into the cloud. Are you able therefore to provide a more rich and complementary range of data services without undermining or compromising that cloud experience? >> I think the key is that cloud experience, that increasingly you need that cloud experience, and it's not either/or, it's both. And so folks have realized that the cloud isn't a panacea. They can absolutely do their work on-prem with data at a lower cost and larger scale and higher performance. They can leverage the cloud for agility. And what's strategic is to have that bridge that allows them to go back and forth depending on the needs of the project. And so when we say cloud everywhere, that assumes that you're going to want to use things on the cloud, in the cloud, and on-prem, and you need a strategic layer of technology for data that can bridge both sides. That's a key part of what we try to deliver. >> So as you talk to your customers, are they utilizing Pure as a way of, or basically the Pure approach to the modern data experience as a way of getting other elements of IT and other elements of the business to think differently and to use data as the foundation for thinking about IT and digital business differently? >> Absolutely. I mean, I'll give you an example. One of our customers is a manufacturing customer. They run a large SAP instance. They wanted to have more agility in how they develop their SAP application. And so they use Pure on-prem to host that application, but they leverage our cloud block store offering to be able to do test dev in the cloud. And this allows them to easily spin up instances, copy production data to the cloud to be able to do test dev around it. And so it's brought new levels of cloud agility to what was a traditional kind of on-prem app. >> That's a great example. Are there any other types of things beyond just test dev that you can think about where the ability to have the certainty associated with Pure and the flexibility associated with the cloud is changing the way IT's thinking? >> I think another big one is DR. You know, if you look at DR investments, folks don't necessarily want to have a second data center. And so being able to leverage the cloud as the DR site not only reduces the cost of DR, but that data's already there, so it then unlocks test dev and other use cases around the cloud. And so that's a big one we see people interested in around cloud block store. >> Now, Pure, even before the modern data experience, was one of the early talkers or early storage companies to talk about how important multi-cloud is going to be. >> Absolutely. >> How does Pure as a target facilitate the practical reality, the pragmatic reality that large enterprises are going to source cloud services from multiple different providers? >> Yeah, I mean, I think, you know, customers are earlier in their journey right now around cloud, so for them, it's more about hybrid cloud than multi-cloud. Multi-cloud is a place they want to get to eventually. But incumbent upon that means a standardized set of services so that storage can speak and be the same, whether it be on this cloud, on that cloud, or on-prem. And look, there's work to do on both sides of the equation, right? If you look at on-prem storage, tier one block storage, we saw that as a gap in the public cloud, so that's why we brought to market cloud block store. If you look at what most people use in the public cloud, it's object storage. Well, most enterprises don't have an object store on-prem. It's one of the reasons we added an object interface to our FlashBlade product. And so this isn't just about bringing things to the public cloud. It's also about bringing some of the public cloud storage services on-prem and making sure they can connect. >> Obviously Pure is associated with storage devices even though you, modern data experience, and what you did at Accelerate is introducing new service classes into how you're going to engage your customers and how customers can source your expertise in their business. But how is that changing Pure? >> I think you picked up on a really interesting thing there around service classes, because one of the things, you know, from the earliest days of Pure, one of our goals was to deliver on the all-flash data center. You know, we obviously brought out tier one flash products to go after the highest end. But we realized that if we wanted to be able to go after all data across the data center, you needed to be able to serve more than one class of data. And so another big push that we announced at Accelerate this year was a QLC-based flash device, the FlashArray//C. And this allows us to really go after that second tier of larger scale and tier two application data in enterprise, to be able to bring that same all-flash cloud experience to this tier two data. >> So what's next? >> I think a big piece of that is we just announced that, so going after that is a large piece of it. The other thing we're really working on is driving up the level of automation and intelligence within the product line. If you look at the first generation of Pure, it was all about simple, right? You know, we have a SaaS-based management experience with Pure1, and we delivered consumer simplicity to this enterprise storage landscape, which was remarkably refreshing to folks. But if you look at this next generation, customers are looking for more intelligence and automation, and so the way you deliver simple to a more sophisticated customer today is open APIs, smart automation, plugin with the orchestration frameworks they're using. And so we're doing a lot of work not only in our API level and our automation level, but also the behind the scenes with our meta AI engine to understand workload and to make intelligent decisions for the customer without them having to deal with it. >> Matt, well, thank you once again for being on theCUBE. >> Likewise. Thanks, Peter. >> And thanks for joining us for another CUBE conversation. I'm Peter Burris. See you next time. (jazzy music)