(soft music) >> Hello, fellow hardware nerds and welcome back to Dallas Texas where we're reporting live from Supercomputing 2022. My name is Savannah Peterson, joined with the John Furrier on my left. >> Looking good today. >> Thank you, John, so are you. It's been a great show so far. >> We've had more hosts, more guests coming than ever before. >> I know. >> Amazing, super- >> We've got a whole thing going on. >> It's been a super computing performance. >> It, wow. And, we'll see how many times we can say super on this segment. Speaking of super things, I am in a very unique position right now. I am a flanked on both sides by people who have been doing content on theCUBE for 12 years. Yes, you heard me right, our next guest was on theCUBE 12 years ago, the third event, was that right, John? >> Man: First ever VM World. >> Yeah, the first ever VM World, third event theCUBE ever did. We are about to have a lot of fun. Please join me in welcoming Justin Emerson of Pure Storage. Justin, welcome back. >> It's a pleasure to be here. It's been too long, you never call, you don't write. (Savannah laughs) >> Great to see you. >> Yeah, likewise. >> How fun is this? Has the set evolved? Is everything looking good? >> I mean, I can barely remember what happened last week, so. (everyone laughs) >> Well, I remember lot's changed that VM world. You know, Paul Moritz was the CEO if you remember at that time. His actual vision actually happened but not the way, for VMware, but the industry, the cloud, he called the software mainframe. We were kind of riffing- >> It was quite the decade. >> Unbelievable where we are now, how we got here, but not where we're going to be. And you're with Pure Storage now which we've been, as you know, covering as well. Where's the connection into the supercomputing? Obviously storage performance, big part of this show. >> Right, right. >> What's the take? >> Well, I think, first of all it's great to be back at events in person. We were talking before we went on, and it's been so great to be back at live events now. It's been such a drought over the last several years, but yeah, yeah. So I'm very glad that we're doing in person events again. For Pure, this is an incredibly important show. You know, the product that I work with, with FlashBlade is you know, one of our key areas is specifically in this high performance computing, AI machine learning kind of space. And so we're really glad to be here. We've met a lot of customers, met a lot of other folks, had a lot of really great conversations. So it's been a really great show for me. And also just seeing all the really amazing stuff that's around here, I mean, if you want to find, you know, see what all the most cutting edge data center stuff that's going to be coming down the pipe, this is the place to do it. >> So one of the big themes of the show for us and probably, well, big theme of your life, is balancing power efficiency. You have a product in this category, Direct Flash. Can you tell us a little bit more about that? >> Yeah, so Pure as a storage company, right, what do we do differently from everybody else? And if I had to pick one thing, right, I would talk about, it's, you know, as the name implies, we're an all, we're purely flash, we're an all flash company. We've always been, don't plan to be anything else. And part of that innovation with Direct Flash is the idea of rather than treating a solid state disc as like a hard drive, right? Treat it as it actually is, treat it like who it really is and that's a very different kind of thing. And so Direct Flash is all about bringing native Flash interfaces to our product portfolio. And what's really exciting for me as a FlashBlade person, is now that's also part of our FlashBlade S portfolio, which just launched in June. And so the benefits of that are our myriad. But, you know, talking about efficiency, the biggest difference is that, you know, we can use like 90% less DRAM in our drives, which you know, everything uses, everything that you put in a drive uses power, it adds cost and all those things and so that really gives us an efficiency edge over everybody else and at a show like this, where, I mean, you walk the aisles and there's there's people doing liquid cooling and so much immersion stuff, and the reason they're doing that is because power is just increasing everywhere, right? So if you can figure out how do we use less power in some areas means you can shift that budget to other places. So if you can talk to a customer and say, well, if I could shrink your power budget for storage by two thirds or even, save you two-thirds of power, how many more accelerators, how many more CPUs, how much more work could you actually get done? So really exciting. >> I mean, less power consumption, more power and compute. >> Right. >> Kind of power center. So talk about the AI implications, where the use cases are. What are you seeing here? A lot of simulations, a lot of students, again, dorm room to the boardroom we've been saying here on theCUBE this is a great broad area, where's the action in the ML and the AI for you guys? >> So I think, not necessarily storage related but I think that right now there's this enormous explosion of custom silicon around AI machine learning which I as a, you said welcome hardware nerds at the beginning and I was like, ah, my people. >> We're all here, we're all here in Dallas. >> So wonderful. You know, as a hardware nerd we're talking about conferences, right? Who has ever attended hot chips and there's so much really amazing engineering work going on in the silicon space. It's probably the most exciting time for, CPU and accelerator, just innovation in, since the days before X 86 was the defacto standard, right? And you could go out and buy a different workstation with 16 different ISAs. That's really the most exciting thing, I walked past so many different places where you know, our booth is right next to Havana Labs with their gout accelerator, and they're doing this cute thing with one of the AI image generators in their booth, which is really cute. >> Woman: We're going to have to go check that out. >> Yeah, but that to me is like one of the more exciting things around like innovation at a, especially at a show like this where it's all about how do we move forward, the state of the art. >> What's different now than just a few years ago in terms of what's opening up the creativity for people to look at things that they could do with some of the scale that's different now. >> Yeah well, I mean, every time the state of the art moves forward what it means is, is that the entry level gets better, right? So if the high end is going faster, that means that the mid-range is going faster, and that means the entry level is going faster. So every time it pushes the boundary forward, it's a rising tide that floats all boats. And so now, the kind of stuff that's possible to do, if you're a student in a dorm room or if you're an enterprise, the world, the possible just keeps expanding dramatically and expanding almost, you know, geometrically like the amount of data that we are, that we have, as a storage guy, I was coming back to data but the amount of data that we have and the amount of of compute that we have, and it's not just about the raw compute, but also the advances in all sorts of other things in terms of algorithms and transfer learning and all these other things. There's so much amazing work going on in this area and it's just kind of this Kay Green explosion of innovation in the area. >> I love that you touched on the user experience for the community, no matter the level that you're at. >> Yeah. >> And I, it's been something that's come up a lot here. Everyone wants to do more faster, always, but it's not just that, it's about making the experience and the point of entry into this industry more approachable and digestible for folks who may not be familiar, I mean we have every end of the ecosystem here, on the show floor, where does Pure Storage sit in the whole game? >> Right, so as a storage company, right? What AI is all about deriving insights from data, right? And so everyone remembers that magazine cover data's the new oil, right? And it's kind of like, okay, so what do you do with it? Well, how do you derive value from all of that data? And AI machine learning and all of this supercomputing stuff is about how do we take all this data? How do we innovate with it? And so if you want data to innovate with, you need storage. And so, you know, our philosophy is that how do we make the best storage platforms that we can using the best technology for our customers that enable them to do really amazing things with AI machine learning and we've got different products, but, you know at the show here, what we're specifically showing off is our new flashlight S product, which, you know, I know we've had Pure folks on theCUBE before talking about FlashBlade, but for viewers out there, FlashBlade is our our scale out unstructured data platform and AI and machine learning and supercomputing is all about unstructured data. It's about sensor data, it's about imaging, it's about, you know, photogrammetry, all this other kinds of amazing stuff. But, you got to land all that somewhere. You got to process that all somewhere. And so really high performance, high throughput, highly scalable storage solutions are really essential. It's an enabler for all of the amazing other kinds of engineering work that goes on at a place like Supercomputing. >> It's interesting you mentioned data's oil. Remember in 2010, that year, our first year of theCUBE, Hadoop World, Hadoop just started to come on the scene, which became, you know kind of went away and, but now you got, Spark and Databricks and Snowflake- >> Justin: And it didn't go away, it just changed, right? >> It just got refactored and right size, I think for what the people wanted it to be easy to use but there's more data coming. How is data driving innovation as you bring, as people see clearly the more data's coming? How is data driving innovation as you guys look at your products, your roadmap and your customer base? How is data driving innovation for your customers? >> Well, I think every customer who has been, you know collecting all of this data, right? Is trying to figure out, now what do I do with it? And a lot of times people collect data and then it will end up on, you know, lower slower tiers and then suddenly they want to do something with it. And it's like, well now what do I do, right? And so there's all these people that are reevaluating you know, we, when we developed FlashBlade we sort of made this bet that unstructured data was going to become the new tier one data. It used to be that we thought unstructured data, it was emails and home directories and all that stuff the kind of stuff that you didn't really need a really good DR plan on. It's like, ah, we could, now of course, as soon as email goes down, you realize how important email is. But, the perspectives that people had on- >> Yeah, exactly. (all laughing) >> The perspectives that people had on unstructured data and it's value to the business was very different and so now- >> Good bet, by the way. >> Yeah, thank you. So now unstructured data is considered, you know, where companies are going to derive their value from. So it's whether they use the data that they have to build better products whether it's they use the data they have to develop you know, improvements in processes. All those kinds of things are data driven. And so all of the new big advancements in industry and in business are all about how do I derive insights from data? And so machine learning and AI has something to do with that, but also, you know, it all comes back to having data that's available. And so, we're working very hard on building platforms that customers can use to enable all of this really- >> Yeah, it's interesting, Savannah, you know, the top three areas we're covering for reinventing all the hyperscale events is data. How does it drive innovation and then specialized solutions to make customers lives easier? >> Yeah. >> It's become a big category. How do you compose stuff and then obviously compute, more and more compute and services to make the performance goes. So those seem to be the three hot areas. So, okay, data's the new oil refineries. You've got good solutions. What specialized solutions do you see coming out because once people have all this data, they might have either large scale, maybe some edge use cases. Do you see specialized solutions emerging? I mean, obviously it's got DPU emerging which is great, but like, do you see anything else coming out at that people are- >> Like from a hardware standpoint. >> Or from a customer standpoint, making the customer's lives easier? So, I got a lot of data flowing in. >> Yeah. >> It's never stopping, it keeps powering in. >> Yeah. >> Are there things coming out that makes their life easier? Have you seen anything coming out? >> Yeah, I think where we are as an industry right now with all of this new technology is, we're really in this phase of the standards aren't quite there yet. Everybody is sort of like figuring out what works and what doesn't. You know, there was this big revolution in sort of software development, right? Where moving towards agile development and all that kind of stuff, right? The way people build software change fundamentally this is kind of like another wave like that. I like to tell people that AI and machine learning is just a different way of writing software. What is the output of a training scenario, right? It's a model and a model is just code. And so I think that as all of these different, parts of the business figure out how do we leverage these technologies, what it is, is it's a different way of writing software and it's not necessarily going to replace traditional software development, but it's going to augment it, it's going to let you do other interesting things and so, where are things going? I think we're going to continue to start coalescing around what are the right ways to do things. Right now we talk about, you know, ML Ops and how development and the frameworks and all of this innovation. There's so much innovation, which means that the industry is moving so quickly that it's hard to settle on things like standards and, or at least best practices you know, at the very least. And that the best practices are changing every three months. Are they really best practices right? So I think, right, I think that as we progress and coalesce around kind of what are the right ways to do things that's really going to make customers' lives easier. Because, you know, today, if you're a software developer you know, we build a lot of software at Pure Storage right? And if you have people and developers who are familiar with how the process, how the factory functions, then their skills become portable and it becomes easier to onboard people and AI is still nothing like that right now. It's just so, so fast moving and it's so- >> Wild West kind of. >> It's not standardized. It's not industrialized, right? And so the next big frontier in all of this amazing stuff is how do we industrialize this and really make it easy to implement for organizations? >> Oil refineries, industrial Revolution. I mean, it's on that same trajectory. >> Yeah. >> Yeah, absolutely. >> Or industrial revolution. (John laughs) >> Well, we've talked a lot about the chaos and sort of we are very much at this early stage stepping way back and this can be your personal not Pure Storage opinion if you want. >> Okay. >> What in HPC or AIML I guess it all falls under the same umbrella, has you most excited? >> Ooh. >> So I feel like you're someone who sees a lot of different things. You've got a lot of customers, you're out talking to people. >> I think that there is a lot of advancement in the area of natural language processing and I think that, you know, we're starting to take things just like natural language processing and then turning them into vision processing and all these other, you know, I think the, the most exciting thing for me about AI is that there are a lot of people who are, you are looking to use these kinds of technologies to make technology more inclusive. And so- >> I love it. >> You know the ability for us to do things like automate captioning or the ability to automate descriptive, audio descriptions of video streams or things like that. I think that those are really,, I think they're really great in terms of bringing the benefits of technology to more people in an automated way because the challenge has always been bandwidth of how much a human can do. And because they were so difficult to automate and what AI's really allowing us to do is build systems whether that's text to speech or whether that's translation, or whether that's captioning or all these other things. I think the way that AI interfaces with humans is really the most interesting part. And I think the benefits that it can bring there because there's a lot of talk about all of the things that it does that people don't like or that they, that people are concerned about. But I think it's important to think about all the really great things that maybe don't necessarily personally impact you, but to the person who's not cited or to the person who you know is hearing impaired. You know, that's an enormously valuable thing. And the fact that those are becoming easier to do they're becoming better, the quality is getting better. I think those are really important for everybody. >> I love that you brought that up. I think it's a really important note to close on and you know, there's always the kind of terminator, dark side that we obsess over but that's actually not the truth. I mean, when we think about even just captioning it's a tool we use on theCUBE. It's, you know, we see it on our Instagram stories and everything else that opens the door for so many more people to be able to learn. >> Right? >> And the more we all learn, like you said the water level rises together and everything is magical. Justin, it has been a pleasure to have you on board. Last question, any more bourbon tasting today? >> Not that I'm aware of, but if you want to come by I'm sure we can find something somewhere. (all laughing) >> That's the spirit, that is the spirit of an innovator right there. Justin, thank you so much for joining us from Pure Storage. John Furrier, always a pleasure to interview with you. >> I'm glad I can contribute. >> Hey, hey, that's the understatement of the century. >> It's good to be back. >> Yeah. >> Hopefully I'll see you guys in, I'll see you guys in 2034. >> No. (all laughing) No, you've got the Pure Accelerate conference. We'll be there. >> That's right. >> We'll be there. >> Yeah, we have our Pure Accelerate conference next year and- >> Great. >> Yeah. >> I love that, I mean, feel free to, you know, hype that. That's awesome. >> Great company, great runs, stayed true to the mission from day one, all Flash, continue to innovate congratulations. >> Yep, thank you so much, it's pleasure being here. >> It's a fun ride, you are a joy to talk to and it's clear you're just as excited as we are about hardware, so thanks a lot Justin. >> My pleasure. >> And thank all of you for tuning in to this wonderfully nerdy hardware edition of theCUBE live from Dallas, Texas, where we're at, Supercomputing, my name's Savannah Peterson and I hope you have a wonderful night. (soft music)

>>Good afternoon and welcome back to Detroit, Lisa Martin here with John Furrier. We are live day two of our coverage of Coan Cloud Native Con North America. John, we've had great conversations. Yeah. All day yesterday. Half a day today. So far we're talking all things, Well, not all things Kubernetes so much more than that. We also have to talk about storage and data management solutions for Kubernetes projects, cuz that's obviously critical. >>Yeah, I mean the big trend here is Kubernetes going mainstream has been for a while. The adopt is crossing over, it's crossing the CADs and with that you're seeing security concerns. You're seeing things being gaps being filled. But enterprise grade is really the, the, the story. It's going enterprise, that's managed services, that's professional service, that's basically making things work at scale. This next segment hits that part and we are gonna talk about it in grade length >>With one of our alumni. Moral morale to Molly is back DP and GM of Port Work's Peer Storage. Great to have you back really? >>Yeah, absolutely. Delightful >>To be here. So I was looking on the website, number one in Kubernetes storage. Three years in a row. Yep. Awesome. What's Coworks doing here at KU Con? >>Well, I'll tell you, we, our engineering crew has been so productive and hard at work that I almost can't decide what to kind of tell you. But I thought what, what, what I thought I would do is kind of tell you that we are in forefront of two major trends in the world of Kubernetes. Right? And the, the two trends that I see are one is as a service, so is trend number one. So it's not software eating the world anymore. That's, that's old, old, old news. It's as a service unifying the world. The world wants easy, We all are, you know, subscribers to things like Netflix. We've been using Salesforce or other HR functions. Everything is as a service. And in the world of Kubernetes, it's a sign of that maturity that John was talking about as a platform that now as a service is the big trend. >>And so headline number one, if you will, is that Port Works is leading in the data management world for Kubernetes by providing, we're going all in on easy on as a service. So everything we do, we are satisfying it, right? So if you think, if you think about, if you think about this, that, that there are really, most of the people who are consuming Kubernetes are people who are building platforms for their dev users. And dev users want self service. That's one of the advantages of, of, of Kubernetes. And the more it is service size and made as a service, the more ready to consume it is. And so we are announcing at the show that we have, you know, the basic Kubernetes data management as a service, ha d r as a service. We have backup as a service and we have database as a service. So these are the three major components of data. And all of those are being made available as a service. And in fact, we're offering and announcing at the show our backup as a service freemium version where you can get free forever a terabyte of, of, you know, stuff to do for Kubernetes for forever. >>Congratulations on the announcement. Totally. In line with what the market wants. Developers want Selfer, they wanna also want simplicity by the way they'll leave if they don't like the service. Correct. So that you, you know that before we get into some more specifics, I want Yeah. Ask you on the industry and some of the point solutions you have, what, it's been two years since the acquisition with Pure Storage. Can you just give an update on how it's gone? Obviously as a service, you guys are hitting all your Marks, developers love it. Storage are big part of the game right now as well as these environments. Yeah. What's the update post acquisition two years. You had a great offering Stay right In >>Point Works. Yeah. So look, John, you're, you're, you're a veteran of the industry and have seen lots of acquisitions, right? And I've been acquired twice before myself. So, you know, there's, there's always best practices and poor practices in terms of acquisitions and I'm, you know, really delighted to say I think this, this acquisition has had some of the best practices. Let me just name a couple of them, right? One of them is just cultural fit, right? Cultural fit is great. Entrepreneurs, anybody, it's not just entrepreneurs. Everybody loves to work in a place they enjoy working with, with people that they, you know, thrive when they, when they interact with. And so the cultural fit with, with Pure is fantastic. The other one is the strategic intent that Pure had when they acquired us is still true. And so that goes a long way, you know, in terms of an investment profile, in terms of the ability to kind of leverage assets within the company. So Pure had kind of disrupted the world of storage using Flash and they wanted to disrupt higher up the stack using Kubernetes. And that's kind of been our role inside their strategy. And it's, it's still true. >>So culture, strategic intent. Yeah. Product market fit as well. You were, you weren't just an asset for customers or acquisition and then let the founders go through their next thing. You are part of their growth play. >>Absolutely. Right. The, the beauty of, of the kind of product market fit is, let's talk about the market is we have been always focused on the global two k and that is at the heart of, you know, purest 10,000 strong customer base, right? They have very strong presence in the, in the global two k. And we, we allow them to kind of go to those same folks with, with the offering. >>So satisfying everything that you do. What's for me as a business, whether I'm a financial services organization, I'm a hospital, I'm a retailer, what's in it for me >>As a customer? Yeah. So the, the what's in it for, for me is two things. It's speed and ease of use, which in a way are related. But, but, but you know, one is when something is provided as a service, it's much more consumable. It's instantly ready. It's like instant oatmeal, right? You just get it just ad hot water and it's there. Yep. So the world of of it has moved from owning large data centers, right? That used to be like 25 years ago and running those data centers better than everybody else to move to let me just consume a data center in the form of a cloud, right? So satisfying the cloud part of the data center. Now people are saying, well I expect that for software and services and I don't want it just from the public cloud, I want it from my own IT department. >>This is old news. And so the, the, the big news here is how fast Kubernetes has kind of moved everything. You know, you take a lot of these changes, Kubernetes is a poster child for things happening faster than the last wave. And in the last couple of years I would say that as a service model has really kind of thrived in the world of Kubernetes. And developers want to be able to get it fast. And the second thing is they want to be able to operate it fast. Self-service is the other benefit. Yeah. So speed and self-service are both benefits of, of >>This. Yeah. And, and the thing that's come up clearly in the cube, this is gonna be part of the headlines we'll probably end up getting a lot of highlights from telling my team to make a note of this, is that developers are gonna be be the, the business if you, if you take digital transformation to its conclusion, they're not a department that serves the business, they are the business that means Exactly. They have to be more productive. So developer productivity has been the top story. Yes. Security as a serves all these things. These are, these are examples to make developers more productive. But one of the things that came up and I wanna get your reaction to is, is that when you have disruption and, and the storage vision, you know what disruption it means. Cuz there's been a whole discussion around disruptive operations. When storage goes down, you have back m dr and failover. If there's a disruption that changes the nature of invisible infrastructure, developers want invisible infrastructure. That's the future steady state. So if there's a disruption in storage >>Yeah. It >>Can't affect the productivity and the tool chains and the workflows of developers. Yep. Right? So how do you guys look at that? Cuz you're a critical component. Storage is a service is a huge thing. Yeah. Storage has to, has to work seamlessly. And let's keep the developers out of the weeds. >>John. I think what, what what you put your finger on is another huge trend in the world of Kubernetes where at Cube Con, after all, which is really where, where all the leading practitioners both come and the leading vendors are. So here's the second trend that we are leading and, and actually I think it's happening not just with us, but with other, for folks in the industry. And that is, you know, the world of DevOps. Like DevOps has been such a catchphrase for all, all of us in the industry last five years. And it's been both a combination of cultural change as well as technology change. Here's what the latest is on the, in the world of DevOps. DevOps is now crystallized. It's not some kind of mysterious art form that you read about how people are practicing. DevOps is, it's broken into two, two things now. >>There is the platform part. So DevOps is now a bunch of platforms. And the other part of DevOps is a bunch of practices. So a little bit on both these, the platforms in the world of es there's only three platforms, right? There's the orchestration platforms, the, you know, eks, the open ships of the world and so on. There are the data management platforms, pro people like Port Works. And the third is security platforms, right? You know, Palo Alto Networks, others Aqua or all in this. So these are the three platforms and there are platform engineering teams now that many of our largest customers, some of the largest banks, the largest service providers, they're all operating as a ES platform engineering team. And then now developers, to your point, developers are in the practice of being able to use these platforms to launch new services. So the, the actual IT ops, the ops are run by developers now and they can do it on these platforms. And the platform engineering team provide that as an ease of use and they're there to troubleshoot when problems happen. So the idea of DevOps as a ops practice and a platform is the newest thing. E and, and ports and pure storage leading in the world of data management platforms >>There. Talk about a customer example that you think really articulates the value that Port Works and Pure Storage delivers from a data management perspective. >>Yeah, so there's so many examples. One of the, one of the longest running examples we have is a very, very large service provider that, you know, you all know and probably use, and they have been using us in the cable kinda set box or cable box business. They get streams of data from, from cable boxes all over the world. They collected all in a centralized large kind of thing and run elastic search and analytics on it. Now what they have done is they couldn't keep up with this at the scale and the depth, right? The speed of, of activity and the distributed nature of the activity. The only way to solve this was to use something like Kubernetes manage with Spark coming, bringing all the data in to deep, deep, deep silos of storage, which are all running not even on a sand, but on kind of, you know, very deep terabytes and terabytes of, of storage. So all of this is orchestrated with the Heco coworks and there's a platform engineering team. We are building that platform for them with some of these other components that allows them to kind of do analytics and, and make some changes in real time. Huge kind of setup for, for >>That. Yeah. Well, you guys have the right architecture. I love the vision. I love what you guys are doing. I think this is right in line with Pures. They've always been disruptors. I remember when we first interviewed the CEO when they started Yep. They, they stayed on path. They didn't waiver. EMC was the big player. They ended up taking their lunch and dinner as well and they beat 'em in the marketplace. But now you got this traction here. So I have to ask you, how's the business, what's the results look like? Either GM cloud native business unit of a storage company that's transformed and transforming? >>Yeah, you know, it's interesting, we just hit the two year anniversary, right John? And so what we did was just kind of like step back and hey, you know, we're running so hard, you just take a step back. And we've tripled the business in the two years since the acquisition, the two years before and, and we were growing through proven. So, you know, that that's quite a fe and we've tripled the number of people, the amount of engineering investments we have, the number of go to market investments have, have been, have been awesome. So business is going really well though, I will say. But I think, you know, we have, we can't be, we we're watching the market closely. You know, as a former ceo, I, you have to kind of learn to read the tea leaves when you invest. And I think, you know, what I would say is we're proceeding with caution in the next two quarters. I view business transformation as not a cancelable activity. So that's the, that's the good news, right? Our customers are large, it's, >>It's >>Right. All they're gonna do is say, Hey, they're gonna put their hand, their hand was always going right on the dial. Now they're kind of putting their hand on the dial going, hey, where, what is happening? But my, my own sense of this is that people will continue to invest through it. The question is at what level? And I also think that this is a six month kind of watch, the watch where, where we put the dial. So Q4 and q1 I think are kind of, you know, we have our, our watch kind of watch the market sign. But I have the highest confidence. What >>Does your gut tell you? You're an entrepreneur, >>Which my, my gut says that we'll go through a little bit of a cautious investment period in the next six months. And after that I think we're gonna be back in, back full, full in the crazy growth that we've always been. We're gonna grow by the way, in the next think >>It's core style. I think I'm, I'm more bullish. I think there's gonna be some, you know, weeding out of some overinvestment pre C or pre bubble. But I think tech's gonna continue to grow. I don't see >>It's stopping. Yeah. And, and the investment is gonna be on these core platforms. See, back to the platform story, it's gonna be in these core platforms and on unifying everything, let's consume it better rather than let's go kind of experiment with a whole bunch of things all over the map, right? So you'll see less experimentation and more kind of, let's harvest some of the investments we've made in the last couple >>Of years and actually be able to, to enable companies in any industry to truly be data companies. Because absolutely. We talked about as a service, we all have these expectations that any service we want, we can get it. Yes. There's no delay because patients has gone Yeah. From the pandemic. >>So it is kind of, you know, tightening up the screws on what they've built. They, you know, adding some polish to it, adding some more capability, like I said, a a a, a combination of harvesting and new investing. It's a combination I think is what we're gonna see. >>Yeah. What are some of the things that you're looking forward to? You talked about some of the, the growth things in the investment, but as we round out Q4 and head into a new year, what are you excited about? >>Yeah, so you know, I mentioned our, as a service kind of platform, the global two K for us has been a set of customers who we co-create stuff with. And so one of the other set of things that we are very excited about and announcing is because we're deployed at scale, we're, we're, we have upgraded our backend. So we have now the ability to go to million IOPS and more and, and for, for the right backends. And so Kubernetes is a add-on which will not slow down your, your core base infrastructure. Second thing that that we, we have is added a bunch of capability in the disaster recovery business continuity front, you know, we always had like metro kind of distance dr. We had long distance dr. We've added a near sync Dr. So now we can provide disaster recovery and business continuity for metro distances across continents and across the planet. Right? That's kind of a major change that we've done. The third thing is we've added the capability for file block and Object. So now by adding object, we're really a complete solution. So it is really that maturity of the business Yeah. That you start seeing as enterprises move to embracing a platform approach, deploying it much more widely. You talked about the early majority. Yeah. Right. And so what they require is more enterprise class capability and those are all the things that we've been adding and we're really looking forward >>To it. Well it sounds like tremendous evolution and maturation of Port Works in the two years since it's been with Pure Storage. You talked about the cultural alignment, great stuff that you're achieving. Congratulations on that. Yeah. Great stuff >>Ahead and having fun. Let's not forget that, that's too life's too short to do. It is right. >>You're right. Thank you. We will definitely, as always on the cube, keep our eyes on this space. Mur. Meley, it's been great to have you back on the program. Thank you for joining, John. >>Thank you so much. It's pleasure. Our, >>For our guests and John Furrier, Lisa Martin here live in Detroit with the cube about Coan Cloud Native Con at 22. We'll be back after a short break.

>>Good afternoon and welcome back to Detroit, Lisa Martin here with John Furrier. We are live day two of our coverage of Coan Cloud Native, Con North America. John, we've had great conversations. Yeah. All day yesterday. Half a day today. So far we're talking all things, Well, not all things Kubernetes so much more than that. We also have to talk about storage and data management solutions for Kubernetes projects, cuz that's obviously critical. >>Yeah, I mean the big trend here is Kubernetes going mainstream has been for a while. The adopt is crossing over, it's crossing the CADs and with that you're seeing security concerns. You're seeing things being gaps being filled. But enterprise grade is really the, the, the story. It's going enterprise, that's managed services, that's professional service, that's basically making things work at scale. This next segment hits that, that part, and we're gonna talk about it in grade length >>With one of our alumni morale to Molly is back VP and GM of Port Work's peer Storage. Great to have you back really? >>Yeah, absolutely. Delightful to >>Be here. So I was looking on the website, number one in Kubernetes storage. Three years in a row. Yep. Awesome. What's Coworks doing here at KU Con? >>Well, I'll tell you, we, our engineering crew has been so productive and hard at work that I almost can't decide what to kind of tell you. But I thought what, what, what I thought I would do is kind of tell you that we are in forefront of two major trends in the world of es. Right? And the, the two trends that I see are one is as a service, so is trend number one. So it's not software eating the world anymore. That's, that's old, old, old news. It's as a service, unifying the world. The world wants easy, We all are, you know, subscribers to things like Netflix. We've been using Salesforce or other HR functions. Everything is as a service. And in the world of Kubernetes, it's a sign of that maturity that John was talking about as a platform that now as a service is the big trend. >>And so headline number one, if you will, is that Port Works is leading in the data management world for the Kubernetes by providing, we're going all in on easy on as a service. So everything we do, we are satisfying it, right? So if you think, if you think about, if you think about this, that, that there are really, most of the people who are consuming Kubernetes are people who are building platforms for their dev users and their users want self service. That's one of the advantages of, of, of Kubernetes. And the more it is service size and made as a service, the more ready to consume it is. And so we are announcing at the show that we have, you know, the basic Kubernetes data management as a service, ha d r as a service. We have backup as a service and we have database as a service. So these are the three major components of data. And all of those are being made available as a service. And in fact, we're offering and announcing at the show our backup as a service freemium version where you can get free forever a terabyte of, of, you know, stuff to do for Kubernetes for forever. >>Congratulations on the announcement. Totally. In line with what the market wants. Developers want self serve, they wanna also want simplicity by the way they'll leave if they don't like the service. Correct. So that you, you know, that before we get into some more specifics, I want to Yeah. Ask you on the industry and some of the point solutions you have, what, it's been two years since the acquisition with Pure Storage. Can you just give an update on how it's gone? Obviously as a service, you guys are hitting all your Marks, developers love it. Storage a big part of the game right now as well as these environments. Yeah. What's the update post acquisition two years, You had a great offering Stay >>Right In Point Works. Yeah. So look, John, you're, you're, you're a veteran of the industry and have seen lots of acquisitions, right? And I've been acquired twice before myself. So, you know, there's, there's always best practices and poor practices in terms of acquisitions and I'm, you know, really delighted to say I think this, this acquisition has had some of the best practices. Let me just name a couple of them, right? One of them is just cultural fit, right? Cultural fit is great. Entrepreneurs, anybody, it's not just entrepreneurs. Everybody loves to work in a place they enjoy working with, with people that they, you know, thrive when they, when they interact with. And so the cultural fit with, with Pure is fantastic. The other one is the strategic intent that Pure had when they acquired us is still true. And so that goes a long way, you know, in terms of an investment profile, in terms of the ability to kind of leverage assets within the company. So Pure had kind of disrupted the world of storage using Flash and they wanted to disrupt higher up the stack using Kubernetes. And that's kind of been our role inside their strategy. And it's, it's still true. >>So culture, strategic intent. Yeah. Product market fit as well. You were, you weren't just an asset for customers or acquisition and then let the founders go through their next thing. You are part of their growth play. >>Absolutely. Right. The, the beauty of, of the kind of product market fit is, let's talk about the market is we have been always focused on the global two k and that is at the heart of, you know, purest 10,000 strong customer base, right? They have very strong presence in the, in the global two k. And we, we allow them to kind of go to those same folks with, with the offering. >>So satisfying everything that you do. What's for me as a business, whether I'm a financial services organization, I'm a hospital, I'm a retailer, what's in it for me >>As a customer? Yeah. So the, the what's in it for, for me is two things. It's speed and ease of use, which in a way are related. But, but, but you know, one is when something is provided as a service, it's much more consumable. It's instantly ready. It's like instant oatmeal, right? You just get it just adho water and it's there. Yep. So the world of of IT has moved from owning large data centers, right? That used to be like 25 years ago and running those data centers better than everybody else to move to let me just consume a data center in the form of a cloud, right? So satisfying the cloud part of the data center. Now people are saying, well I expect that for software and services and I don't want it just from the public cloud, I want it from my own IT department. >>This is old news. And so the, the, the big news here is how fast Kubernetes has kind of moved everything. You know, you take a lot of these changes, Kubernetes is a poster child for things happening faster than the last wave. And in the last couple of years I would say that as a service model has really kind of thrived in the world of Kubernetes. And developers want to be able to get it fast. And the second thing is they wanna be able to operate it fast. Self-service is the other benefit. Yeah. So speed and self-service are both benefits of, of >>This. Yeah. And, and the thing that's come up clearly in the cube, and this is gonna be part of the headlines, we'll probably end up getting a lot of highlights from telling my team to make a note of this, is that developers are gonna be be the business if you, if you take digital transformation to its conclusion, they're not a department that serves the business, they are the business that means Exactly. They have to be more productive. So developer productivity has been the top story. Yes. Security as a services, all these things. These are, these are examples to make developers more productive. But one of the things that came up and I wanna get your reaction to Yeah. Is, is that when you have disruption and, and the storage vision, you know what disruption it means. Cuz there's been a whole discussion around disruptive operations. When storage goes down, you have back DR. And failover. If there's a disruption that changes the nature of invisible infrastructure, developers want invisible infrastructure. That's the future steady state. So if there's a disruption in storage >>Yeah. It >>Can't affect the productivity and the tool chains and the workflows of developers. Yep. Right? So how do you guys look at that? Cause you're a critical component. Storage is a service, it's a huge thing. Yeah. Storage has to, has to work seamlessly. And let's keep the developers out of the weeds. >>John. I think what, what what you put your finger on is another huge trend in the world of Kubernetes where Atan after all, which is really where, where all the leading practitioners both come and the leading vendors are. So here's the second trend that we are leading and, and actually I think it's happening not just with us, but with other, for folks in the industry. And that is, you know, the world of DevOps. Like DevOps has been such a catchphrase for all of of us in the industry last five years. And it's been both a combination of cultural change as well as technology change. Here's what the latest is on the, in the world of DevOps. DevOps is now crystallized. It's not some kind of mysterious art form that you read about. Okay. How people are practicing. DevOps is, it's broken into two, two things now. >>There is the platform part. So DevOps is now a bunch of platforms. And the other part of DevOps is a bunch of practices. So a little bit on both these, the platforms in the world of es there's only three platforms, right? There's the orchestration platforms, the, you know, eks, the open ships of the world and so on. There are the data management platforms, pro people like Port Works. And the third is security platforms, right? You know, Palo Alto Networks, others Aqua are all in this. So these are the three platforms and there are platform engineering teams now that many of our largest customers, some of the largest banks, the largest service providers, they're all operating as a ES platform engineering team. And then now developers, to your point, developers are in the practice of being able to use these platforms to launch new services. So the, the actual IT ops, the ops are run by developers now and they can do it on these platforms. And the platform engineering team provide that as an ease of use and they're there to troubleshoot when problems happen. So the idea of DevOps as a ops practice and a platform is the newest thing. And, and ports and pure storage leading in the world of data management >>Platforms there. Talk about a customer example that you think really articulates the value that Port Works and Pure Storage delivers from a data management >>Perspective. Yeah, so there's so many examples. One of the, one of the longest running examples we have is a very, very large service provider that, you know, you all know and probably use, and they have been using us in the cable kind of set box or cable box business. They get streams of data from, from cable boxes all over the world. They collected all in a centralized large kind of thing and run elastic search and analytics on it. Now what they have done is they couldn't keep up with this at the scale and the depth, right? The speed of, of activity and the distributed nature of the activity. The only way to solve this was to use something like Kubernetes manage with Spark coming, bringing all the data in into deep, deep, deep silos of storage, which are all running not even on a sand, but on kind of, you know, very deep terabytes and terabytes of, of storage. So all of this is orchestrated with the he of Coworks and there's a platform engineering team. We are building that platform for them, them with some of these other components that allows them to kind of do analytics and, and make some changes in real time. Huge kind of setup for, for >>That. Yeah. Well, you guys have the right architecture. I love the vision. I love what you guys are doing. I think this is right in line with Pures. They've always been disruptors. I remember when we first interviewed the CEO and they started Yep. They, they stayed on path. They didn't waver. EMC was the big player. They ended up taking their lunch and dinner as well and they beat 'em in the marketplace. But now you got this traction here. So I have to ask you, how's the business, what's the results look like? You're a GM cloud native business unit of a storage company that's transformed and transforming. >>Yeah, you know, it's interesting, we just hit the two year anniversary, right John? And so what we did was just kind of like step back and hey to, you know, we're running so hard, you just take a step back and we've tripled the business in the two years since the acquisition, the two years before and, and we were growing through proven. So, you know, that that's quite a fee. And we've tripled the number of people, the amount of engineering investments we have, the number of go to market investments have been, have been awesome. So business is going really well though, I will say. But I think, you know, we have, we can't be, we're watching the market closely. You know, as a former ceo, I, you have to kind of learn to read the tea leaves when you invest. And I think, you know, what I would say is we're proceeding with caution in the next two quarters. I view business transformation as not a cancelable activity. So that's the, that's the good news, right? Our customers are large, >>It's >>Right. Never gonna stop prices, right? All they're gonna do is say, Hey, they're gonna put their hand, their hand was always going right on the dial. Now they're kind of putting their hand on the dial going, hey, where, what is happening? But my, my own sense of this is that people who continue to invest through it, the question is at what level? And I also think that this is a six month kind of watch, the watch where, where we put the dial. So Q4 and q1 I think are kind of, you know, we have our, our watch kind of watch the market sign. But I have the highest confidence. What >>Does your gut tell you? You're an >>Entrepreneur. My, my gut says that we'll go through a little bit of a cautious investment period in the next six months. And after that I think we're gonna be back in, back full, full in the crazy growth that we've always been. Yeah. We're gonna grow by the way, in the next, I think >>It's corn style. I think I'm, I'm more bullish. I think it's gonna be some, you know, weeding out of some overinvestment, pre covid or pre bubble. But I think tech's gonna continue to grow. I don't see >>It's stopping. Yeah. And, and the investment is gonna be on these core platforms. See, back to the platform story, it's gonna be in these lower platforms and on unifying everything, let's consume it better rather than let's go kind of experiment with a whole bunch of things all over the map, right? So you'll see less experimentation and more kind of, let's harvest some of the investments we've made in the last couple >>Of years and actually be able to, to enable companies in, in the industry to truly be data companies because absolutely. We talked about as a service, we all have these expectations that any service we want, we can get it. Yes. There's no delay because patients has gone Yeah. From the pandemic. >>So it is kind of, you know, tightening up the screws on what they've built. They, you know, adding some polish to it, adding some more capability, like I said, a, a a, a combination of harvesting and new investing. It's a combination I think is what we're gonna see. >>Yeah. What are some of the things that you're looking forward to? You talked about some of the, the growth things in the investment, but as we round out Q4 and head into a new year, what are you excited about? >>Yeah, so, you know, I mentioned our, as a service kind of platform. The global two K for us has been a set of customers who we co-create stuff with. And so one of the other set of things that we are very excited about and announcing is because we're deployed at scale, we're, we're, we have upgraded our backend. So we have now the ability to go to million IOPS and more and, and for, for the right backends. And so Kubernetes is a add-on, which will not slow down your, your core base infrastructure. Second thing that that we, we have is added a bunch of capability in the disaster recovery business continuity front, you know, we always had like metro kind of distance Dr. We had long distance dr. We've added a near sync Dr. So now we can provide disaster recovery and business continuity for metro distances across continents and across the planet. Right? That's kind of a major change that we've done. The third thing is we've added the capability for file block and Object. So now by adding object, we're really a complete solution. So it is really that maturity of the business Yeah. That you start seeing as enterprises move to embracing a platform approach, deploying it much more widely. You talked about the early majority. Yeah. Right. And so what they require is more enterprise class capability and those are all the things that we've been adding and we're really looking forward to it. >>Well it sounds like tremendous evolution and maturation of Port Works in the two years since it's been with Pure Storage. You talked about the cultural alignment, Great stuff that you are achieving. Congratulations on that. Great stuff >>Ahead and having fun. Let's not forget that that's too life's too short to do. It is. You're right. >>Right. Thank you. We will definitely, as always on the cube, keep our eyes on this space. Mur. Meley, it's been great to have you back on the program. Thank you for joining, John. >>Great. Thank you so much. It's a pleasure. Our, >>For our guests and John Furrier, Lisa Martin here live in Detroit with the cube about Cob Con Cloud native Con at 22. We'll be back after a short break.

(upbeat music) >> The Cloud essentially turned the data center into an API and ushered in the era of programmable infrastructure, no longer do we think about deploying infrastructure in rigid silos with a hardened, outer shell, rather infrastructure has to facilitate digital business strategies. And what this means is putting data at the core of your organization, irrespective of its physical location. It also means infrastructure generally and storage specifically must be accessed as sets of services that can be discovered, deployed, managed, secured, and governed in a DevOps model or OpsDev, if you prefer. Now, this has specific implications as to how vendor product strategies will evolve and how they'll meet modern data requirements. Welcome to this Cube conversation, everybody. This is Dave Vellante. And with me to discuss these sea changes is Ajay Singh, the Chief Product Officer of Pure Storage, Ajay welcome. >> Thank you, David, gald to be on. >> Yeah, great to have you, so let's talk about your role at Pure. I think you're the first CPO, what's the vision there? >> That's right, I just joined up Pure about eight months ago from VMware as the chief product officer and you're right, I'm the first our chief product officer at Pure. And at VMware I ran the Cloud management business unit, which was a lot about automation and infrastructure as code. And it's just great to join Pure, which has a phenomenal all flash product set. I kind of call it the iPhone or flash story super easy to use. And how do we take that same ease of use, which is a heart of a Cloud operating principle, and how do we actually take it up to really deliver a modern data experience, which includes infrastructure and storage as code, but then even more beyond that and how do you do modern operations and then modern data services. So super excited to be at Pure. And the vision, if you may, at the end of the day, is to provide, leveraging this moderate experience, a connected and effortless experience data experience, which allows customers to ultimately focus on what matters for them, their business, and by really leveraging and managing and winning with their data, because ultimately data is the new oil, if you may, and if you can mine it, get insights from it and really drive a competitive edge in the digital transformation in your head, and that's what be intended to help our customers to. >> So you joined earlier this year kind of, I guess, middle of the pandemic really I'm interested in kind of your first 100 days, what that was like, what key milestones you set and now you're into your second a 100 plus days. How's that all going? What can you share with us in and that's interesting timing because the effects of the pandemic you came in in a kind of post that, so you had experience from VMware and then you had to apply that to the product organization. So tell us about that sort of first a 100 days and the sort of mission now. >> Absolutely, so as we talked about the vision, around the modern data experience, kind of have three components to it, modernizing the infrastructure and really it's kudos to the team out of the work we've been doing, a ton of work in modernizing the infrastructure, I'll briefly talk to that, then modernizing the data, much more than modernizing the operations. I'll talk to that as well. And then of course, down the pike, modernizing data services. So if you think about it from modernizing the infrastructure, if you think about Pure for a minute, Pure is the first company that took flash to mainstream, essentially bringing what we call consumer simplicity to enterprise storage. The manual for the products with the front and back of a business card, that's it, you plug it in, boom, it's up and running, and then you get proactive AI driven support, right? So that was kind of the heart of Pure. Now you think about Pure again, what's unique about Pure has been a lot of our competition, has dealt with flash at the SSD level, hey, because guess what? All this software was built for hard drive. And so if I can treat NAND as a solid state drive SSD, then my software would easily work on it. But with Pure, because we started with flash, we released went straight to the NAND level, and as opposed to kind of the SSD layer, and what that does is it gives you greater efficiency, greater reliability and create a performance compared to an SSD, because you can optimize at the chip level as opposed to at the SSD module level. That's one big advantage that Pure has going for itself. And if you look at the physics, in the industry for a minute, there's recent data put out by Wikibon early this year, effectively showing that by the year 2026, flash on a dollar per terabyte basis, just the economics of the semiconductor versus the hard disk is going to be cheaper than hard disk. So this big inflection point is slowly but surely coming that's going to disrupt the hardest industry, already the high end has been taken over by flash, but hybrid is next and then even the long tail is coming up over there. And so to end to that extent our lead, if you may, the introduction of QLC NAND, QLC NAND powerful competition is barely introducing, we've been at it for a while. We just recently this year in my first a 100 days, we introduced the flasher AC, C40 and C60 drives, which really start to open up our ability to go after the hybrid story market in a big way. It opens up a big new market for us. So great work there by the team,. Also at the heart of it. If you think about it in the NAND side, we have our flash array, which is a scale-up latency centric architecture and FlashBlade which is a scale-out throughput architecture, all operating with NAND. And what that does is it allows us to cover both structured data, unstructured data, tier one apps and tier two apps. So pretty broad data coverage in that journey to the all flash data center, slowly but surely we're heading over there to the all flash data center based on demand economics that we just talked about, and we've done a bunch of releases. And then the team has done a bunch of things around introducing and NVME or fabric, the kind of thing that you expect them to do. A lot of recognition in the industry for the team or from the likes of TrustRadius, Gartner, named FlashRay, the Carton Peer Insights, the customer choice award and primary storage in the MQ. We were the leader. So a lot of kudos and recognition coming to the team as a result, Flash Blade just hit a billion dollars in cumulative revenue, kind of a leader by far in kind of the unstructured data, fast file an object marketplace. And then of course, all the work we're doing around what we say, ESG, environmental, social and governance, around reducing carbon footprint, reducing waste, our whole notion of evergreen and non-disruptive upgrades. We also kind of did a lot of work in that where we actually announced that over 2,700 customers have actually done non-disruptive upgrades over the technology. >> Yeah a lot to unpack there. And a lot of this sometimes you people say, oh, it's the plumbing, but the plumbing is actually very important too. 'Cause we're in a major inflection point, when we went from spinning disk to NAND. And it's all about volumes, you're seeing this all over the industry now, you see your old boss, Pat Gelsinger, is dealing with this at Intel. And it's all about consumer volumes in my view anyway, because thanks to Steve Jobs, NAND volumes are enormous and what two hard disk drive makers left in the planet. I don't know, maybe there's two and a half, but so those volumes drive costs down. And so you're on that curve and you can debate as to when it's going to happen, but it's not an if it's a when. Let me, shift gears a little bit. Because Cloud, as I was saying, it's ushered in this API economy, this as a service model, a lot of infrastructure companies have responded. How are you thinking at Pure about the as a service model for your customers? What's the strategy? How is it evolving and how does it differentiate from the competition? >> Absolutely, a great question. It's kind of segues into the second part of the moderate experience, which is how do you modernize the operations? And that's where automation as a service, because ultimately, the Cloud has validated and the address of this model, right? People are looking for outcomes. They care less about how you get there. They just want the outcome. And the as a service model actually delivers these outcomes. And this whole notion of infrastructure as code is kind of the start of it. Imagine if my infrastructure for a developer is just a line of code, in a Git repository in a program that goes through a CICD process and automatically kind of is configured and set up, fits in with the Terraform, the Ansibles, all that different automation frameworks. And so what we've done is we've gone down the path of really building out what I think is modern operations with this ability to have storage as code, disability, in addition modern operations is not just storage scored, but also we've got recently introduced some comprehensive ransomware protection, that's part of modern operations. There's all the threat you hear in the news or ransomware. We introduced what we call safe mode snapshots that allow you to recover in literally seconds. When you have a ransomware attack, we also have in the modern operations Pure one, which is maybe the leader in AI driven support to prevent downtime. We actually call you 80% of the time and fix the problems without you knowing about it. That's what modern operations is all about. And then also Martin operations says, okay, you've got flash on your on-prem side, but even maybe using flash in the public Cloud, how can I have seamless multi-Cloud experience in our Cloud block store we've introduced around Amazon, AWS and Azure allows one to do that. And then finally, for modern applications, if you think about it, this whole notion of infrastructure's code, as a service, software driven storage, the Kubernetes infrastructure enables one to really deliver a great automation framework that enables to reduce the labor required to manage the storage infrastructure and deliver it as code. And we have, kudos to Charlie and the Pure storage team before my time with the acquisition of Portworx, Portworx today is truly delivers true storage as code orchestrated entirely through Kubernetes and in a multi-Cloud hybrid situation. So it can run on EKS, GKE, OpenShift rancher, Tansu, recently announced as the leader by giggle home for enterprise Kubernetes storage. We were really proud about that asset. And then finally, the last piece are Pure as a service. That's also all outcome oriented, SLS. What matters is you sign up for SLS, and then you get those SLS, very different from our competition, right? Our competition tends to be a lot more around financial engineering, hey, you can buy it OPEX versus CapEx. And, but you get the same thing with a lot of professional services, we've really got, I'd say a couple of years and lead on, actually delivering and managing with SRE engineers for the SLA. So a lot of great work there. We recently also introduced Cisco FlashStack, again, flash stack as a service, again, as a service, a validation of that. And then finally, we also recently did a announcement with Aquaponics, with their bare metal as a service where we are a key part of their bare metal as a service offering, again, pushing the kind of the added service strategy. So yes, big for us, that's where the buck is skating, half the enterprises, even on prem, wanting to consume things in the Cloud operating model. And so that's where we're putting it lot. >> I see, so your contention is, it's not just this CapEx to OPEX, that's kind of the, during the economic downturn of 2007, 2008, the economic crisis, that was the big thing for CFOs. So that's kind of yesterday's news. What you're saying is you're creating a Cloud, like operating model, as I was saying upfront, irrespective of physical location. And I see that as your challenge, the industry's challenge, be, if I'm going to effect the digital transformation, I don't want to deal with the Cloud primitives. I want you to hide the underlying complexity of that Cloud. I want to deal with higher level problems, but so that brings me to digital transformation, which is kind of the now initiative, or I even sometimes call it the mandate. There's not a one size fits all for digital transformation, but I'm interested in your thoughts on the must take steps, universal steps that everybody needs to think about in a digital transformation journey. >> Yeah, so ultimately the digital transformation is all about how companies are gain a competitive edge in this new digital world or that the company are, and the competition are changing the game on, right? So you want to make sure that you can rapidly try new things, fail fast, innovate and invest, but speed is of the essence, agility and the Cloud operating model enables that agility. And so what we're also doing is not only are we driving agility in a multicloud kind of data, infrastructure, data operation fashion, but we also taking it a step further. We were also on the journey to deliver modern data services. Imagine on a Pure on-prem infrastructure, along with your different public Clouds that you're working on with the Kubernetes infrastructures, you could, with a few clicks run Kakfa as a service, TensorFlow as a service, Mongo as a service. So me as a technology team can truly become a service provider and not just an on-prem service provider, but a multi-Cloud service provider. Such that these services can be used to analyze the data that you have, not only your data, your partner data, third party public data, and how you can marry those different data sets, analyze it to deliver new insights that ultimately give you a competitive edge in the digital transformation. So you can see data plays a big role there. The data is what generates those insights. Your ability to match that data with partner data, public data, your data, the analysis on it services ready to go, as you get the digital, as you can do the insights. You can really start to separate yourself from your competition and get on the leaderboard a decade from now when this digital transformation settles down. >> All right, so bring us home, Ajay, summarize what does a modern data strategy look like and how does it fit into a digital business or a digital organization? >> So look, at the end of the day, data and analysis, both of them play a big role in the digital transformation. And it really comes down to how do I leverage this data, my data, partner data, public data, to really get that edge. And that links back to a vision. How do we provide that connected and effortless, modern data experience that allows our customers to focus on their business? How do I get the edge in the digital transformation? But easily leveraging, managing and winning with their data. And that's the heart of where Pure is headed. >> Ajay Singh, thanks so much for coming inside theCube and sharing your vision. >> Thank you, Dave, it was a real pleasure. >> And thank you for watching this Cube conversation. This is Dave Vellante and we'll see you next time. (upbeat music)

(bright upbeat music) >> Hey, welcome to theCUBE's coverage of Pure Accelerate 2021. I'm Lisa Martin, please stay welcoming back one of our alumni Murli Thirumale is here, the VP & GM of the Cloud Native Business Unit at Pure Storage, Murli, welcome back. >> Lisa, it's great to be back at theCUBE, looking forward to discussion. >> Likewise, so it's been about six months or so since the Portworx acquisition by Pure Storage, give us a lay of the land, what's been going on? What are some of the successes, early wins, and some of the lessons that you've learned? >> Yeah, this is my third time being in Cloud, being a serial entrepreneur. So I've seen this movie before, and I have to say that this is really a lot of good anticipation followed by actually a lot of good stuff that has happened since, so it's been really a great ride so far. And when, let me start with the beginning, what the fundamental goal of the acquisition were, right? The couple of major goals, and then I can talk about how that integration is going. Really, I think from our viewpoint, from the Portworx viewpoint, the goal of the acquisition, from our view, was really to help turbocharge in our growth, we had really a very, very good product that was well accepted and established at customers, doing well as far as industry acceptance was concerned. And frankly, we had some great reference customers and some great installs expanding pretty well. Our issue was really how fast can we turbocharge that growth because as everybody knows, for a startup, the expensive part of an expansion is really on the go-to-market and sales side. And frankly, the timing for this was critical for us because the market had moved from the Kubernetes' market, has moved from sort of the innovator stage to the early majority stage. So from the Pure side, I think this made a lot of sense for them, because they have been looking for how they can expand their subscription models, how they can move to add more value from the array based business that there really have been a wonderful disruptor and to add more value up the stack, and that was the premise of the acquisition. One of the things that I paid a lot of attention to, as anybody does in acquisitions, is not just the strategy but really to understand if there was a culture fit between the teams, because a lot of the times acquisitions don't work because of the poor culture fit. So now let me kind of fast forward little bit and say, "Hey, what we know looking back in about six to eight months into it, how has it turning out so far?" And things have been just absolutely wonderful. Let me actually start with the culture fit, because that often is ignored and is one of the most important parts, right? The resonance in the culture between the two companies is just off the charts, right? It actually starts with what I would call a dramatic kind of customer first orientation, it's something we always had at Portworx. I always used to tell our customers with a startup you end up kind of, you buy the product, but you get the team, right? That's what happens with early stage startups, but Pure is sort of the same way, they are very focused on customer. So the customer focus is a very very useful thing that pulls us together. The second thing that's been really heartwarming to see has been really the focus on product excellence. Pure made it's dramatic entry into the market using Flash, and being the best Flash-based solution, and now they've expanded into many, many different areas. And Portworx also had a focus on product excellence, and so that has kind of moved the needle forward for both of us. And then I think the third thing is really a focus on the team winning, and not just an individual, right? And look, in these COVID times, this has been a tough year for everybody, I think it's, to some extent, even as we onboard new people, it's the culture of the team, the ability to bring new people onboard, and buy the culture, and make progress, all of that is really a function of how well the team is, 'we' is greater than 'me' type of a model, and I think that both these three values of customer first, high focus on product excellence, and the value in the team, including the resellers and the customers as part of the team, has really been the cornerstone, I think, of our success in the integration. >> That's outstanding because, like you said, this is not your first rodeo launching, coming out of stealth and launching and getting acquired, but doing so during one of the most challenging times in the last 100 years in our history while aligning cultures, I think that says a lot about the leadership on the Portworx side and the Pure side. >> I have to say, right? This is one of those amazing things, many people now that having been acquired can say this, really, most of the diligence, the transactions, all of that were done over Zoom, right? So, and then of course, everything since then is we're still in Zoom paradise. And so I think it really is a testament to the modern tools and stuff that we have that enable that. Now, let me talk a little bit about the content of what has happened, right? So strategically, I think the three areas that I think we've had huge synergy and seeing the benefits are first and foremost on the product side. A little later, I'd like to talk a little bit about some of the announcements we're making, but essentially, Pure had this outstanding core storage infrastructure product, well-known in the industry, very much Flash-oriented, part of the whole all Flash era now. And Portworx really came in with the idea of driving Kubernetes and Cloud Native workloads, which are really the majority of modern workloads. And what we found since then is that the integration of having really a more complete stack, which is really centered around what used to be an IT infrastructure of purchase, and what is in fact, for Kubernetes, a more DevOps oriented purchase. And that kind of a combination of being able to provide that combo in one package is something that we've been working very hard on in the last six months. And I'll mention some of the announcements, but we have a number of integrations with FlashArray and FlashBlade and other Pure products that we're able to highlight. So product integration for sure has been an area of some focus, but against a lot of progress. The second one is really customer synergy. I kind of described to our team when we got acquired, I said it's, for us, it's, being acquired by Pure is like strapping a rocket ship to ourselves as a small company, because we now have access to a huge customer footprint. Pure has over 8,000 customers, hugely amazingly high, almost unbelievable NPS score with customers, one of the best in the IT industry. And I think we are finding that with the deployment of containers becoming more ubiquitous, right? 80, 90% of customers in the enterprise are adopting Kubernetes and Containers. And therefore these 8,000 customers are a big huge target, they got a big target sign for both of us to be able to leverage. And so we've had a number of things that we're doing to address and use the Pure sales team to get access to them. The Pure channel of course is also part of that, Pure is 100% channel organization, which is great. So I think the synergy on the customer side with being able to have a solution that works for infrastructure and for DevOps has been a big area. In this day and age, Kubernetes is an area, for many of your listeners who are very, very familiar with Kubernetes, customers struggle, not just with day zero, but day one, day two, day three, right? It's how do you put it in production. And support, and integrating, and the use of Kubernetes and containers, putting that stack together is a big area. So support is a big area of pain for customers, and it's an area that, again, for a Portworx viewpoint, now we've expanded our footprint with a great support organization that we can bring to bear 24 by seven around the globe. Portworx is running on a lot of mission critical applications in big industries like finance and retail, and these types of things, really, support is a big area. And then the last thing I will just say is the use cases are usually synergistic, right? And we'll talk a little bit more about use cases as we go along here, but really there's legacy apps, right? In an interesting way, there's 80% of, IT spending is still on legacy apps, if you will, in that stack. However, 80% of all the new applications are being deployed on this modern app stack, right? >> Right. >> With all these open-source type of products and technologies. And most of that stack, most of the modern app stack is containerized. The 80, 85% of those applications really are where customers have chosen containers and Kubernetes as the as the mechanism to deliver those apps. And therefore Pure products like FlashBlade were very, very focused with fast recovery for these kind of modern apps, which are the stack of AI, and personalization, and all the modern digital apps. And I think those things can align well with the Portworx offering. So really around the areas of culture, customers, product synergy, support, and finally use cases, are all kind of been areas of huge progress for us. >> It also seems to me that the Portworx acquisition gives Pure a foray, a new buying center with respect to DevOps, talk to me a little bit about that as an opportunity for Pure. >> Yeah, the modern world is one where the enterprise itself has segmented into whole lot of new areas of spending and infrastructure ownership, right? And in the old days it used to be the network, storage, compute, and apps, sort of the old model of the world. And of course the app model has moved on, and then certainly there's a lot of different ways, web apps, the three tier apps, and the web apps, and so on. But the infrastructure world has morphed really into a bunch of other sub-segments, and some of it is still traditional hardware, but then even that is being cloudified, right? Because a lot of companies like Pure have taken their hardware array offerings and are offering that as a cloud-like offering where you can purchase it as a service, and in fact, Pure is offering a set of solutions called Evergreen that allow you to not even, you're just under subscription, you get your hardware refresh bundled in, very, very innovative. So you have now new buying centers coming in, in addition to the old traditional IT, there is sort of this whole, what used to be in the old ways called middleware, now has kind of morphed into this DevSecOps set of folks, right? Which is DevOps it's ITOps, and even security is a big part of that, the CISO Organization has that kind of segment. And so these buying centers often have new budgets, right? It turns out that, for example, to contrast, the Portworx budget really comes from entirely different budget, right? Our top two budget sources are usually CIO initiatives, they're not from the traditional storage budget, it comes from things like move to cloud or business transformation. And those set of folks, that set of customers, is really born in a different era, so to speak. You know, Lisa, they come, and I come from the old world, so I would say that I'm kind of more of an oldie, hopefully a Goldie, but an oldie. These folks are born in the post-DevOps, post-cloud, post-open-source world, right? They are used to brand new tools, get-ops, the way that everything's run on the cloud, it's on demand. So what we bring to Pure is really the ability to take their initiatives, which were around infrastructure, and cloudifying infrastructure to now adding two layers on top of that, right? So what Portworx adds to Pure is the access to the new automation layer of middleware. Kubernetes is nothing but really an automation of model for containers and for infrastructure now. And then the third layer is on top of us, is what I would call SaaS, the SaaSified layer, and as a service layer. And so we bring the opportunity to get those SaaS-like budgets, the DevOps budgets, and the DevOps and the SaaS kind of buyers, and together the business has very different models to it. In addition to not just a different technologies, the buying behavior is different, it's based on a consumption model, it's a subscription business. So it really is a change for new budgets, new buyers, and new financial models, which is a subscription model, which as you know, is valued much more highly by Wall Street nowadays compared to say some of the older hardware models. >> Well, Murli, when we talk about storage, we talk about data or the modern data experience. The more and more data that's being produced, the more value potentially there is for organizations, I think we saw, we learned several lessons in the last year, and one of them is that being able to glean insights from data in real-time or near real-time is, for many businesses, no longer a nice to have, it's really table stakes, it was for survival of getting through COVID, it is now in terms of identification of new business models, but it elevates the data conversation up to the C-suite, the board going, "Is our data protected? Is it secure? Can we access it?" And, "How do we deliver a modern data experience to our customers and to our internal employees?" So with that modern data experience, and maybe the elevation about conversation lengths, talk to me about some of the things that you're announcing at Accelerate with respect to Portworx. >> Yeah, so there are two sets of announcements. To be honest actually, this is a pretty exciting time for us, we're in theCUBE Cone time and the Accelerate time. And so let me kind of draw a circle around both those sets of announcements, if you will, right? So let's start perhaps with just the sets of things that we are announcing at Accelerate, right? This is kind of the first things that are coming up right now. And I'll tell you, there are some very, very exciting things that we're doing. So the majority of the announcements are centered around a release that we have called 2.8, so Portworx says, "We've been in the market now for well over five years with the product that really has been well deployed in very large global 2K enterprises." So the three or four major announcements, one of them is what I was talking about earlier, the integration of true Kubernetes applications running on Pure Storage. So we have a Cloud Native, a Native implementation of Portworx running on FlashArray and FlashBlade, where essentially when users now provision a container volume to Portworx, the storage volumes are magically created on FlashArray and FlashBlade, right? It's the idea of, without having to interface, so a DevOps engineer can deploy storage as code by provisioning volumes using Kubernetes without having to go issue a trouble ticket or a service ticket for a PureArray. And Portworx essentially access a layer between Kubernetes and the PureArray, and we allow configuration of volumes on the storage volumes of the PureArray directly. So essentially now on FlashArray, these volumes now receive the full suite of Portworx Storage Management features, including Kubernetes DR, backup, security, auto scaling, and migration. So that is a first version of this integration, right? The second one, it's, I am, is a personal favorite of mine, it's very, very exciting, right? When we came into Pure, we discovered that Pure already had this software solution called Pure as a service, it was essentially a Pure1 service that allowed for continuous call home, and log and diagnostic information, really an awesome window for customers to be able to see what their array utilization is like, complete observability, end-to-end on capacity, what's coming up, and allowed for proactive addressing of outages, or issues, or being able to kind of see it before it happen. The good news now is Portworx is integrated with Pure1, and so now customers have a unified observability stack for their Kubernetes applications using Portworx and FlashArray and FlashBlade in the Pure1 portal. So we are in the Pure1 portal now really providing end-to-end troubleshooting of issues and deployment, so very, very exciting, something that I think is a major step forward, right? >> Absolutely, well that single pane of glass is critical for management, so many companies waste a lot of time and resources managing disparate disconnected systems. And again, the last year has taught us so many businesses, there wasn't time, because there's going to be somebody right behind you that's going to be faster and more nimble, and has that single pane of glass unified view to be able to make better decisions. Last question, really, before we wrap here. >> Yeah. >> I can hear your momentum, I can feel your momentum through Zoom here. Talk to me about what's next, 'cause I know that when the acquisition happened about, we said six months or so ago, you said, "This is a small step in the Portworx journey." So what's ahead? >> Lisa, great question. I can state 10 things, but let me kind of step up a little bit at the 10,000 foot level, right? In one sense, I think no company gets to declare victory in this ongoing battle and we're just getting started. But if I had to kind of say, "What are some of the major teams that we have been part of and have been able to make happen in addition to take advantage of?" Pure obviously took advantage of the Flash wave, and they moved to all Flash, that's been a major disruptor with Pure being the lead. For Portworx, it has been really the move to containers and data management in an automated form, right? Kubernetes has become sort of not just a container orchestrator looking North, but looking southbound, is orchestrating infrastructure, we are in the throws of that revolution. But if you think about it, the other thing that's happening is all of this is in the service of, if you're a CIO, you're in the service of lines of businesses asking for a way to run their applications in a multicloud way, run their applications faster. And that is really the, as a service revolution, and it feels a little silly to almost talk about it as a service in that it's this late in the Cloud era, but the reality is that's just beginning, right? As a service revolution dramatically changed the IaaS business, the infrastructure business. But if you look at it, data services as a, data as a service is something that is what our customers are doing, so our customers are taking Pure hardware, Portworx software, and then they are building them into a platform as a service, things like databases as a service. And what we are doing, you will see some announcements from us in the second half of this year, terribly exciting, I just can't wait for it, where we're going to be actually moving forward to allow our customers to more quickly get to data services at the push of a button, so to speak, right? So- >> Excellent. >> The idea of database as a service to offer messaging as a service, search as a service, streaming as a service, and then finally some ML kind of AI as a service, these five categories of data services are what you should be expecting to see from Portworx and Pure going forward in the next half. >> Big potential there to really kick the door wide open on the total adjustable market. Well, Murli, it's been great to have you on the program, I can't wait to have you on next 'cause I know that there's so much more, like I said, I can feel your momentum through our virtual experience here. Thank you so much for joining us and giving us the lay of the land of what's been happening with the Portworx acquisition and all of the momentum and excitement that is about to come, we appreciate your time. >> Thank you, Lisa. Cheers to a great reduced COVID second half of the year. >> Oh, cheers to that. >> Yeah cheers, thanks. >> From Murli Thirumale, I'm Lisa Martin, you're watching theCUBE's coverage of Pure Accelerate. (bright upbeat music)

>> Announcer: From around the globe it's theCUBE. Presenting the Convergence of File and Object brought to you by Pure Storage. >> Okay now we're going to get the customer perspective on object and we'll talk about the convergence of file and object, but really focusing on the object pieces this is a content program that's being made possible by Pure Storage and it's co-created with theCUBE. Christopher CB Bohn is here. He's a lead architect for MicroFocus the enterprise data warehouse and principal data engineer at MicroFocus. CB welcome good to see you. >> Thanks Dave good to be here. >> So tell us more about your role at Microfocus it's a pan Microfocus role because we know the company is a multi-national software firm it acquired the software assets of HP of course including Vertica tell us where you fit. >> Yeah so Microfocus is you know, it's like I can says it's wide, worldwide company that it sells a lot of software products all over the place to governments and so forth. And it also grows often by acquiring other companies. So there is there the problem of integrating new companies and their data. And so what's happened over the years is that they've had a number of different discreet data systems so you've got this data spread all over the place and they've never been able to get a full complete introspection on the entire business because of that. So my role was come in, design a central data repository and an enterprise data warehouse, that all reporting could be generated against. And so that's what we're doing and we selected Vertica as the EDW system and Pure Storage FlashBlade as the communal repository. >> Okay so you obviously had experience with with Vertica in your previous role, so it's not like you were starting from scratch, but paint a picture of what life was like before you embarked on this sort of consolidated approach to your data warehouse. Was it just dispared data all over the place? A lot of M and A going on, where did the data live? >> CB: So >> Right so again the data is all over the place including under people's desks and just dedicated you know their own private SQL servers, It, a lot of data in a Microfocus is one on SQL server, which has pros and cons. Cause that's a great transactional database but it's not really good for analytics in my opinion. So but a lot of stuff was running on that, they had one Vertica instance that was doing some select reporting. Wasn't a very powerful system and it was what they call Vertica enterprise mode where it had dedicated nodes which had the compute and storage in the same locus on each server okay. So Vertica Eon mode is a whole new world because it separates compute from storage. Okay and at first was implemented in AWS so that you could spin up you know different numbers of compute nodes and they all share the same communal storage. But there has been a demand for that kind of capability, but in an on-prem situation. Okay so Pure storage was the first vendor to come along and have an S3 emulation that was actually workable. And so Vertica worked with Pure Storage to make that all happen and that's what we're using. >> Yeah I know back when back from where we used to do face-to-face, we would be at you know Pure Accelerate, Vertica was always there it stopped by the booth, see what they're doing so tight integration there. And you mentioned Eon mode and the ability to scale, storage and compute independently. And so and I think Vertica is the only one I know they were the first, I'm not sure anybody else does that both for cloud and on-prem, but so how are you using Eon mode, are you both in AWS and on-prem are you exclusively cloud? Maybe you could describe that a little bit. >> Right so there's a number of internal rules at Microfocus that you know there's, it's not AWS is not approved for their business processes. At least not all of them, they really wanted to be on-prem and all the transactional systems are on-prem. And so we wanted to have the analytics OLAP stuff close to the OLTP stuff right? So that's why they called there, co-located very close to each other. And so we could, what's nice about this situation is that these S3 objects, it's an S3 object store on the Pure Flash Blade. We could copy those over if we needed it to AWS and we could spin up a version of Vertica there, and keep going. It's like a tertiary GR strategy cause we actually have a, we're setting up a second, Flash Blade Vertica system geo located elsewhere for backup and we can get into it if you want to talk about how the latest version of the Pure software for the Flash Blade allows synchronization across network boundaries of those Flash Blade which is really nice because if, you know there's a giant sinkhole opens up under our Koll of facility and we lose that thing then we just have to switch to DNS. And we were back in business of the DR. And then the third one was to go, we could copy those objects over to AWS and be up and running there. So we're feeling pretty confident about being able to weather whatever comes along. >> Yeah I'm actually very interested in that conversation but before we go there. you mentioned you want, you're going to have the old lab close to the OLTP, was that for latency reasons, data movement reasons, security, all of the above. >> Yeah it's really all of the above because you know we are operating under the same sub-net. So to gain access to that data, you know you'd have to be within that VPN environment. We didn't want to going out over the public internet. Okay so and just for latency reasons also, you know we have a lot of data and we're continually doing ETL processes into Vertica from our production data, transactional databases. >> Right so they got to be approximate. So I'm interested in so you're using the Pure Flash Blade as an object store, most people think, oh object simple but slow. Not the case for you is that right? >> Not the case at all >> Why is that. >> This thing had hoop It's ripping, well you have to understand about Vertica and the way it stores data. It stores data in what they call storage containers. And those are immutable, okay on disc whether it's on AWS or if you had a enterprise mode Vertica, if you do an update or delete it actually has to go and retrieve that object container from disc and it destroys it and rebuilds it, okay which is why you don't, you want to avoid updates and deletes with vertica because the way it gets its speed is by sorting and ordering and encoding the data on disk. So it can read it really fast. But if you do an operation where you're deleting or updating a record in the middle of that, then you've got to rebuild that entire thing. So that actually matches up really well with S3 object storage because it's kind of the same way, it gets destroyed and rebuilt too okay. So that matches up very well with Vertica and we were able to design the system so that it's a panda only. Now we have some reports that we're running in SQL server. Okay which we're taking seven days. So we moved that to Vertica from SQL server and we rewrote the queries, which were had, which had been written in TC SQL with a bunch of loops and so forth and we were to get, this is amazing it went from seven days to two seconds, to generate this report. Which has tremendous value to the company because it would have to have this long cycle of seven days to get a new introspection in what they call the knowledge base. And now all of a sudden it's almost on demand two seconds to generate it. That's great and that's because of the way the data is stored. And the S3 you asked about, oh you know it, it's slow, well not in that context. Because what happens really with Vertica Eon mode is that it can, they have, when you set up your compute nodes, they have local storage also which is called the depot. It's kind of a cache okay. So the data will be drawn from the Flash Blade and cached locally. And that was, it was thought when they designed that, oh you know it's that'll cut down on the latency. Okay but it turns out that if you have your compute nodes close meaning minimal hops to the Flash Blade that you can actually tell Vertica, you know don't even bother caching that stuff just read it directly on the fly from the from the Flash Blade and the performance is still really good. It depends on your situation. But I know for example a major telecom company that uses the same topologies we're talking about here they did the same thing. They just dropped the cache cause the Flash Blade was able to deliver the data fast enough. >> So that's, you're talking about that's speed of light issues and just the overhead of switching infrastructure is that, it's eliminated and so as a result you can go directly to the storage array? >> That's correct yeah, it's like, it's fast enough that it's almost as if it's local to the compute node. But every situation is different depending on your needs. If you've got like a few tables that are heavily used, then yeah put them in the cache because that'll be probably a little bit faster. But if you're have a lot of ad hoc queries that are going on, you know you may exceed the storage of the local cache and then you're better off having it just read directly from the, from the Flash Blade. >> Got it so it's >> Okay. >> It's an append only approach. So you're not >> Right >> Overwriting on a record, so but then what you have automatically re index and that's the intelligence of the system. how does that work? >> Oh this is where we did a little bit of magic. There's not really anything like magic but I'll tell you what it is I mean. ( Dave laughing) Vertica does not have indexes. They don't exist. Instead I told you earlier that it gets a speed by sorting and encoding the data on disk and ordering it right. So when you've got an append-only situation, the natural question is well if I have a unique record, with let's say ID one, two, three, what happens if I append a new version of that, what happens? Well the way Vertica operates is that there's a thing called a projection which is actually like a materialized columnar data store. And you can have a, what they call a top-K projection, which says only put in this projection the records that meet a certain condition. So there's a field that we like to call a discriminator field which is like okay usually it's the latest update timestamp. So let's say we have record one, two, three and it had yesterday's date and that's the latest version. Now a new version comes in. When the data at load time vertical looks at that and then it looks in the projection and says does this exist already? If it doesn't then it adds it. If it does then that one now goes into that projection okay. And so what you end up having is a projection that is the latest snapshot of the data, which would be like, oh that's the reality of what the table is today okay. But inherent in that is that you now have a table that has all the change history of those records, which is awesome. >> Yeah. >> Because, you often want to go back and revisit, you know what it will happen to you. >> But that materialized view is the most current and the system knows that at least can (murmuring). >> Right so we then create views that draw off from that projection so that our users don't have to worry about any of that. They just get oh and say select from this view and they're getting the latest greatest snapshot of what the reality of the data is right now. But if they want to go back and say, well how did this data look two days ago? That's an easy query for them to do also. So they get the best of both worlds. >> So could you just plug any flash array into your system and achieve the same results or is there anything really unique about Pure? >> Yeah well they're the only ones that have got I think really dialed in the S3 object form because I don't think AWS actually publishes every last detail of that S3 spec. Okay so it had, there's a certain amount of reverse engineering they had to do I think. But they got it right. When we've, a couple maybe a year and a half ago or so there they were like at 99%, but now they worked with Vertica people to make sure that that object format was true to what it should be. So that it works just as if Vertica doesn't care, if it is on AWS or if it's on Pure Flash Blade because Pure did a really good job of dialing in that format and so Vertica doesn't care. It just knows S3, doesn't know what it doesn't care where it's going it just works. >> So the essentially vendor R and D abstracted that complexity so you didn't have to rewrite the application is that right? >> Right, so you know when Vertica ships it's software, you don't get a specific version for Pure or AWS, it's all in one package, and then when you configure it, it knows oh okay well, I'm just pointed at the, you know this port, on the Pure storage Flash Blade, and it just works. >> CB what's your data team look like? How is it evolving? You know a lot of customers I talked to they complain that they struggled to get value out of the data and they don't have the expertise, what does your team look like? How is it, is it changing or did the pandemic change things at all? I wonder if you could bring us up to date on that? >> Yeah but in some ways Microfocus has an advantage in that it's such a widely dispersed across the world company you know it's headquartered in the UK, but I deal with people I'm in the Bay Area, we have people in Mexico, Romania, India. >> Okay enough >> All over the place yeah all over the place. So when this started, it was actually a bigger project it got scaled back, it was almost to the point where it was going to be cut. Okay, but then we said, well let's try to do almost a skunkworks type of thing with reduced staff. And so we're just like a hand. You could count the number of key people on this on one hand. But we got it all together, and it's been a traumatic transformation for the company. Now there's, it's one approval and admiration from the highest echelons of this company that, hey this is really providing value. And the company is starting to get views into their business that they didn't have before. >> That's awesome, I mean, I've watched Microfocus for years. So to me they've always had a, their part of their DNA is private equity I mean they're sharp investors, they do great M and A >> CB: Yeah >> They know how to drive value and they're doing modern M and A, you know, we've seen what they what wait, what they did with SUSE, obviously driving value out of Vertica, they've got a really, some sharp financial people there. So that's they must have loved the the Skunkworks, fast ROI you know, small denominator, big numerator. (laughing) >> Well I think that in this case, smaller is better when you're doing development. You know it's a two-minute cooks type of thing and if you've got people who know what they're doing, you know I've got a lot of experience with Vertica, I've been on the advisory board for Vertica for a long time. >> Right And you know I was able to learn from people who had already, we're like the second or third company to do a Pure Flash Blade Vertica installation, but some of the best companies after they've already done it we are members of the advisory board also. So I learned from the best, and we were able to get this thing up and running quickly and we've got you know, a lot of other, you know handful of other key people who know how to write SQL and so forth to get this up and running quickly. >> Yeah so I mean, look it Pure is a fit I mean I sound like a fan boy, but Pure is all about simplicity, so is object. So that means you don't have to ra, you know worry about wrangling storage and worrying about LANs and all that other nonsense and file names but >> I have burned by hardware in the past you know, where oh okay they built into a price and so they cheap out on stuff like fans or other things in these components fail and the whole thing goes down, but this hardware is super good quality. And so I'm happy with the quality of that we're getting. >> So CB last question. What's next for you? Where do you want to take this initiative? >> Well we are in the process now of, we're when, so I designed a system to combine the best of the Kimball approach to data warehousing and the inland approach okay. And what we do is we bring over all the data we've got and we put it into a pristine staging layer. Okay like I said it's a, because it's append-only, it's essentially a log of all the transactions that are happening in this company, just as they appear okay. And then from the Kimball side of things we're designing the data marts now. So that's what the end users actually interact with. So we're taking the, we're examining the transactional systems to say, how are these business objects created? What's the logic there and we're recreating those logical models in Vertica. So we've done a handful of them so far, and it's working out really well. So going forward we've got a lot of work to do, to create just about every object that the company needs. >> CB you're an awesome guest really always a pleasure talking to you and >> Thank you. >> congratulations and good luck going forward stay safe. >> Thank you, you too Dave. >> All right thank you. And thank you for watching the Convergence of File and Object. This is Dave Vellante for theCUBE. (soft music)

>> Thank you, sir. All right, you ready to roll? >> Ready. >> All right, we'll go ahead and go in five, four, three, two. >> Okay, let's summarize the convergence of file and object. First, I want to thank our guests, Matt Burr, Scott Sinclair, Garrett Belsner, and CB Bonne. I'm your host, Dave Vellante, and please allow me to briefly share some of the key takeaways from today's program. So first, as Scott Sinclair of ESG stated surprise, surprise, data's growing. And Matt Burr, he helped us understand the growth of unstructured data. I mean, estimates indicate that the vast majority of data will be considered unstructured by mid decade, 80% or so. And obviously, unstructured data is growing very, very rapidly. Now, of course, your definition of unstructured data, now that may vary across a wide spectrum. I mean, there's video, there's audio, there's documents, there's spreadsheets, there's chat. I mean, these are generally considered unstructured data but of course they all have some type of structure to them. You know, perhaps it's not as strict as a relational database, but there's certainly metadata and certain structure to these types of use cases that I just mentioned. Now, the key to what Pure is promoting is this idea of unified fast file and object, U-F-F-O. Look, object is great, it's inexpensive, it's simple, but historically, it's been less performant, so good for archiving, or cheap and deep types of examples. Organizations often use file for higher performance workloads and let's face it, most of the world's data lives in file formats. What Pure is doing is bringing together file and object by, for example, supporting multiple protocols, ie, NFS, SMB, and S3. S3, of course, has really given a new life to object over the past decade. Now, the key here is to essentially enable customers to have the best of both worlds, not having to trade off performance for object simplicity. And a key discussion point that we've had in the program has been the impact of Flash on the long, slow, death of spinning disk. Look, hard disk drives, they had a great run, but HDD volumes, they peaked in 2010, and Flash, as you well know, has seen tremendous volume growth thanks to the consumption of Flash in mobile devices and then of course, its application into the enterprise. And as volume is just going to keep growing and growing, and growing. the price declines of Flash are coming down faster than those of HDD. So it's, the writing's on the wall. It's just a matter of time. So Flash is riding down that cost curve very, very aggressively and HDD has essentially become a managed decline business. Now, by bringing Flash to object as part of the FlashBlade portfolio and allowing for multiple protocols, Pure hopes to eliminate the dissonance between file and object and simplify the choice. In other words, let the workload decide. If you have data in a file format, no problem. Pure can still bring the benefits of simplicity of object at scale to the table. So again, let the workload inform what the right strategy is not the technical infrastructure. Now Pure, of course, is not alone. There are others supporting this multi-protocol strategy. And so we asked Matt Burr why Pure, what's so special about you? And not surprisingly, in addition to the product innovation, he went right to Pure's business model advantages. I mean, for example, with its Evergreen support model which was very disruptive in the marketplace. You know, frankly, Pure's entire business disrupted the traditional disk array model which was, fundamentally, it was flawed. Pure forced the industry to respond. And when it achieved escape velocity and Pure went public, the entire industry had to react. And a big part of the Pure value prop in addition to this business model innovation that we just discussed is simplicity. Pure's keep it simple approach coincided perfectly with the ascendancy of cloud where technology organizations needed cloud-like simplicity for certain workloads that were never going to move into the cloud. They were going to stay on-prem. Now I'm going to come back to this but allow me to bring in another concept that Garrett and CB really highlighted, and that is the complexity of the data pipeline. And what do I mean, what do I mean by that, and why is this important? So Scott Sinclair articulated or he implied that the big challenge is organizations, they're data full, but insights are scarce; a lot of data, not as much insights, and it takes time, too much time to get to those insights. So we heard from our guests that the complexity of the data pipeline was a barrier to getting to faster insights. Now, CB Bonne shared how he streamlined his data architecture using Vertica's Eon Mode which allowed him to scale, compute, independently of storage, so that brought critical flexibility and improved economics at scale. And FlashBlade, of course, was the backend storage for his data warehouse efforts. Now, the reason I think this is so important is that organizations are struggling to get insights from data and the complexity associated with the data pipeline and data lifecycles, let's face it, it's overwhelming organizations. And there, the answer to this problem is a much longer and different discussion than unifying object and file. That's, you know, I could spend all day talking about that, but let's focus narrowly on the part of the issue that is related to file and object. So the situation here is the technology has not been serving the business the way it should. Rather, the formula is twisted in the world of data and big data, and data architectures. The data team is mired in complex technical issues that impact the time to insights. Now, part of the answer is to abstract the underlying infrastructure complexity and create a layer with which the business can interact that accelerates instead of impedes innovation. And unifying file and object is a simple example of this where the business team is not blocked by infrastructure nuance, like does this data reside in the file or object format? Can I get to it quickly and inexpensively in a logical way or is the infrastructure in a stovepipe and blocking me? So if you think about the prevailing sentiment of how the cloud is evolving to incorporate on premises, workloads that are hybrid, and configurations that are working across clouds, and now out to the edge, this idea of an abstraction layer that essentially hides the underlying infrastructure is a trend we're going to see evolve this decade. Now, is UFFO the be-all end-all answer to solving all of our data pipeline challenges? No, no, of course not. But by bringing the simplicity and economics of object together with the ubiquity and performance of file, UFFO makes it a lot easier. It simplifies a life organizations that are evolving into digital businesses, which by the way, is every business. So, we see this as an evolutionary trend that further simplifies the underlying technology infrastructure and does a better job supporting the data flows for organizations so they didn't have to spend so much time worrying about the technology details that add little value to the business. Okay, so thanks for watching the convergence of file and object and thanks to Pure Storage for making this program possible. This is Dave Vellante for theCUBE. We'll see you next time.

>>from >>around the globe. It's the Cube with digital coverage of AWS reinvent 2020. Special coverage sponsored by AWS Global Partner Network. >>Hi. Welcome to the Cube. Virtual and our coverage of AWS reinvent 2020 with special coverage of a PM partner experience. I'm John for your host. We are the Cube. Virtual. We can't be there in person with a remote. And our two next guests are We have pure storage. Michael Slotnick, VP of Worldwide Alliances, Pure storage. And Robert Czarnecki, principal product manager for a U. S. Outposts. Welcome to the Cube. >>Wonderful to be here. Great to see you. And thanks for having us, >>Michael. Great to see you pure. You guys had some great Momenta, um, earnings and some announcements. You guys have some new news? We're here. Reinvent all part of a W s and outpost. I want to get into it right away. Uh, talk about the relationship with AWS. I know you guys have some hot news. Just came out in late November. We're here in the event. All the talk is about new higher level services. Hybrid edge. What do you guys doing? What's the story? >>Yeah, Look, I gotta tell you the partnership with AWS is a very high profile and strategic partnership for pure storage. We've worked hard with our cloud block store for AWS, which is an extensive bility solution for pure flash array and into a W s. I think the big news and one of things that we're most proud of is the recent establishment of pure being service ready and outpost ready. And the first and Onley on Prem storage solution and were shoulder to shoulder with AWS is a W s takes outpost into the data center. Now they're going after key workloads that were well known for. And we're very excited Thio, partner with AWS in that regard, >>you know, congratulations to pure. We've been following you guys from the beginning since inception since it was founded startup. And now I'll see growing public company on the next level kind of growth plan. You guys were early on all this stuff with with with flash with software and cloud. So it's paying off. Rob, I wanna get toe Outpost because this was probably most controversial announcements I've ever covered at reinvent for the past eight years. It really was the first sign that Andy was saying, You know what? We're working backwards from the customers and they all are talking Hybrid. We're gonna have Outpost. Give us the update. What kind of workloads and verticals are seeing Success without post? Now that that's part of the portfolio, How does it all working out? Give us the update on the workloads in the verticals. >>Absolutely. Although I have to say I'd call it more exciting than controversial. We're so excited about the opportunities that outpost opened for our customers. And, you know, customers have been asking us for years. How can we bring AWS services to our data centers? And we thought about it for a long time. And until until we define the outpost service, we really I thought we could do better. And what outpost does it lets us take those services that customers are familiar with? It lets us bring it to their data center and and one of the really bright spots over the past year has just been how many different industries and market segments have shown interest. Outpost right. You could have customers, for example, with data residency needs, those that have to do local data processing. Uh, maybe have Leighton see needs on a specific workload that needs to run near their end users. We're just folks trying to modernize their data center, and that's a journey. That transformation takes time, right? So So Outpost works for all of those customers. And one of the things that's really become clear to us is that to enable the success that we think L Post can have, we need to meet customers where they are. And and one of the fantastic things about the outpost ready program is many of those customers air using pure and they have pure hardware and way. Send an outpost over to the pure lab recently, and I have to tell you a picture of those two racks next to each other looks really good. >>You know, 20 used to kind of welcome back my controversial comments. You know, I meant in the sense of that's when Cloud really got big into the enterprise and you have to deal with hybrid. So I do think it's exciting because the edges a big theme here. Can you just share real quick before I get in some of the pure questions on this edge piece with the hybrid because what what's the customer need? And when you talk to customers, I know you guys, you know, really kind of work backwards from the customer. What are their needs? What causes them to look at Outpost as part of their hybrid? What's the Keith consideration? >>Yeah, so? So there are a couple of different needs. John, right? One, for example, is way have regions and local zones across the globe. But we're not everywhere and and their their data residency regulations that they're becoming increasingly common and popular. So customers I come to us and say, Look, I really need to run, for example, of financial services workload. It needs to be in Thailand, and we don't have a reason or local zone in Thailand. But we could get him an outpost to to places where they need to be right. So the that that requirement to keep data, whether it's by regulation or by a contractual agreement, that's a that's a big driver. The other pieces there's There's a tremendous amount of interest in the that top down executive sponsorship across enterprise customers to transform their operations right to modernize their their digital approach but there, when they actually look a look at their estate, they do see an awful lot of hardware, and that's a hard challenge. Thio Plan the migration when you could bring an outpost right into that data center. It really makes it much easier because AWS is right there. There could be a monolithic architecture that it doesn't lend well toe having part of the workload running in the region, part of the workload running in their data center. But with an outpost, they can extend AWS to their data center, and that just makes it so much easier for them to get started on their digital transformation. >>Michael, this is This is the key trend. You guys saw early Cloud operations on premise. It becomes cloud ified at that point when you have Dev ops on on Premises and then cloud pure cloud for bursting all that stuff. And now you've got the edge exploding as well of growth and opportunity. What causes the customer to get the pure option on outputs? What's the What's the angle for you guys? Obviously storage, you get data and I can see this whole Yeah, there's no region and certainly outpost stores data, and that's a requirement for a lot of, you know, certainly global customers and needs. What's the pure angle on this? >>Yeah, I appreciate that. And appreciate Rob's comments around what AWS sees in the wild in terms of yours footprint in the market share that we've established his company over 11 years in business and, you know, over eight years of shipping product. You know, what I would tell you is one of the things that that a lot of people misses the simplicity and the consistency that air characteristically, you know very much in the AWS experience and equally within the pure experience and that that's really powerful. So as we were successful in putting pure into workloads that, you know, for for all the reasons that Rob talked about right data gravity, you know, the the regulatory issues, you know, just application architecture and its inability to move to the public cloud. Um, you know, our predictability are simplicity. Are consistency really match with the costumers getting with other work clothes that they had in AWS? And so with a W S outposts that's really bringing to the customer that single pane of glass to manage their entire environment. And so we saw that we made the three year investment in Outpost. Is Rob said Just having our solution? Inp Yours Data center. It's set up and running today with a solution built on flash Blade, which is our unstructured data solution and, you know, delivering fantastic performance results in a I and ML workloads. We see the same opportunity within backup and disaster recovery workloads and into analytics and then equally the opportunity toe build. You know, Flash Ray and our other storage solutions, and to build architectures with outposts in our data center and bring them to market >>real quick just to follow up on that. What use cases are you seeing that are most successful without post and in general in general, how do you guys get your customers to integrate with the rest of, uh, their environment? Because you you no one's got. Now this operating environments not just cloud public, is cloud on premise and everything else. >>Yeah, you know what's cool is, and then Rob hit right on. It is the the wide range of industries and the wide range of use cases and workloads that air finding themselves attracted to the outpost offering on DSO. You know, without a doubt there's gonna be, You know, I think what people would immediately believe ai and ml workloads and the importance of having high performance storage and to have a high performance outpost environment, you know, as close to the center as possible of those solutions. But it doesn't stop there. Traditional virtualized database workloads that for reasons of application architecture, aren't candidates to move. AWS is public cloud offering our great fit for outpost and those air workloads that we've always traditionally been successful within the market and see a great opportunity. Thio, you know, build on that success as an outpost partner. >>Rob, I gotta ask, you last reinvent when we're in person. When we had real life back then e was at the replay party and hanging out, and this guy comes out to me. I don't even know who he was. Obviously big time engineer over there opens his hand up and shows me this little processor and I'm like, closes and he's like and I go take a picture and it was like freaking out. Don't take a picture. It was it was the big processor was the big, uh, kind of person. Uh, I think it was the big monster. And it was just so small. See the innovation and hard where you guys have done a lot, there s that's cool. I like get your thoughts on where the future is going there because you've got great hardware innovation, but you got the higher level services with containers. I know you guys took your time. Containers are super important because that's going to deal with that. So how do you look at that? You got the innovation in the hardware check containers. How does that all fit in? Because you guys have been making a lot of investments in some of these cloud native projects. What's your position on that? >>You know, it's all part of one common story, John right customers that they want an easy path to delivering impact for their business. Right. And, you know, you've heard us speak a lot over the past few years about how we're really seeing these two different types of customers. We have those customers that really loved to get those foundational core building blocks and stitch them together in a creative way. But then you have more and more customers that they wanna. They wanna operate at a different level, and and that's okay. We want to support both of them. We want to give both of them all the tools that they need. Thio spend their time and put their resource is towards what differentiates their business and just be able to give them support at whatever level they need on the infrastructure side. And it's fantastic that are combination of investments in hardware and services. And now, with Outpost, we can bring those investments even closer to the customer. If you really think about it that way, the possibilities become limitless. >>Yeah, it's not like the simplicity asked, but it was pretty beautiful to the way it looks. It looks nice. Michael. Gotta ask you on your side. A couple of big announcements over that we've been following from pure looking back. You already had the periods of service announcement you bought the port Works was acquisition. Yeah, that's container management. Across the data center, including outposts you got pure is a service is pure. Is the service working with outpost and how and if so, how and what's the consumption model for customers there. >>Yeah, thanks so much, John. And appreciate you following us the way that you do it. Zits meaningful and appreciate it. Listen, you know, I think the customers have made it clear and in AWS is, you know, kind of led the way in terms of the consumption and experience expectations that customers have. It's got to be consumable. They've got to pay for what they use. It's got to be outcome oriented and and we're doing that with pure is a service. And so I think we saw that early and have invested in pure is a service for our customers. And, you know, we look at the way we acquired outposts as ah customer and a partner of AWS aan dat is exactly the same way customers can consume pure. You know, all of our solutions in a, you know, use what you need, pay for what you use, um, environment. And, you know, one of the exciting things about AWS partnership is its wide ranging and one of the things that AWS has done, I think world class is marketplace. And so we're excited to share with this audience, you know, really? On the back of just recent announcement that, pure is the service is available within the AWS marketplace. And so you think about the, you know, simplicity and the consistency that pure and AWS delivered to the market. AWS customers demand that they get that in the marketplace, and and we're proud to have our offerings there. And Port Works has been in the marketplace and and will continue to be showcased from a container management standpoint. So as those workloads increasingly become, you know, the cloud native you know, Dev Ops, Containerized workloads. We've got a solution and to end to support >>that great job. Great insight. Congratulations to pure good moves as making some good moves. Rob, I want to just get to the final word here on Outpost again. Great. Everyone loves this product again. It's a lot of attention. It's really that that puts the operating models cloud firmly on the in the on premise world for Amazon opens up a lot of good conversation and business opportunities and technical integrations or are all around you. So what's your message to the ecosystem out there for outposts? How do I What's the what's the word? I wanna do I work with you guys? How do I get involved? What are some of the opportunities? What's your position? How do you talk to the ecosystem? >>Yeah, You know, John, I think the best way to frame it is we're just getting started. We've got our first year in the books. We've seen so many promising signals from customers, had so many interesting conversations that just weren't possible without outposts. And, uh, you know, working with partners like pure and expanding our outpost. Ready program is just the beginning. Right? We launched back in September. We've We've seen another meaningful set of partners come out. Uh, here it reinvent, and we're gonna continue toe double down on both the outpost business, but specifically on on working with our partners. I think that the key to unlocking the magic of outpost is meeting customers where they are. And those customers are using our partners. And there's no reason that it shouldn't just work when they move there. Their partner based workload from their existing infrastructure right over to the outpost. >>All right, I'll leave it there. Michael saw the VP of worldwide alliances that pier storage congratulations. Great innovation strategy It's easy to do alliances when you've got a great product and technology congratulated. Rob Kearney Key principle product manager. Outpost will be speaking more to you throughout the next couple of weeks. Here at Reinvent Virtual. Thanks for coming. I appreciate it. >>Thank you. Thank you. >>Okay. So cute. Virtual. We are the Cube. Virtual. We wish we could be there in person this year, but it's a virtual event. Over three weeks will be lots of coverage. I'm John for your host. Thanks for watching.

>> From theCUBE Studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. (intense music) >> Hi, everybody, this is Dave Vellante in theCUBE, and as you know, I've been doing a CEO series, and welcome to the isolation economy. We're here at theCUBE's remote studio, and really pleased to have Charlie Giancarlo, who is the CEO of PureStorage. Charlie, I wish we were face-to-face at Pure Accelerate, but this'll have to do. Thanks for coming on. >> You know, Dave, it's always fun to be face-to-face with you. At Pure Accelerate when we do it in person is great fun, but we do what we have to do, and actually, this has been a great event for us, so appreciate you coming on air with me. >> Yeah, and we're going to chat about that, but I want to start off with this meme that's been going around the internet. I was going to use the wrecking ball. I don't know if you've seen that. It's got the people, the executives in the office building saying, "Eh, digital transformation; "not in my lifetime," complacency, and then this big wrecking ball, the COVID-19. You've probably seen it, but as you can see here, somebody created a survey, Who's leading the digital transformation at your company? The CEO, the CTO, or of course circled is COVID-19, and so we've seen that, right? You had no choice but to be a digital company. >> Well, there's that, and there's also the fact that the CEOs who've been wanting to push a digital transformation against a team that wants to stick with the status quo, it gives the CEO now, and even within our own company in Pure, to drive towards that digital transformation when people didn't really take up the mantle. So no, it's a great opportunity for digital transformation, and of course, the companies that have been doing it all along have been getting ahead during this crisis, and the ones that haven't are having some real trouble. And you and I have had some really interesting conversations. Again, that's, I think, the thing I miss most, not only having you in theCUBE, but the side conversations at the cocktail parties, et cetera. And we've talked about IP, and China, and the history of the US, and all kinds of interesting things there, but one of the things I want to put forth, and I know you guys, Kix especially, has done a lot of work on Tech For Good, but the narrative pre-COVID, PC I guess we'd call it, was really a lot of vitriol toward big tech especially, but you know what? That tech lash... Without tech, where would we be right now? >> Well, just think about it, right? Where would we be without videoconferencing, without the internet, right? We'd be sheltered in place with literally nothing to do, and all business would stop, and of course many businesses that require in-person have, but thank God you can still get goods at your home. You can still get food, you can still get all these things that today is enabled by technology. We've seen this ourselves, in terms of having to make emergency shipments during our first quarter to critical infrastructure to keep things going. It's been quite a quarter. I was saying to my team recently that we had just gotten everyone together in February for our sales kickoff for the year, and it felt like a full year since I had seen them all. >> Well, I had interviewed, I think, is it Mike Fitzgerald, your head of supply chain. >> Yes. >> In March, and he was saying, "No. "We have no disruptions. "We're delivering for clients," and we certainly saw that in your results in the quarter. >> Yeah, no, we're very fortunate, but we had been planning for doing our normal business continuity disaster planning, and actually, once we saw COVID in Asia in January we started exercising all those muscles, including pre-shipping product around to depos around the world in case transportation got clogged, which it in fact did. So we were well-prepared, but we're also, I think, very fortunate in terms of the fact that we had a very distributed supply chain. >> Yeah, I mean you guys obviously did a good job. You saw in Dell's earnings they held pretty firm. HPE, on the other hand, really saw some disruption, so congratulations to you and the team on that. So as we think about exiting this isolation economy, we've done work that shows about 44% of CIOs see a U-shaped recovery, but it's very fragmented. It varies by industry. It varies by how digital the organizations are. Are they able to provide physical distancing? How essential are these organizations? And so I'm sure you're seeing that in your customer base as well. How are you thinking about exiting this isolation economy? >> Well, I've certainly resisted trying to predict a U- or a V-shape, because I think there are many more unknowns than there are knowns, and in particular, we don't know if there's a second wave. If there is a second wave, is it going to be more or less lethal than the first wave? And as you know, maybe some of your audience knows, I contracted COVID in March. So I've done a lot of reading on not just COVID, but also on the Spanish flu of 1918-1919. It's going to take a while before this settles down, and we don't know what it's going to look like the rest of the year or next year. So a lot of the recovery is going to depend on that. What we can do, however, is make sure that we're prepared to work from home, work in the office, that we make sure that our team out in the field is well-placed to be able to support our customers in the environment, and the way that we're incenting our overall team now has less to do with the macro than it does with our specific segment, and what I mean by that is we're incenting our team to continue to build market share, and to continue to outperform our competition as we go forward, and also on our customer satisfaction figure, which you know is our Net Promoter Score, which is the highest in the industry. So that's how we're incenting our team. >> Yeah, and we're going to talk about that, and by the way, yes, I did know, and it's great to see you healthy, and I'd be remiss if I didn't also express my condolences, Matt, the loss of Matt Danziger, your head of IR, terrible tragedy. Of course Matt had some roots in Boston, went to school in Maine. >> Yeah. >> Loved Cape Cod, and so really sad loss, I'm sure, for all of the Puritans. >> It's affected us all very personally, because Matt was just an incredible team member, a great friend, and so young and vital. When someone that young dies for almost unexplainable reasons. It turned out to be a congenital heart condition that nobody knew about, but it just breaks... It just breaks everyone's heart, so thank you for your condolences. I appreciate it. >> You're welcome. Okay, so let's get into the earnings a little bit. I want to just pull up one of the charts that shows roughly, I have approximately Q1 because some companies like NetApp, Dell, HPE, are sort of staggered, but the latest results you saw IBM growing at 19%. Now we know that was mainframe-driven in a very easy compare. Pure plus 12, and then everybody else in the negative. Dell, minus five, so actually doing pretty well relative to NetApp and HPE, who, as I said, had some challenges with deliveries. But let's talk about your quarter. You continue to be the one sort of shining star in the storage business. Let's get into it. What are your big takeaways that you want us to know about? >> Well, of course I'd rather see everybody in the black, right, everybody in the positive, but we continue to take market share and continue to grow 20 to 30% faster than the rest of the industry combined, and it's quarter after quarter. It's not just a peak in one quarter and then behind in another quarter. Every quarter we're ahead of the rest of the industry, and I think the reasoning is really quite straightforward. We're the one company that invests in storage as if it's high technology. You do hear quite often, and even among some customers, that storage is commoditized, and all of our competitors invest in it, or don't invest in it, as if it's a commoditized market. Our view is quite straightforward. The science and the engineering of computing and data centers continues to evolve, continues to advance, has to advance if we continue down this path of becoming more of a digital economy. As we all know, processors advance in speed and capability. Networking advances in terms of speed and capability. Well, data storage is a third of data center spend, and if it doesn't continue to advance at the same pace or faster than everything else, it becomes a major bottleneck. We've been the innovator. If you look at a number of different studies, year after year, now over six or seven years, we are the leader in innovation in the data storage market, and we're being rewarded for that by penetrating more and more of the customer base. >> All right, let's talk about that. And you mentioned in your keynote at Accelerate that you guys spend more on R&D as a percentage of revenue than anybody, and so I want to throw out some stats. I'm sorry, folks, I don't have a slide on this. HPE spends about 1.8 billion a year on R&D, about 6% of revenues. IBM, I've reported on IBM and how it's spending the last 10 years, spent a huge amount on dividends and stock buybacks, and they spent six billion perpetually on R&D, which is now 8% of revenue. Dell at five billion. Of course Dell used to spend well under a billion before the EMC acquisition. That's about 6% of revenue. And NetApp, 800 million, much higher. They're a pure play, about 13%. Pure spends 430 million last year on R&D, which is over 30% of revenue on R&D, to your point. >> Yeah, yeah, well, as I said, we treat it like it's high technology, which it is, right? If you're not spending at an appropriate level you're going to fall behind, and so we continue to advance. I will say that you mentioned big numbers by the other players, but I was part of a big organization as well with a huge R&D budget, but what matters is what percent of the revenue of a specific area are you spending, right? You mentioned Dell and VMware. A very large fraction of their spend is on VMware. Great product and great company, but very little is being spent in the area of storage. >> Well, and the same thing's true for IBM, and I've made this point. In fact, I made this point about Snowflake last week in my breaking analysis. How is Snowflake able to compete with all these big whales? And the same thing for you guys. Every dime you spend on R&D goes to making your storage products better for your customers. Your go-to-market, same thing. Your partner ecosystem, same thing, and so you're the much more focused play. >> Right, well I think it boils down to one very simple thing, right? Most of our competitors are, you might call them one-stop shops, so the shopping mall of IT gear, right? The Best Buy, if you will, of information technology. We're really the sole best of breed player in data storage, right, and if you're a company that wants two vendors, you might choose one that's a one-stop shop. If you have the one-stop shop, the next one you want is a best of breed player, right? And we fill that role for our customers. >> Look it, this business is a technology business, and technology and innovation is driven by research and development, period, the end. But I want to ask you, so the storage business generally, look, you're kind of the one-eyed man in the land of the blind here. I mean the storage business has been somewhat on the back burner. In part it's your fault because you put so much flash into the data center, gave so much headroom that organizations didn't have to buy spindles anymore to get to performance, the cloud has also been a factor. But look, last decade was a better decade for storage than the previous decade when you look at the exits that you guys had and escape velocity, Nutanix, if you can kind of put them in there, too. Much larger than say the Compellents or 3PARs. They didn't make it to a billion. So my question is storage businesses, is it going to come back as a growth business? Like you said, you wish everybody were in the black here. >> Right, well a lot of what's being measured, of course, is enterprise on-prem storage, right? If we add on-prem and cloud, it actually continues to be a big growth business, because data is not shrinking. In fact, data is still growing faster than the price reduction of the media underneath, right, so it's still growing. And as you know, more recently we've introduced what we call Pure as-a-Service and Cloud Block Store. So now we have our same software, which we call Purity, that runs on our on-prem arrays, also running on AWS, and currently in beta on Azure. So from our point of view this is a... First of all, it's a big market, about $30 to $40 billion total. If you add in cloud, it's another $10 to $15 billion, which is a new opportunity for us. Last year we were about 1.65 billion. We're still less than, as you know, less than 10% of the overall market. So the opportunity for us to grow is just tremendous out there, and whether or not total storage grows, for us it's less important right now than the market share that we pick up. >> Right, okay, so I want to stay on that for a minute and talk about... I love talking about the competition. So what I'm showing here with this kind of wheel slide is data from our data partner ETR, and they go out every quarter. They have a very simple methodology. It's like Net Promoter Score, and it's very consistent. They say relative to last year, are you adopting the platform, that's the lime green, and so this is Pure's data. Are you increasing spend by 6% or more? That's the 32%, the forest green. Is spending going to be flat? Is it going to decrease by more than 6%? That's the 9%. And then are you replacing the platform, 2%. Now this was taken at the height of the US lockdown. This last survey. >> Wow. >> So you can see the vast majority of customers are either keeping spending the same, or they're spending more. >> Yeah. >> So that's very, very strong. And I want to just bring up another data point, which is we like to plot that Net Score here on the vertical axis, and then what we call market share. It's not like IDC market share, but it's pervasiveness in the survey. And you can see here, to your point, Pure is really the only, and I've cited the other vendors on the right hand, that box there, you're the only company in the green with a 40% Net Score, and you can see everybody else is well below the line in the red, but to your point, you got a long way to go in terms of gaining market share. >> Exactly, right, and the reason... I think the reason why you're seeing that is really our fundamental and basic value is that our product and our company is easy to do business with and easy to operate, and it's such a pleasure to use versus the competition that customers really appreciate the product and the company. We do have a Net Promoter Score of over 80, which I think you'd be hard-pressed to find another company in any industry with Net Promoter Scores that high. >> Yeah, so I want to stay on the R&D thing for a minute, because you guys bet the company from day one on simplicity, and that's really where you put a lot of effort. So the cloud is vital here, and I want to get your perspective on it. You mentioned your Cloud Block Store, which I like that, it's native to AWS. I think you're adding other platforms. I think you're adding Azure as well, and I'm sure you'll do Google. >> Azure, Azure's in beta, yes. >> Yeah, Google's just a matter of time. Alibaba, you'll get them all, but the key here is that you're taking advantage of the native services, and let's take AWS as an example. You're using EC2, and high priority instances of EC2, as an example, to essentially improve block storage on Amazon. Amazon loves it because it sells Compute. Maybe the storage guys in Amazon don't love it so much, but it's all about the customer, and so the native cloud services are critical. I'm sure you're going to do the same thing for Azure and other clouds, and that takes a lot of investment, but I heard George Kurian today addressing some analysts, talking about they're the only company doing kind of that cloud native approach. Where are you placing your bets? How much of it is cloud versus kind of on-prem, if you will? >> Yeah, well... So first of all, an increasing fraction is cloud, as you might imagine, right? We started off with a few dozen developers, and now we're at many more than that. Of course the majority of our revenue still comes from on-prem, but the value is the following in our case, which is that we literally have the same software operating, from a customer and from a application standpoint. It is the same software operating on-prem as in the cloud, which means that the customer doesn't have to refactor their application to move it into the cloud, and we're the one vendor that's focused on block. What NetApp is doing is great, but it's a file-based system. It's really designed for smaller workloads and low performance workloads. Our system's designed for high performance enterprise workloads, Tier 1 workloads in the cloud. To say that they're both cloud sort of washes over the fact that they're almost going after two completely separate markets. >> Well, I think it's interesting that you're both really emphasizing cloud native, which I think is very important. I think that some of the others have some catching up to do in that regard, and again, that takes a big investment in not just wrapping your stack, and shoving it in the cloud, and hosting it in the cloud. You're actually taking advantage of the local services. >> Well, I mean one thing I'll mention was Amazon gave us an award, which they give to very few vendors. It's called the Well-Architected AWS Award, because we've designed it not to operate, let's say, in a virtualized environment on AWS. We really make use of the native AWS EC2 services. It is designed like a web service on EC2. >> And the reason why this is so important is just, again, to share with our audience is because when you start talking about multi-cloud and hybrid cloud, you want the same exact experience on-prem as you do in the cloud, whether it's hybrid or across clouds, and the key is if you're using cloud native services, you have the most efficient, the highest performance, lowest latency, and lowest cost solution. That is going to be... That's going to be a determinate of the winner. >> Yes, I believe so. Customers don't want to be doing... Be working with software that is going to change, fundamentally change and cause them to have to refactor their applications. If it's not designed natively to the cloud, then when Amazon upgrades it may cause a real problem with the software or with the environment, and so customers don't want that. They want to know they're cloud native. >> Well, your task over the next 10 years is something. Look it, it's very challenging to grow a company the size of Pure, period, but let's face it, you guys caught EMC off-guard. You were driving a truck through the Symmetrics base and the VNX base. Not that that was easy. (chuckling) And they certainly didn't make it easy for ya. But now we've got this sort of next chapter, and I want to talk a little bit about this. You guys call it the Modern Data Experience. You laid it out last Accelerate, kind of your vision. You talked about it more at this year's Accelerate. I wonder if you could tell us the key takeaways from your conference this year. >> Right, the key takeaway... So let me talk about both. I'll start with Modern Data Experience and then key takeaways from this Accelerate. So Modern Data Experience, for those that are not yet familiar with it, is the idea that an on-prem experience would look very similar, if not identical, to a cloud experience. That is to say that applications and orchestrators just use APIs to be able to call upon and have delivered the storage environment that they want to see instantaneously over a high speed network. The amazing thing about storage, even today, is that it's highly mechanical, it's highly hardware-oriented to where if you have a new application and you want storage, you actually have to buy an array and connect it. It's physical. Where we want to be is just like in the cloud. If you have a new application and you want storage or you want data services, you just write a few APIs in your application and it's delivered immediately and automatically, and that's what we're delivering on-prem with the Modern Data Experience. What we're also doing, though, is extending that to the cloud, and with Cloud Block Store as part of this, with that set of interfaces and management system exactly the same as on-prem, you now have that cloud experience across all the clouds without having to refactor applications in one or the other. So that's our Modern Data Experience. That's the vision that drives us. We've delivered more and more against it starting at the last Accelerate, but even more now. Part of this is being able to deliver storage that is flexible and able to be delivered by API. On this Accelerate we delivered our Purity 6.0 for Flash Array, which adds not only greater resiliency characteristics, but now file for the first time in a Flash Array environment, and so now the same Flash Array can deliver both file and block. Which is a unified experience, but all delivered by API and simple to operate. We've also delivered, more recently, Flash Array 3.0... I'm sorry, Purity 3.0 on FlashBlade that delivers the ability for FlashBlade now to have very high resiliency characteristics, and to be able to even better deliver the ability to restore applications when there's been a failure of their data systems very, very rapidly, something that we call Rapid Restore. So these are huge benefits. And the last one I'll mention, Pure as-a-Service allows a customer today to be able to contract for storage as a service on-prem and in the cloud with one unified subscription. So they only pay for what they use. They only pay for what they use when they use it, and they only pay for it, regardless of where it's used, on-prem or in the cloud, and it's a true subscription model. It's owned and operated by Pure, but the customer gets the benefit of only paying for what they use, regardless of where they use it. >> Awesome, thanks for that run through. And a couple other notes that I had, I mean you obviously talked about the support for the work from home and remote capabilities. Automation came up a lot. >> Yep. >> You and I, I said, we have these great conversations, and one of the ones I would have with you if we were having a drink somewhere would be if you look at productivity stats in US and Europe, they're declining-- >> Yes. >> Pretty dramatically. And if you think about the grand challenges we have, the global challenges, whether it's pandemics, or healthcare, or feeding people, et cetera, we're not going to be able to meet those challenges without automation. I mean people, for years, have been afraid of automation. "Oh, we're going to lose jobs." We don't have enough people to solve all these problems, and so I think that's behind us, right-- >> Yeah, I agree. >> The fear of automation. So that came up. Yeah, go ahead, please. >> I once met with Alan Greenspan. You may remember him. >> Of course. >> This is after he was the chairman, and he said, "Look, I've studied the economies now "for the last 100 years, "and the fact of the matter is "that wealth follows productivity." The more productive you are as a society, that means the greater the wealth that exists for every individual, right? The standard of living follows productivity, and without productivity there's no wealth creation for society. So to your point, yeah, if we don't become more productive, more efficient, people don't live better, right? >> Yeah, I knew you'd have some good thoughts on that, and of course, speaking of Greenspan, we're seeing a little bit of rational exuberance maybe in the market. (chuckling) Pretty amazing. But you also talked about containers, and persisting containers, and Kubernetes, the importance of Kubernetes. That seems to be a big trend that you guys are hopping on as well. >> You bet. It is the wave of the future. Now, like all waves of the future, it's going to take time. Containers work entirely differently from VMs and from machines in terms of how they utilize resources inside a data center environment, and they are extraordinarily dynamic. They require the ability to build up, tear down connections to storage, and create storage, and spin it down at very, very rapid rates, and again, it's all API-driven. It's all responsive, not to human operators, but it's got to be responsive to the application itself and to the orchestration environment. And again, I'll go back to what we talked about with our Modern Data Experience. It's exactly the kind of experience that our customers want to be able to be that responsive to this new environment. >> My last question is from John Furrier. He asked me, "Hey, Charlie knows a lot about networking." We were talking about multi-cloud. Obviously cross-cloud networks are going to become increasingly important. People are trying to get rid of their MPLS networks, really moving to an SD-WAN environment. Your thoughts on the evolution of networking over the next decade. >> Well, I'll tell you. I'm a big believer that even SD-WANs, over time, are going to become obsolete. Another way to phrase it is the new private network is the internet. I mean look at it now. What does SD-WAN mean when nobody's in the local office, right? No one's in the remote office; they're all at home. And so now we need to think about the fact... Sometimes it's called Zero Trust. I don't like that term. Nobody wants to talk about zero anything. What it really is about is that there is no internal network anymore. The fact of the matter is even for... Let's say I'm inside my own company's network. Well, do they trust my machine? Maybe not. They may trust me but not my machine, and so what we need to have is going to a cloud model where all communication to all servers goes through a giant, call it a firewall or a proxy service, where everything is cleaned before it's delivered. People, individuals only get, and applications, only get access to the applications that they're authorized to use, not to a network, because once they're in the network they can get anywhere. So they should only get access to the applications they're able to use. So my personal opinion is the internet is the future private network, and that requires a very different methodology for authentication for security and so forth, and if we think that we protect ourselves now by firewalls, we have to rethink that. >> Great perspectives. And by the way, you're seeing more than glimpses of that. You look at Zscaler's results recently, and that's kind of the security cloud, and I'm glad you mentioned that you don't like that sort of Zero Trust. You guys, even today, talked about near zero RPO. That's an honest statement-- >> Right. >> Because there's no such thing as zero RPO. (chuckling) >> Right, yeah. >> Charlie, great to have you on. Thanks so much for coming back in theCUBE. Great to see you again. >> Dave, always a pleasure. Thank you so much, and hopefully next time in person. >> I hope so. All right, and thank you for watching, everybody. This is Dave Vellante for theCUBE, and we'll see you next time. (smooth music)

>>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.

>> Joy: Welcome to the Virtual Big Data Conference. Vertica is so excited to host this event. I'm Joy King, and I'll be your host for today's Big Data Conference Keynote Session. It's my honor and my genuine pleasure to lead Vertica's product and go-to-market strategy. And I'm so lucky to have a passionate and committed team who turned our Vertica BDC event, into a virtual event in a very short amount of time. I want to thank the thousands of people, and yes, that's our true number who have registered to attend this virtual event. We were determined to balance your health, safety and your peace of mind with the excitement of the Vertica BDC. This is a very unique event. Because as I hope you all know, we focus on engineering and architecture, best practice sharing and customer stories that will educate and inspire everyone. I also want to thank our top sponsors for the virtual BDC, Arrow, and Pure Storage. Our partnerships are so important to us and to everyone in the audience. Because together, we get things done faster and better. Now for today's keynote, you'll hear from three very important and energizing speakers. First, Colin Mahony, our SVP and General Manager for Vertica, will talk about the market trends that Vertica is betting on to win for our customers. And he'll share the exciting news about our Vertica 10 announcement and how this will benefit our customers. Then you'll hear from Amy Fowler, VP of strategy and solutions for FlashBlade at Pure Storage. Our partnership with Pure Storage is truly unique in the industry, because together modern infrastructure from Pure powers modern analytics from Vertica. And then you'll hear from John Yovanovich, Director of IT at AT&T, who will tell you about the Pure Vertica Symphony that plays live every day at AT&T. Here we go, Colin, over to you. >> Colin: Well, thanks a lot joy. And, I want to echo Joy's thanks to our sponsors, and so many of you who have helped make this happen. This is not an easy time for anyone. We were certainly looking forward to getting together in person in Boston during the Vertica Big Data Conference and Winning with Data. But I think all of you and our team have done a great job, scrambling and putting together a terrific virtual event. So really appreciate your time. I also want to remind people that we will make both the slides and the full recording available after this. So for any of those who weren't able to join live, that is still going to be available. Well, things have been pretty exciting here. And in the analytic space in general, certainly for Vertica, there's a lot happening. There are a lot of problems to solve, a lot of opportunities to make things better, and a lot of data that can really make every business stronger, more efficient, and frankly, more differentiated. For Vertica, though, we know that focusing on the challenges that we can directly address with our platform, and our people, and where we can actually make the biggest difference is where we ought to be putting our energy and our resources. I think one of the things that has made Vertica so strong over the years is our ability to focus on those areas where we can make a great difference. So for us as we look at the market, and we look at where we play, there are really three recent and some not so recent, but certainly picking up a lot of the market trends that have become critical for every industry that wants to Win Big With Data. We've heard this loud and clear from our customers and from the analysts that cover the market. If I were to summarize these three areas, this really is the core focus for us right now. We know that there's massive data growth. And if we can unify the data silos so that people can really take advantage of that data, we can make a huge difference. We know that public clouds offer tremendous advantages, but we also know that balance and flexibility is critical. And we all need the benefit that machine learning for all the types up to the end data science. We all need the benefits that they can bring to every single use case, but only if it can really be operationalized at scale, accurate and in real time. And the power of Vertica is, of course, how we're able to bring so many of these things together. Let me talk a little bit more about some of these trends. So one of the first industry trends that we've all been following probably now for over the last decade, is Hadoop and specifically HDFS. So many companies have invested, time, money, more importantly, people in leveraging the opportunity that HDFS brought to the market. HDFS is really part of a much broader storage disruption that we'll talk a little bit more about, more broadly than HDFS. But HDFS itself was really designed for petabytes of data, leveraging low cost commodity hardware and the ability to capture a wide variety of data formats, from a wide variety of data sources and applications. And I think what people really wanted, was to store that data before having to define exactly what structures they should go into. So over the last decade or so, the focus for most organizations is figuring out how to capture, store and frankly manage that data. And as a platform to do that, I think, Hadoop was pretty good. It certainly changed the way that a lot of enterprises think about their data and where it's locked up. In parallel with Hadoop, particularly over the last five years, Cloud Object Storage has also given every organization another option for collecting, storing and managing even more data. That has led to a huge growth in data storage, obviously, up on public clouds like Amazon and their S3, Google Cloud Storage and Azure Blob Storage just to name a few. And then when you consider regional and local object storage offered by cloud vendors all over the world, the explosion of that data, in leveraging this type of object storage is very real. And I think, as I mentioned, it's just part of this broader storage disruption that's been going on. But with all this growth in the data, in all these new places to put this data, every organization we talk to is facing even more challenges now around the data silo. Sure the data silos certainly getting bigger. And hopefully they're getting cheaper per bit. But as I said, the focus has really been on collecting, storing and managing the data. But between the new data lakes and many different cloud object storage combined with all sorts of data types from the complexity of managing all this, getting that business value has been very limited. This actually takes me to big bet number one for Team Vertica, which is to unify the data. Our goal, and some of the announcements we have made today plus roadmap announcements I'll share with you throughout this presentation. Our goal is to ensure that all the time, money and effort that has gone into storing that data, all the data turns into business value. So how are we going to do that? With a unified analytics platform that analyzes the data wherever it is HDFS, Cloud Object Storage, External tables in an any format ORC, Parquet, JSON, and of course, our own Native Roth Vertica format. Analyze the data in the right place in the right format, using a single unified tool. This is something that Vertica has always been committed to, and you'll see in some of our announcements today, we're just doubling down on that commitment. Let's talk a little bit more about the public cloud. This is certainly the second trend. It's the second wave maybe of data disruption with object storage. And there's a lot of advantages when it comes to public cloud. There's no question that the public clouds give rapid access to compute storage with the added benefit of eliminating data center maintenance that so many companies, want to get out of themselves. But maybe the biggest advantage that I see is the architectural innovation. The public clouds have introduced so many methodologies around how to provision quickly, separating compute and storage and really dialing-in the exact needs on demand, as you change workloads. When public clouds began, it made a lot of sense for the cloud providers and their customers to charge and pay for compute and storage in the ratio that each use case demanded. And I think you're seeing that trend, proliferate all over the place, not just up in public cloud. That architecture itself is really becoming the next generation architecture for on-premise data centers, as well. But there are a lot of concerns. I think we're all aware of them. They're out there many times for different workloads, there are higher costs. Especially if some of the workloads that are being run through analytics, which tend to run all the time. Just like some of the silo challenges that companies are facing with HDFS, data lakes and cloud storage, the public clouds have similar types of siloed challenges as well. Initially, there was a belief that they were cheaper than data centers, and when you added in all the costs, it looked that way. And again, for certain elastic workloads, that is the case. I don't think that's true across the board overall. Even to the point where a lot of the cloud vendors aren't just charging lower costs anymore. We hear from a lot of customers that they don't really want to tether themselves to any one cloud because of some of those uncertainties. Of course, security and privacy are a concern. We hear a lot of concerns with regards to cloud and even some SaaS vendors around shared data catalogs, across all the customers and not enough separation. But security concerns are out there, you can read about them. I'm not going to jump into that bandwagon. But we hear about them. And then, of course, I think one of the things we hear the most from our customers, is that each cloud stack is starting to feel even a lot more locked in than the traditional data warehouse appliance. And as everybody knows, the industry has been running away from appliances as fast as it can. And so they're not eager to get locked into another, quote, unquote, virtual appliance, if you will, up in the cloud. They really want to make sure they have flexibility in which clouds, they're going to today, tomorrow and in the future. And frankly, we hear from a lot of our customers that they're very interested in eventually mixing and matching, compute from one cloud with, say storage from another cloud, which I think is something that we'll hear a lot more about. And so for us, that's why we've got our big bet number two. we love the cloud. We love the public cloud. We love the private clouds on-premise, and other hosting providers. But our passion and commitment is for Vertica to be able to run in any of the clouds that our customers choose, and make it portable across those clouds. We have supported on-premises and all public clouds for years. And today, we have announced even more support for Vertica in Eon Mode, the deployment option that leverages the separation of compute from storage, with even more deployment choices, which I'm going to also touch more on as we go. So super excited about our big bet number two. And finally as I mentioned, for all the hype that there is around machine learning, I actually think that most importantly, this third trend that team Vertica is determined to address is the need to bring business critical, analytics, machine learning, data science projects into production. For so many years, there just wasn't enough data available to justify the investment in machine learning. Also, processing power was expensive, and storage was prohibitively expensive. But to train and score and evaluate all the different models to unlock the full power of predictive analytics was tough. Today you have those massive data volumes. You have the relatively cheap processing power and storage to make that dream a reality. And if you think about this, I mean with all the data that's available to every company, the real need is to operationalize the speed and the scale of machine learning so that these organizations can actually take advantage of it where they need to. I mean, we've seen this for years with Vertica, going back to some of the most advanced gaming companies in the early days, they were incorporating this with live data directly into their gaming experiences. Well, every organization wants to do that now. And the accuracy for clickability and real time actions are all key to separating the leaders from the rest of the pack in every industry when it comes to machine learning. But if you look at a lot of these projects, the reality is that there's a ton of buzz, there's a ton of hype spanning every acronym that you can imagine. But most companies are struggling, do the separate teams, different tools, silos and the limitation that many platforms are facing, driving, down sampling to get a small subset of the data, to try to create a model that then doesn't apply, or compromising accuracy and making it virtually impossible to replicate models, and understand decisions. And if there's one thing that we've learned when it comes to data, prescriptive data at the atomic level, being able to show end of one as we refer to it, meaning individually tailored data. No matter what it is healthcare, entertainment experiences, like gaming or other, being able to get at the granular data and make these decisions, make that scoring applies to machine learning just as much as it applies to giving somebody a next-best-offer. But the opportunity has never been greater. The need to integrate this end-to-end workflow and support the right tools without compromising on that accuracy. Think about it as no downsampling, using all the data, it really is key to machine learning success. Which should be no surprise then why the third big bet from Vertica is one that we've actually been working on for years. And we're so proud to be where we are today, helping the data disruptors across the world operationalize machine learning. This big bet has the potential to truly unlock, really the potential of machine learning. And today, we're announcing some very important new capabilities specifically focused on unifying the work being done by the data science community, with their preferred tools and platforms, and the volume of data and performance at scale, available in Vertica. Our strategy has been very consistent over the last several years. As I said in the beginning, we haven't deviated from our strategy. Of course, there's always things that we add. Most of the time, it's customer driven, it's based on what our customers are asking us to do. But I think we've also done a great job, not trying to be all things to all people. Especially as these hype cycles flare up around us, we absolutely love participating in these different areas without getting completely distracted. I mean, there's a variety of query tools and data warehouses and analytics platforms in the market. We all know that. There are tools and platforms that are offered by the public cloud vendors, by other vendors that support one or two specific clouds. There are appliance vendors, who I was referring to earlier who can deliver package data warehouse offerings for private data centers. And there's a ton of popular machine learning tools, languages and other kits. But Vertica is the only advanced analytic platform that can do all this, that can bring it together. We can analyze the data wherever it is, in HDFS, S3 Object Storage, or Vertica itself. Natively we support multiple clouds on-premise deployments, And maybe most importantly, we offer that choice of deployment modes to allow our customers to choose the architecture that works for them right now. It still also gives them the option to change move, evolve over time. And Vertica is the only analytics database with end-to-end machine learning that can truly operationalize ML at scale. And I know it's a mouthful. But it is not easy to do all these things. It is one of the things that highly differentiates Vertica from the rest of the pack. It is also why our customers, all of you continue to bet on us and see the value that we are delivering and we will continue to deliver. Here's a couple of examples of some of our customers who are powered by Vertica. It's the scale of data. It's the millisecond response times. Performance and scale have always been a huge part of what we have been about, not the only thing. I think the functionality all the capabilities that we add to the platform, the ease of use, the flexibility, obviously with the deployment. But if you look at some of the numbers they are under these customers on this slide. And I've shared a lot of different stories about these customers. Which, by the way, it still amaze me every time I talk to one and I get the updates, you can see the power and the difference that Vertica is making. Equally important, if you look at a lot of these customers, they are the epitome of being able to deploy Vertica in a lot of different environments. Many of the customers on this slide are not using Vertica just on-premise or just in the cloud. They're using it in a hybrid way. They're using it in multiple different clouds. And again, we've been with them on that journey throughout, which is what has made this product and frankly, our roadmap and our vision exactly what it is. It's been quite a journey. And that journey continues now with the Vertica 10 release. The Vertica 10 release is obviously a massive release for us. But if you look back, you can see that building on that native columnar architecture that started a long time ago, obviously, with the C-Store paper. We built it to leverage that commodity hardware, because it was an architecture that was never tightly integrated with any specific underlying infrastructure. I still remember hearing the initial pitch from Mike Stonebreaker, about the vision of Vertica as a software only solution and the importance of separating the company from hardware innovation. And at the time, Mike basically said to me, "there's so much R&D in innovation that's going to happen in hardware, we shouldn't bake hardware into our solution. We should do it in software, and we'll be able to take advantage of that hardware." And that is exactly what has happened. But one of the most recent innovations that we embraced with hardware is certainly that separation of compute and storage. As I said previously, the public cloud providers offered this next generation architecture, really to ensure that they can provide the customers exactly what they needed, more compute or more storage and charge for each, respectively. The separation of compute and storage, compute from storage is a major milestone in data center architectures. If you think about it, it's really not only a public cloud innovation, though. It fundamentally redefines the next generation data architecture for on-premise and for pretty much every way people are thinking about computing today. And that goes for software too. Object storage is an example of the cost effective means for storing data. And even more importantly, separating compute from storage for analytic workloads has a lot of advantages. Including the opportunity to manage much more dynamic, flexible workloads. And more importantly, truly isolate those workloads from others. And by the way, once you start having something that can truly isolate workloads, then you can have the conversations around autonomic computing, around setting up some nodes, some compute resources on the data that won't affect any of the other data to do some things on their own, maybe some self analytics, by the system, etc. A lot of things that many of you know we've already been exploring in terms of our own system data in the product. But it was May 2018, believe it or not, it seems like a long time ago where we first announced Eon Mode and I want to make something very clear, actually about Eon mode. It's a mode, it's a deployment option for Vertica customers. And I think this is another huge benefit that we don't talk about enough. But unlike a lot of vendors in the market who will dig you and charge you for every single add-on like hit-buy, you name it. You get this with the Vertica product. If you continue to pay support and maintenance, this comes with the upgrade. This comes as part of the new release. So any customer who owns or buys Vertica has the ability to set up either an Enterprise Mode or Eon Mode, which is a question I know that comes up sometimes. Our first announcement of Eon was obviously AWS customers, including the trade desk, AT&T. Most of whom will be speaking here later at the Virtual Big Data Conference. They saw a huge opportunity. Eon Mode, not only allowed Vertica to scale elastically with that specific compute and storage that was needed, but it really dramatically simplified database operations including things like workload balancing, node recovery, compute provisioning, etc. So one of the most popular functions is that ability to isolate the workloads and really allocate those resources without negatively affecting others. And even though traditional data warehouses, including Vertica Enterprise Mode have been able to do lots of different workload isolation, it's never been as strong as Eon Mode. Well, it certainly didn't take long for our customers to see that value across the board with Eon Mode. Not just up in the cloud, in partnership with one of our most valued partners and a platinum sponsor here. Joy mentioned at the beginning. We announced Vertica Eon Mode for Pure Storage FlashBlade in September 2019. And again, just to be clear, this is not a new product, it's one Vertica with yet more deployment options. With Pure Storage, Vertica in Eon mode is not limited in any way by variable cloud, network latency. The performance is actually amazing when you take the benefits of separate and compute from storage and you run it with a Pure environment on-premise. Vertica in Eon Mode has a super smart cache layer that we call the depot. It's a big part of our secret sauce around Eon mode. And combined with the power and performance of Pure's FlashBlade, Vertica became the industry's first advanced analytics platform that actually separates compute and storage for on-premises data centers. Something that a lot of our customers are already benefiting from, and we're super excited about it. But as I said, this is a journey. We don't stop, we're not going to stop. Our customers need the flexibility of multiple public clouds. So today with Vertica 10, we're super proud and excited to announce support for Vertica in Eon Mode on Google Cloud. This gives our customers the ability to use their Vertica licenses on Amazon AWS, on-premise with Pure Storage and on Google Cloud. Now, we were talking about HDFS and a lot of our customers who have invested quite a bit in HDFS as a place, especially to store data have been pushing us to support Eon Mode with HDFS. So as part of Vertica 10, we are also announcing support for Vertica in Eon Mode using HDFS as the communal storage. Vertica's own Roth format data can be stored in HDFS, and actually the full functionality of Vertica is complete analytics, geospatial pattern matching, time series, machine learning, everything that we have in there can be applied to this data. And on the same HDFS nodes, Vertica can actually also analyze data in ORC or Parquet format, using External tables. We can also execute joins between the Roth data the External table holds, which powers a much more comprehensive view. So again, it's that flexibility to be able to support our customers, wherever they need us to support them on whatever platform, they have. Vertica 10 gives us a lot more ways that we can deploy Eon Mode in various environments for our customers. It allows them to take advantage of Vertica in Eon Mode and the power that it brings with that separation, with that workload isolation, to whichever platform they are most comfortable with. Now, there's a lot that has come in Vertica 10. I'm definitely not going to be able to cover everything. But we also introduced complex types as an example. And complex data types fit very well into Eon as well in this separation. They significantly reduce the data pipeline, the cost of moving data between those, a much better support for unstructured data, which a lot of our customers have mixed with structured data, of course, and they leverage a lot of columnar execution that Vertica provides. So you get complex data types in Vertica now, a lot more data, stronger performance. It goes great with the announcement that we made with the broader Eon Mode. Let's talk a little bit more about machine learning. We've been actually doing work in and around machine learning with various extra regressions and a whole bunch of other algorithms for several years. We saw the huge advantage that MPP offered, not just as a sequel engine as a database, but for ML as well. Didn't take as long to realize that there's a lot more to operationalizing machine learning than just those algorithms. It's data preparation, it's that model trade training. It's the scoring, the shaping, the evaluation. That is so much of what machine learning and frankly, data science is about. You do know, everybody always wants to jump to the sexy algorithm and we handle those tasks very, very well. It makes Vertica a terrific platform to do that. A lot of work in data science and machine learning is done in other tools. I had mentioned that there's just so many tools out there. We want people to be able to take advantage of all that. We never believed we were going to be the best algorithm company or come up with the best models for people to use. So with Vertica 10, we support PMML. We can import now and export PMML models. It's a huge step for us around that operationalizing machine learning projects for our customers. Allowing the models to get built outside of Vertica yet be imported in and then applying to that full scale of data with all the performance that you would expect from Vertica. We also are more tightly integrating with Python. As many of you know, we've been doing a lot of open source projects with the community driven by many of our customers, like Uber. And so now with Python we've integrated with TensorFlow, allowing data scientists to build models in their preferred language, to take advantage of TensorFlow. But again, to store and deploy those models at scale with Vertica. I think both these announcements are proof of our big bet number three, and really our commitment to supporting innovation throughout the community by operationalizing ML with that accuracy, performance and scale of Vertica for our customers. Again, there's a lot of steps when it comes to the workflow of machine learning. These are some of them that you can see on the slide, and it's definitely not linear either. We see this as a circle. And companies that do it, well just continue to learn, they continue to rescore, they continue to redeploy and they want to operationalize all that within a single platform that can take advantage of all those capabilities. And that is the platform, with a very robust ecosystem that Vertica has always been committed to as an organization and will continue to be. This graphic, many of you have seen it evolve over the years. Frankly, if we put everything and everyone on here wouldn't fit on a slide. But it will absolutely continue to evolve and grow as we support our customers, where they need the support most. So, again, being able to deploy everywhere, being able to take advantage of Vertica, not just as a business analyst or a business user, but as a data scientists or as an operational or BI person. We want Vertica to be leveraged and used by the broader organization. So I think it's fair to say and I encourage everybody to learn more about Vertica 10, because I'm just highlighting some of the bigger aspects of it. But we talked about those three market trends. The need to unify the silos, the need for hybrid multiple cloud deployment options, the need to operationalize business critical machine learning projects. Vertica 10 has absolutely delivered on those. But again, we are not going to stop. It is our job not to, and this is how Team Vertica thrives. I always joke that the next release is the best release. And, of course, even after Vertica 10, that is also true, although Vertica 10 is pretty awesome. But, you know, from the first line of code, we've always been focused on performance and scale, right. And like any really strong data platform, the execution engine, the optimizer and the execution engine are the two core pieces of that. Beyond Vertica 10, some of the big things that we're already working on, next generation execution engine. We're already actually seeing incredible early performance from this. And this is just one example, of how important it is for an organization like Vertica to constantly go back and re-innovate. Every single release, we do the sit ups and crunches, our performance and scale. How do we improve? And there's so many parts of the core server, there's so many parts of our broader ecosystem. We are constantly looking at coverages of how we can go back to all the code lines that we have, and make them better in the current environment. And it's not an easy thing to do when you're doing that, and you're also expanding in the environment that we are expanding into to take advantage of the different deployments, which is a great segue to this slide. Because if you think about today, we're obviously already available with Eon Mode and Amazon, AWS and Pure and actually MinIO as well. As I talked about in Vertica 10 we're adding Google and HDFS. And coming next, obviously, Microsoft Azure, Alibaba cloud. So being able to expand into more of these environments is really important for the Vertica team and how we go forward. And it's not just running in these clouds, for us, we want it to be a SaaS like experience in all these clouds. We want you to be able to deploy Vertica in 15 minutes or less on these clouds. You can also consume Vertica, in a lot of different ways, on these clouds. As an example, in Amazon Vertica by the Hour. So for us, it's not just about running, it's about taking advantage of the ecosystems that all these cloud providers offer, and really optimizing the Vertica experience as part of them. Optimization, around automation, around self service capabilities, extending our management console, we now have products that like the Vertica Advisor Tool that our Customer Success Team has created to actually use our own smarts in Vertica. To take data from customers that give it to us and help them tune automatically their environment. You can imagine that we're taking that to the next level, in a lot of different endeavors that we're doing around how Vertica as a product can actually be smarter because we all know that simplicity is key. There just aren't enough people in the world who are good at managing data and taking it to the next level. And of course, other things that we all hear about, whether it's Kubernetes and containerization. You can imagine that that probably works very well with the Eon Mode and separating compute and storage. But innovation happens everywhere. We innovate around our community documentation. Many of you have taken advantage of the Vertica Academy. The numbers there are through the roof in terms of the number of people coming in and certifying on it. So there's a lot of things that are within the core products. There's a lot of activity and action beyond the core products that we're taking advantage of. And let's not forget why we're here, right? It's easy to talk about a platform, a data platform, it's easy to jump into all the functionality, the analytics, the flexibility, how we can offer it. But at the end of the day, somebody, a person, she's got to take advantage of this data, she's got to be able to take this data and use this information to make a critical business decision. And that doesn't happen unless we explore lots of different and frankly, new ways to get that predictive analytics UI and interface beyond just the standard BI tools in front of her at the right time. And so there's a lot of activity, I'll tease you with that going on in this organization right now about how we can do that and deliver that for our customers. We're in a great position to be able to see exactly how this data is consumed and used and start with this core platform that we have to go out. Look, I know, the plan wasn't to do this as a virtual BDC. But I really appreciate you tuning in. Really appreciate your support. I think if there's any silver lining to us, maybe not being able to do this in person, it's the fact that the reach has actually gone significantly higher than what we would have been able to do in person in Boston. We're certainly looking forward to doing a Big Data Conference in the future. But if I could leave you with anything, know this, since that first release for Vertica, and our very first customers, we have been very consistent. We respect all the innovation around us, whether it's open source or not. We understand the market trends. We embrace those new ideas and technologies and for us true north, and the most important thing is what does our customer need to do? What problem are they trying to solve? And how do we use the advantages that we have without disrupting our customers? But knowing that you depend on us to deliver that unified analytics strategy, it will deliver that performance of scale, not only today, but tomorrow and for years to come. We've added a lot of great features to Vertica. I think we've said no to a lot of things, frankly, that we just knew we wouldn't be the best company to deliver. When we say we're going to do things we do them. Vertica 10 is a perfect example of so many of those things that we from you, our customers have heard loud and clear, and we have delivered. I am incredibly proud of this team across the board. I think the culture of Vertica, a customer first culture, jumping in to help our customers win no matter what is also something that sets us massively apart. I hear horror stories about support experiences with other organizations. And people always seem to be amazed at Team Vertica's willingness to jump in or their aptitude for certain technical capabilities or understanding the business. And I think sometimes we take that for granted. But that is the team that we have as Team Vertica. We are incredibly excited about Vertica 10. I think you're going to love the Virtual Big Data Conference this year. I encourage you to tune in. Maybe one other benefit is I know some people were worried about not being able to see different sessions because they were going to overlap with each other well now, even if you can't do it live, you'll be able to do those sessions on demand. Please enjoy the Vertica Big Data Conference here in 2020. Please you and your families and your co-workers be safe during these times. I know we will get through it. And analytics is probably going to help with a lot of that and we already know it is helping in many different ways. So believe in the data, believe in data's ability to change the world for the better. And thank you for your time. And with that, I am delighted to now introduce Micro Focus CEO Stephen Murdoch to the Vertica Big Data Virtual Conference. Thank you Stephen. >> Stephen: Hi, everyone, my name is Stephen Murdoch. I have the pleasure and privilege of being the Chief Executive Officer here at Micro Focus. Please let me add my welcome to the Big Data Conference. And also my thanks for your support, as we've had to pivot to this being virtual rather than a physical conference. Its amazing how quickly we all reset to a new normal. I certainly didn't expect to be addressing you from my study. Vertica is an incredibly important part of Micro Focus family. Is key to our goal of trying to enable and help customers become much more data driven across all of their IT operations. Vertica 10 is a huge step forward, we believe. It allows for multi-cloud innovation, genuinely hybrid deployments, begin to leverage machine learning properly in the enterprise, and also allows the opportunity to unify currently siloed lakes of information. We operate in a very noisy, very competitive market, and there are people, who are in that market who can do some of those things. The reason we are so excited about Vertica is we genuinely believe that we are the best at doing all of those things. And that's why we've announced publicly, you're under executing internally, incremental investment into Vertica. That investments targeted at accelerating the roadmaps that already exist. And getting that innovation into your hands faster. This idea is speed is key. It's not a question of if companies have to become data driven organizations, it's a question of when. So that speed now is really important. And that's why we believe that the Big Data Conference gives a great opportunity for you to accelerate your own plans. You will have the opportunity to talk to some of our best architects, some of the best development brains that we have. But more importantly, you'll also get to hear from some of our phenomenal Roth Data customers. You'll hear from Uber, from the Trade Desk, from Philips, and from AT&T, as well as many many others. And just hearing how those customers are using the power of Vertica to accelerate their own, I think is the highlight. And I encourage you to use this opportunity to its full. Let me close by, again saying thank you, we genuinely hope that you get as much from this virtual conference as you could have from a physical conference. And we look forward to your engagement, and we look forward to hearing your feedback. With that, thank you very much. >> Joy: Thank you so much, Stephen, for joining us for the Vertica Big Data Conference. Your support and enthusiasm for Vertica is so clear, and it makes a big difference. Now, I'm delighted to introduce Amy Fowler, the VP of Strategy and Solutions for FlashBlade at Pure Storage, who was one of our BDC Platinum Sponsors, and one of our most valued partners. It was a proud moment for me, when we announced Vertica in Eon mode for Pure Storage FlashBlade and we became the first analytics data warehouse that separates compute from storage for on-premise data centers. Thank you so much, Amy, for joining us. Let's get started. >> Amy: Well, thank you, Joy so much for having us. And thank you all for joining us today, virtually, as we may all be. So, as we just heard from Colin Mahony, there are some really interesting trends that are happening right now in the big data analytics market. From the end of the Hadoop hype cycle, to the new cloud reality, and even the opportunity to help the many data science and machine learning projects move from labs to production. So let's talk about these trends in the context of infrastructure. And in particular, look at why a modern storage platform is relevant as organizations take on the challenges and opportunities associated with these trends. The answer is the Hadoop hype cycles left a lot of data in HDFS data lakes, or reservoirs or swamps depending upon the level of the data hygiene. But without the ability to get the value that was promised from Hadoop as a platform rather than a distributed file store. And when we combine that data with the massive volume of data in Cloud Object Storage, we find ourselves with a lot of data and a lot of silos, but without a way to unify that data and find value in it. Now when you look at the infrastructure data lakes are traditionally built on, it is often direct attached storage or data. The approach that Hadoop took when it entered the market was primarily bound by the limits of networking and storage technologies. One gig ethernet and slower spinning disk. But today, those barriers do not exist. And all FlashStorage has fundamentally transformed how data is accessed, managed and leveraged. The need for local data storage for significant volumes of data has been largely mitigated by the performance increases afforded by all Flash. At the same time, organizations can achieve superior economies of scale with that segregation of compute and storage. With compute and storage, you don't always scale in lockstep. Would you want to add an engine to the train every time you add another boxcar? Probably not. But from a Pure Storage perspective, FlashBlade is uniquely architected to allow customers to achieve better resource utilization for compute and storage, while at the same time, reducing complexity that has arisen from the siloed nature of the original big data solutions. The second and equally important recent trend we see is something I'll call cloud reality. The public clouds made a lot of promises and some of those promises were delivered. But cloud economics, especially usage based and elastic scaling, without the control that many companies need to manage the financial impact is causing a lot of issues. In addition, the risk of vendor lock-in from data egress, charges, to integrated software stacks that can't be moved or deployed on-premise is causing a lot of organizations to back off the all the way non-cloud strategy, and move toward hybrid deployments. Which is kind of funny in a way because it wasn't that long ago that there was a lot of talk about no more data centers. And for example, one large retailer, I won't name them, but I'll admit they are my favorites. They several years ago told us they were completely done with on-prem storage infrastructure, because they were going 100% to the cloud. But they just deployed FlashBlade for their data pipelines, because they need predictable performance at scale. And the all cloud TCO just didn't add up. Now, that being said, well, there are certainly challenges with the public cloud. It has also brought some things to the table that we see most organizations wanting. First of all, in a lot of cases applications have been built to leverage object storage platforms like S3. So they need that object protocol, but they may also need it to be fast. And the said object may be oxymoron only a few years ago, and this is an area of the market where Pure and FlashBlade have really taken a leadership position. Second, regardless of where the data is physically stored, organizations want the best elements of a cloud experience. And for us, that means two main things. Number one is simplicity and ease of use. If you need a bunch of storage experts to run the system, that should be considered a bug. The other big one is the consumption model. The ability to pay for what you need when you need it, and seamlessly grow your environment over time totally nondestructively. This is actually pretty huge and something that a lot of vendors try to solve for with finance programs. But no finance program can address the pain of a forklift upgrade, when you need to move to next gen hardware. To scale nondestructively over long periods of time, five to 10 years plus is a crucial architectural decisions need to be made at the outset. Plus, you need the ability to pay as you use it. And we offer something for FlashBlade called Pure as a Service, which delivers exactly that. The third cloud characteristic that many organizations want is the option for hybrid. Even if that is just a DR site in the cloud. In our case, that means supporting appplication of S3, at the AWS. And the final trend, which to me represents the biggest opportunity for all of us, is the need to help the many data science and machine learning projects move from labs to production. This means bringing all the machine learning functions and model training to the data, rather than moving samples or segments of data to separate platforms. As we all know, machine learning needs a ton of data for accuracy. And there is just too much data to retrieve from the cloud for every training job. At the same time, predictive analytics without accuracy is not going to deliver the business advantage that everyone is seeking. You can kind of visualize data analytics as it is traditionally deployed as being on a continuum. With that thing, we've been doing the longest, data warehousing on one end, and AI on the other end. But the way this manifests in most environments is a series of silos that get built up. So data is duplicated across all kinds of bespoke analytics and AI, environments and infrastructure. This creates an expensive and complex environment. So historically, there was no other way to do it because some level of performance is always table stakes. And each of these parts of the data pipeline has a different workload profile. A single platform to deliver on the multi dimensional performances, diverse set of applications required, that didn't exist three years ago. And that's why the application vendors pointed you towards bespoke things like DAS environments that we talked about earlier. And the fact that better options exists today is why we're seeing them move towards supporting this disaggregation of compute and storage. And when it comes to a platform that is a better option, one with a modern architecture that can address the diverse performance requirements of this continuum, and allow organizations to bring a model to the data instead of creating separate silos. That's exactly what FlashBlade is built for. Small files, large files, high throughput, low latency and scale to petabytes in a single namespace. And this is importantly a single rapid space is what we're focused on delivering for our customers. At Pure, we talk about it in the context of modern data experience because at the end of the day, that's what it's really all about. The experience for your teams in your organization. And together Pure Storage and Vertica have delivered that experience to a wide range of customers. From a SaaS analytics company, which uses Vertica on FlashBlade to authenticate the quality of digital media in real time, to a multinational car company, which uses Vertica on FlashBlade to make thousands of decisions per second for autonomous cars, or a healthcare organization, which uses Vertica on FlashBlade to enable healthcare providers to make real time decisions that impact lives. And I'm sure you're all looking forward to hearing from John Yavanovich from AT&T. To hear how he's been doing this with Vertica and FlashBlade as well. He's coming up soon. We have been really excited to build this partnership with Vertica. And we're proud to provide the only on-premise storage platform validated with Vertica Eon Mode. And deliver this modern data experience to our customers together. Thank you all so much for joining us today. >> Joy: Amy, thank you so much for your time and your insights. Modern infrastructure is key to modern analytics, especially as organizations leverage next generation data center architectures, and object storage for their on-premise data centers. Now, I'm delighted to introduce our last speaker in our Vertica Big Data Conference Keynote, John Yovanovich, Director of IT for AT&T. Vertica is so proud to serve AT&T, and especially proud of the harmonious impact we are having in partnership with Pure Storage. John, welcome to the Virtual Vertica BDC. >> John: Thank you joy. It's a pleasure to be here. And I'm excited to go through this presentation today. And in a unique fashion today 'cause as I was thinking through how I wanted to present the partnership that we have formed together between Pure Storage, Vertica and AT&T, I want to emphasize how well we all work together and how these three components have really driven home, my desire for a harmonious to use your word relationship. So, I'm going to move forward here and with. So here, what I'm going to do the theme of today's presentation is the Pure Vertica Symphony live at AT&T. And if anybody is a Westworld fan, you can appreciate the sheet music on the right hand side. What we're going to what I'm going to highlight here is in a musical fashion, is how we at AT&T leverage these technologies to save money to deliver a more efficient platform, and to actually just to make our customers happier overall. So as we look back, and back as early as just maybe a few years ago here at AT&T, I realized that we had many musicians to help the company. Or maybe you might want to call them data scientists, or data analysts. For the theme we'll stay with musicians. None of them were singing or playing from the same hymn book or sheet music. And so what we had was many organizations chasing a similar dream, but not exactly the same dream. And, best way to describe that is and I think with a lot of people this might resonate in your organizations. How many organizations are chasing a customer 360 view in your company? Well, I can tell you that I have at least four in my company. And I'm sure there are many that I don't know of. That is our problem because what we see is a repetitive sourcing of data. We see a repetitive copying of data. And there's just so much money to be spent. This is where I asked Pure Storage and Vertica to help me solve that problem with their technologies. What I also noticed was that there was no coordination between these departments. In fact, if you look here, nobody really wants to play with finance. Sales, marketing and care, sure that you all copied each other's data. But they actually didn't communicate with each other as they were copying the data. So the data became replicated and out of sync. This is a challenge throughout, not just my company, but all companies across the world. And that is, the more we replicate the data, the more problems we have at chasing or conquering the goal of single version of truth. In fact, I kid that I think that AT&T, we actually have adopted the multiple versions of truth, techno theory, which is not where we want to be, but this is where we are. But we are conquering that with the synergies between Pure Storage and Vertica. This is what it leaves us with. And this is where we are challenged and that if each one of our siloed business units had their own stories, their own dedicated stories, and some of them had more money than others so they bought more storage. Some of them anticipating storing more data, and then they really did. Others are running out of space, but can't put anymore because their bodies aren't been replenished. So if you look at it from this side view here, we have a limited amount of compute or fixed compute dedicated to each one of these silos. And that's because of the, wanting to own your own. And the other part is that you are limited or wasting space, depending on where you are in the organization. So there were the synergies aren't just about the data, but actually the compute and the storage. And I wanted to tackle that challenge as well. So I was tackling the data. I was tackling the storage, and I was tackling the compute all at the same time. So my ask across the company was can we just please play together okay. And to do that, I knew that I wasn't going to tackle this by getting everybody in the same room and getting them to agree that we needed one account table, because they will argue about whose account table is the best account table. But I knew that if I brought the account tables together, they would soon see that they had so much redundancy that I can now start retiring data sources. I also knew that if I brought all the compute together, that they would all be happy. But I didn't want them to tackle across tackle each other. And in fact that was one of the things that all business units really enjoy. Is they enjoy the silo of having their own compute, and more or less being able to control their own destiny. Well, Vertica's subclustering allows just that. And this is exactly what I was hoping for, and I'm glad they've brought through. And finally, how did I solve the problem of the single account table? Well when you don't have dedicated storage, and you can separate compute and storage as Vertica in Eon Mode does. And we store the data on FlashBlades, which you see on the left and right hand side, of our container, which I can describe in a moment. Okay, so what we have here, is we have a container full of compute with all the Vertica nodes sitting in the middle. Two loader, we'll call them loader subclusters, sitting on the sides, which are dedicated to just putting data onto the FlashBlades, which is sitting on both ends of the container. Now today, I have two dedicated storage or common dedicated might not be the right word, but two storage racks one on the left one on the right. And I treat them as separate storage racks. They could be one, but i created them separately for disaster recovery purposes, lashing work in case that rack were to go down. But that being said, there's no reason why I'm probably going to add a couple of them here in the future. So I can just have a, say five to 10, petabyte storage, setup, and I'll have my DR in another 'cause the DR shouldn't be in the same container. Okay, but I'll DR outside of this container. So I got them all together, I leveraged subclustering, I leveraged separate and compute. I was able to convince many of my clients that they didn't need their own account table, that they were better off having one. I eliminated, I reduced latency, I reduced our ticketing I reduce our data quality issues AKA ticketing okay. I was able to expand. What is this? As work. I was able to leverage elasticity within this cluster. As you can see, there are racks and racks of compute. We set up what we'll call the fixed capacity that each of the business units needed. And then I'm able to ramp up and release the compute that's necessary for each one of my clients based on their workloads throughout the day. And so while they compute to the right before you see that the instruments have already like, more or less, dedicated themselves towards all those are free for anybody to use. So in essence, what I have, is I have a concert hall with a lot of seats available. So if I want to run a 10 chair Symphony or 80, chairs, Symphony, I'm able to do that. And all the while, I can also do the same with my loader nodes. I can expand my loader nodes, to actually have their own Symphony or write all to themselves and not compete with any other workloads of the other clusters. What does that change for our organization? Well, it really changes the way our database administrators actually do their jobs. This has been a big transformation for them. They have actually become data conductors. Maybe you might even call them composers, which is interesting, because what I've asked them to do is morph into less technology and more workload analysis. And in doing so we're able to write auto-detect scripts, that watch the queues, watch the workloads so that we can help ramp up and trim down the cluster and subclusters as necessary. There has been an exciting transformation for our DBAs, who I need to now classify as something maybe like DCAs. I don't know, I have to work with HR on that. But I think it's an exciting future for their careers. And if we bring it all together, If we bring it all together, and then our clusters, start looking like this. Where everything is moving in harmonious, we have lots of seats open for extra musicians. And we are able to emulate a cloud experience on-prem. And so, I want you to sit back and enjoy the Pure Vertica Symphony live at AT&T. (soft music) >> Joy: Thank you so much, John, for an informative and very creative look at the benefits that AT&T is getting from its Pure Vertica symphony. I do really like the idea of engaging HR to change the title to Data Conductor. That's fantastic. I've always believed that music brings people together. And now it's clear that analytics at AT&T is part of that musical advantage. So, now it's time for a short break. And we'll be back for our breakout sessions, beginning at 12 pm Eastern Daylight Time. We have some really exciting sessions planned later today. And then again, as you can see on Wednesday. Now because all of you are already logged in and listening to this keynote, you already know the steps to continue to participate in the sessions that are listed here and on the previous slide. In addition, everyone received an email yesterday, today, and you'll get another one tomorrow, outlining the simple steps to register, login and choose your session. If you have any questions, check out the emails or go to www.vertica.com/bdc2020 for the logistics information. There are a lot of choices and that's always a good thing. Don't worry if you want to attend one or more or can't listen to these live sessions due to your timezone. All the sessions, including the Q&A sections will be available on demand and everyone will have access to the recordings as well as even more pre-recorded sessions that we'll post to the BDC website. Now I do want to leave you with two other important sites. First, our Vertica Academy. Vertica Academy is available to everyone. And there's a variety of very technical, self-paced, on-demand training, virtual instructor-led workshops, and Vertica Essentials Certification. And it's all free. Because we believe that Vertica expertise, helps everyone accelerate their Vertica projects and the advantage that those projects deliver. Now, if you have questions or want to engage with our Vertica engineering team now, we're waiting for you on the Vertica forum. We'll answer any questions or discuss any ideas that you might have. Thank you again for joining the Vertica Big Data Conference Keynote Session. Enjoy the rest of the BDC because there's a lot more to come

hi everybody and welcome to this cube special presentation of the verdict of virtual Big Data conference the cube is running in parallel with day 1 and day 2 of the verdict big data event by the way the cube has been at every single big data event and it's our pleasure to be here in the virtual / digital event as well Gabriel Chapman is here is the director of flash blade product solutions marketing at pure storage gave great to see you thanks for coming on great to see you - 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 and hopefully we'll be able to meet it accelerate at some point you cheer or one of the 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 Vertica announced a partnership I remember a joint being ran up to me and said hey you got to check this out the separation of Butte and storage by a Eon mode now available on flash played so and and I believe still the only company that can support that separation and independent scaling both on permit 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 in kind of a larger shift when it comes to modern analytics platforms towards moving away from the the traditional you know Hadoop type architecture where we were doing on and leveraging a lot of direct attached 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 play platform ended up being a great solution to support Vertica in their transition to Eon mode leveraging is essentially as an s3 object store okay so let's let's circle back on that you guys in your in your announcement of a 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 it's supposed to yeah III 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 you know it's 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 of best file storage place in realistically would we start to see more organizations start to look at building solutions that leverage cloud storage characteristics but doing so on prem or multitude different reasons we've built a platform that really addresses a lot of those needs around simplicity around you know making things assure that you know vast 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 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 Flash blade and then better understand the fit for analytic databases generally but specifically Vertica so it is a blade so you got compute and a network included it's a key value store based system so you're talking about scale out unlike unlike viewers sort of you know initial products which were scale up and so I want to under in as a fabric base system I want to understand what that all mean 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 meaning we call it a flash blade 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 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 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 and scale from essentially seven blades right now to a hundred and fifty that's that's the kind of scale that customers are looking for especially as we start to address these larger analytic spools they have multi petabyte datasets 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 and Christopher's been you know attacked by some of the competitors is not having a scale out I asked him his thoughts on that he said well first of all our Flash blade is scale-out and he said look anything that that that adds the 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 you know realistically I think you know that that approach is better when we're starting to learn to work with large unstructured data sets I mean flash plays uniquely architected to allow customers to achieve you know a superior resource utilization for compute and storage well at the same time you know reducing significantly the complexity that is arisen around these kind of bespoke or siloed nature of big data and analytic 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 and unstructured data and and for some organizations the importance is bringing that on Prem I mean we do seek repatriation that coming on on for a lot of organizations as these data egress charges continue to expand and grow and then organizations that want even higher performance in the 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 standpoint we still want to be able to scale the way we scale on 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 to 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 that do that with inside their own data center yes are you talking about the trends earlier you had these cloud native databases that allowed the scaling of compute and storage independently of Vertica 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 are that are non Barney deal like in other words any specific you know engineering you know other go to market programs can you talk about that a little bit yeah it's it's it's more than just you know I then what we consider a channel meet in the middle or you know that Barney type of deal it's the realistically you know we've done some first with Vertica that I think are really important if they think you look at the architecture and how we do have 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 vertigo they're very high on or very excited about the partnership because it often it opens up a new set of opportunities for their customers to to leverage Eon mode and you know get into some of the the nuanced aspects of how they leverage the depot for Depot with inside each individual compute node and adjustments with inside there I reach additional performance gains for customers on Prem and at the same time for them there'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 and you know do white papers and all of the the traditional marketing aspects that we bring devote to the market and then you know joint selling opportunities as exists where they are and so that's realistically I think like any other organization that's going to market with a partner or an ISP 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 okay you know of course he used to be a Gartner analyst and you go over to the vendor side now but as but as it but as a gardener analyst you're obviously objective you see it all you know well there's a lot of ways to skin a cat there are there are there are strengths weaknesses opportunities threats etc for every vendor so you have you have Vertica who's got a very mature stack and and talking to a number of the customers out there we're 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 you know has to play its trump card which is look we've got a big on-premise state and we're gonna bring that you know 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 and 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 providing you know we're really proud to provide pretty much the only on Prem storage platform that's validated with the vertical yawn mode to deliver a modern data experience for our customers together you know it's it's that partnership that allows us to go into customers that on Prem space where I think that they're still you know not to say that not everybody wants to go 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 the 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 skills skill sets that are required to bring you know 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 for 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 that that better option when it comes to a modern architecture they can do it 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 flash Wood was built or it was built for a platform that can address small files or large files or high throughput high throughput low latency scale to petabytes in a single namespace in 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 namespace 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 verdict 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 in but take us through kind of what the discuss the customer discussions are like obviously you've got a big customer base you and Vertica that that's on prem that's the the the unique advantage of this but there are others it's not just the economics of the the granular scaling of compute and storage independently there are other aspects so to take us through that sort of a primary use case or use cases yeah you know I mean I can give you a couple customer examples and we have a large SAS analyst company which uses verdict on flash play to authenticate the quality of digital media in real time and you know then for them it makes a big difference is they're doing they're streaming and whatnot that they can they can fine tune and grandly control that so that's one aspect that that we get address we have a multi national car con company which uses verdict on flash blade to make thousands of decisions per second for autonomous vehicle decision-making trees that you know that's what really these new modern analytics platforms were built or there's another healthcare organization that uses Vertica on flash blade to enable healthcare providers to make decisions in real time the impact Ives especially when we start to look at and you know the current state of affairs with Kovac in the coronavirus you know those types of technologies are really going to help us kind of get love and and help lower and been you know bend that curve downward so you know there's all these different areas where we can address the goals and the achievements that we're trying to look bored with with real-time analytic decision making tools like Berta and you know realistically as we have these conversations with customers they're looking to get beyond the ability of just you know you know a data scientist or a data architect looking to just kind of drive in 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 you know spinning up you know compute and storage a little bit of networking in there seems like the the a next a new workload that's clearly emerging is you've got and it started with the cloud databases but then bringing in you know 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 you know driven by Hadoop bringing machine intelligence into the equation scaling it with either cloud public cloud or bringing that cloud experience on prams scale you know across your organizations and across your partner network that really is a new emerging work load do you see that and maybe talk a little bit about you know 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 you know especially when it comes to you know 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 you know it derived a significant value from that they do want to be able to control more characteristics right and we know machine learning you know needs to needs to learn from a massive amounts of data to provide accuracy there's just too much data to 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 you know we see this the visualization of data analytics is traditionally deployed as being on a continuum with you know the things that we've been doing in the long you know in the past you know with data warehousing data lakes AI on the other end but but this way we're starting to manifest it in organizations that are looking towards you know getting more utility and better you know elasticity out of the data that they are working for so they're not looking to just build ups you know silos of bespoke AI environments they're looking to leverage you know a platform that can allow them to you know do a I for one thing machine learning for another leverage multiple protocols to access that data because the tools are so much different 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 environments I think there's gonna be a big growth area in the coming years gay ball I wish we were in Boston together you would have painted your little corner of Boston Orange I know that you guys are sharing but I really appreciate you coming on the cube wall-to-wall coverage two days at the vertical Vertica virtual big data conference keep you right there but right back right after this short break [Music]

hi everybody and welcome to this cube special presentation of the verdict of virtual Big Data conference the cube is running in parallel with day 1 and day 2 of the verdict big data event by the way the cube has been at every single big data event and it's our pleasure to be here in the virtual / digital event as well Gabriel Chapman is here is the director of flash blade product solutions marketing at pure storage gave great to see you thanks for coming on great to see you - 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 and hopefully we'll be able to meet it accelerate at some point you cheer or one of the 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 Vertica announced a partnership I remember a joint being ran up to me and said hey you got to check this out the separation of Butte and storage by a Eon mode now available on flash played so and and I believe still the only company that can support that separation and independent scaling both on permit 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 in kind of a larger shift when it comes to modern analytics platforms towards moving away from the the traditional you know Hadoop type architecture where we were doing on and leveraging a lot of direct attached 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 play platform ended up being a great solution to support Vertica in their transition to Eon mode leveraging is essentially as an s3 object store okay so let's let's circle back on that you guys in your in your announcement of a 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 it's supposed to yeah III 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 you know it's 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 of best file storage place in realistically would we start to see more organizations start to look at building solutions that leverage cloud storage characteristics but doing so on prem or multitude different reasons we've built a platform that really addresses a lot of those needs around simplicity around you know making things assure that you know vast 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 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 Flash blade and then better understand the fit for analytic databases generally but specifically Vertica so it is a blade so you got compute and a network included it's a key value store based system so you're talking about scale out unlike unlike viewers sort of you know initial products which were scale up and so I want to under in as a fabric base system I want to understand what that all mean 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 meaning we call it a flash blade 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 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 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 and scale from essentially seven blades right now to a hundred and fifty that's that's the kind of scale that customers are looking for especially as we start to address these larger analytic spools they have multi petabyte datasets 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 and Christopher's been you know attacked by some of the competitors is not having a scale out I asked him his thoughts on that he said well first of all our Flash blade is scale-out and he said look anything that that that adds the 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 you know realistically I think you know that that approach is better when we're starting to learn to work with large unstructured data sets I mean flash plays uniquely architected to allow customers to achieve you know a superior resource utilization for compute and storage well at the same time you know reducing significantly the complexity that is arisen around these kind of bespoke or siloed nature of big data and analytic 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 and unstructured data and and for some organizations the importance is bringing that on Prem I mean we do seek repatriation that coming on on for a lot of organizations as these data egress charges continue to expand and grow and then organizations that want even higher performance in the 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 standpoint we still want to be able to scale the way we scale on 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 to 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 that do that with inside their own data center yes are you talking about the trends earlier you had these cloud native databases that allowed the scaling of compute and storage independently of Vertica 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 are that are non Barney deal like in other words any specific you know engineering you know other go to market programs can you talk about that a little bit yeah it's it's it's more than just you know I then what we consider a channel meet in the middle or you know that Barney type of deal it's the realistically you know we've done some first with Vertica that I think are really important if they think you look at the architecture and how we do have 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 vertigo they're very high on or very excited about the partnership because it often it opens up a new set of opportunities for their customers to to leverage Eon mode and you know get into some of the the nuanced aspects of how they leverage the depot for Depot with inside each individual compute node and adjustments with inside there I reach additional performance gains for customers on Prem and at the same time for them there'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 and you know do white papers and all of the the traditional marketing aspects that we bring devote to the market and then you know joint selling opportunities as exists where they are and so that's realistically I think like any other organization that's going to market with a partner or an ISP 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 okay you know of course he used to be a Gartner analyst and you go over to the vendor side now but as but as it but as a gardener analyst you're obviously objective you see it all you know well there's a lot of ways to skin a cat there are there are there are strengths weaknesses opportunities threats etc for every vendor so you have you have Vertica who's got a very mature stack and and talking to a number of the customers out there we're 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 you know has to play its trump card which is look we've got a big on-premise state and we're gonna bring that you know 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 and 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 providing you know we're really proud to provide pretty much the only on Prem storage platform that's validated with the vertical yawn mode to deliver a modern data experience for our customers together you know it's it's that partnership that allows us to go into customers that on Prem space where I think that they're still you know not to say that not everybody wants to go 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 the 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 skills skill sets that are required to bring you know 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 for 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 that that better option when it comes to a modern architecture they can do it 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 flash Wood was built or it was built for a platform that can address small files or large files or high throughput high throughput low latency scale to petabytes in a single namespace in 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 namespace 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 verdict 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 in but take us through kind of what the discuss the customer discussions are like obviously you've got a big customer base you and Vertica that that's on prem that's the the the unique advantage of this but there are others it's not just the economics of the the granular scaling of compute and storage independently there are other aspects so to take us through that sort of a primary use case or use cases yeah you know I mean I can give you a cup of customer examples and we have a large SAS analyst company which uses verdict on flash play to authenticate the quality of digital media in real time and you know then for them it makes a big difference is they're doing they're streaming and whatnot that they can they can fine tune and grandly control that so that's one aspect that we get address we have a multi national car con company which uses verdict on flash blade to make thousands of decisions per second for autonomous vehicle decision-making trees that you know that's what really these new modern analytics platforms were built or there's another healthcare organization that uses Vertica on flash blade to enable healthcare providers to make decisions in real time the impact Ives especially when we start to look at and you know the current state of affairs with Kovac in the coronavirus you know those types of technologies are really going to help us kind of get love and and help lower and been you know bend that curve downward so you know there's all these different areas where we can address the goals and the achievements that we're trying to look bored with with real-time analytic decision making tools like Berta and you know realistically as we have these conversations with customers they're looking to get beyond the ability of just you know you know a data scientist or a data architect looking to just kind of drive in 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 you know spinning up you know compute and storage a little bit of networking in there seems like the the a next a new workload that's clearly emerging is you've got and it started with the cloud databases but then bringing in you know 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 you know driven by Hadoop bringing machine intelligence into the equation scaling it with either cloud public cloud or bringing that cloud experience on prams scale you know across your organizations and across your partner network that really is a new emerging work load do you see that and maybe talk a little bit about you know 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 you know especially when it comes to you know 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 you know it derived a significant value from that they do want to be able to control more characteristics right and we know machine learning you know needs to needs to learn from a massive amounts of data to provide accuracy there's just too much data to 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 you know we see this the visualization of data analytics is traditionally deployed as being on a continuum with you know the things that we've been doing in the long you know in the past you know with data warehousing data lakes AI on the other end but but this way we're starting to manifest it in organizations that are looking towards you know getting more utility and better you know elasticity out of the data that they are working for so they're not looking to just build ups you know silos of bespoke AI environments they're looking to leverage you know a platform that can allow them to you know do a I for one thing machine learning for another leverage multiple protocols to access that data because the tools are so much different 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 environments I think there's gonna be a big growth area in the coming years gay ball I wish we were in Boston together you would have painted your little corner of Boston Orange I know that you guys are sharing but I really appreciate you coming on the cube wall-to-wall coverage two days at the vertical Vertica virtual big data conference keep you right there but right back right after this short break [Music]

six five four three two one hi everybody and welcome to this cube special presentation of the verdict of virtual big data conference the cube is running in parallel with day 1 and day 2 of the verdict the big data event by the way the cube has been at every single big data event and it's our pleasure to be here in the virtual / digital event as well Gabriel Chapman is here is the director of flash blade product solutions marketing at pure storage Gabe great to see you thanks for coming on great to see you - 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 and hopefully we'll be able to meet it accelerate at some point you cheer or one of the 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 Vertica announced a partnership I remember a joint being ran up to me and said hey you got to check this out the separation of Butte and storage by a Eon mode now available on flash played so and and I believe still the only company that can support that separation and independent scaling both on prime and in the cloud so gave I want to ask you what were the trends in analytical database and plowed 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 the traditional you know Hadoop type architecture where we were doing on and leveraging a lot of direct mass 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 Vertica in their transition to Eon mode leveraging >> essentially as an s3 object store okay so let's let's circle back on that you guys in your in your announcement of a 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 I defend that it's supposed to yeah I 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 as we've developed flash Wade as a platform and keep in mind it's been a product that's been around for over three years now and has you know it's 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 of 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 assure that you know vast 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 day day 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 Flash blade and then better understand the fit for analytic databases generally but specifically for Vertica so it is a blade so you got compute and a network included it's a key value store based system so you're talking about scale out unlike unlike viewers sort of you know initial products which were scale up and so I want to as a fabric base system I want to understand what that all mean 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 mean we call it a flash blade 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 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 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 and scale from essentially seven blades right now to a hundred and fifty that's that's the kind of scale that customers are looking for especially as we start to address these larger analytics pools mayo multi petabyte datasets 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 I saw you interviewed cause last September and accelerate and and Christopher's 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 flash plate is scale out he said look anything that that that adds the 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 you know realistically I think you know that that approach is better when we're starting to learn to work with large unstructured data sets I mean flash plays uniquely architected to allow customers to achieve you know a superior resource utilization for compute and storage well at the same time you know reducing significantly the complexity that is arisen around these kind of bespoke or siloed nature of big data and analytic 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 and unstructured data and and for some organizations the importance is bringing that on Prem I mean we do seek 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 in the 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 standpoint we still want to be able to scale the way we scale on 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 to 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 that do that with inside their own data center so you're 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 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 are 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 it's it's more than just you know I then 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 Vertica that I think are really important if they think you look at the architecture and how we do 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 vertigo they're very high on they're very excited about the partnership because it often it opens up a new set of opportunities for their customers to to leverage Eon mode and you know get into some of the the nuanced aspects of how they leverage the Depot or Depot with inside each individual compute node and adjustments with inside there I reach additional performance gains for customers on Prem and it's the same time for them there'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 and you know do white papers and all of the the traditional marketing aspects that we bring into 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 or an ISP that they have a strong partnership with you'll continue to see us you know talking about our shows mutually beneficial relationships and the solutions that we're bringing it to the market okay you know of course he used to be a Gartner analyst and you go over to the vendor side now but as but as it but as a gardener 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 there are there are strengths weaknesses opportunities threats etc for every vendor so you have you have Vertica who's got a very mature stack and 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 you know has to play its trump card which is look we've got a big on-premise state and we're gonna bring that you know Eon capability both on Prem and we're embracing the cloud now they're obviously having 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 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 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 providing you know we're really proud to provide pretty much the only on Prem storage platform that's validated with the vertical Aeon mode to deliver a modern data experience for our customers together you know it's it's that partnership that allows us to go into customers that on Prem space where I think that they're still you know not to say that not everybody wants to go the cloud I think there's aspects and then solutions that work 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 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 you 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 skills skill sets that are required to bring you know 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 that that better option when it comes to a modern architecture they can do it you know we can deliver and address a diverse set of performance requirements and allowed 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 Wood was built for it was built for a platform that can address small files or large files or high throughput high throughput low latency scale to petabytes in a single namespace in 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 namespace 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 verdict technology I'm interested in exploring the 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 in but take us through kind of what to discuss the customer discussions are like obviously you've got a big customer base you and Vertica 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 so to take us through that sort of a primary use case or use cases yeah you know I mean I can give you a couple customer examples and we have a large SAS analyst company which uses verdict on flash play to authenticate the quality of digital media in real time and you know then for them it makes a big difference is they're doing they're streaming and whatnot that they can they can fine tune and grandly control that so that's one aspect that we need to address we have a multi national car company which uses verdict 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 there's another healthcare organization that uses Vertica on flash blade to enable healthcare providers to make decisions in real time the impact vibes especially when we start to look at and you know the current state of affairs little Co vid and the coronavirus you know those types of technologies are really going to help us kind of get love and and help lower and been you know bend that curve downward so you know there's all these different areas where we can address the goals and the achievements that we're trying to look bored with with real-time analytic decision making tools like birth and you know realistically as we have these conversations with customers they're looking to get beyond the ability of just you know you know a data scientist or a data architect looking to just kind of drive in information you know you know I'm gonna set this model up and we'll come back in a day now we need to make these and the performs characteristics the Aeon mode and vertical allows for can get us towards this almost near real-time analytics decision-making process and that the customers and that's the kind of conversations that we're having with customers who really need to be able to turn this around very quickly instead of waiting well I think you're hitting on something that is actually pretty relevant and that is that near real-time analytic you know database 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 you know spinning up you know compute and storage a little bit of networking in there seems like the the a next a 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 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 you know driven by Hadoop bringing machine intelligence into the equation scaling it with either cloud public cloud or bringing that cloud experience on-premise scale you know across your organizations and across your partner network that really is a new emerging work load do you see that and maybe talk a little bit about you know 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 you know especially when it comes to you know 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 you know that derived a significant value from that they do want to be able to control more characteristics right and we know machine learning you know needs to needs to learn from a massive amounts of data to provide accuracy there's just too much data to 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 you know we see this the visualization of data analytics is traditionally deployed as being on a continuum with you know the things that we've been doing in the long you know in the past you know with data warehousing data lakes AI on the other end but but this way we're starting to manifest it in organizations that are looking towards you know getting more utility and better you know elasticity out of the data that they are working for so they're not looking to just build ups you know silos of bespoke AI environments they're looking to leverage you know a platform that can allow them to you know do a I for one thing machine learning for another leverage multiple protocols to access that data because the tools are so much different 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 environments I think there's gonna be a big growth area in the coming years Gabe well I wish we were in Boston together you would have painted your little corner of Boston Orange I know that you guys are sure but I really appreciate you coming on the cube and thank you very much have a great day you too okay thank you everybody for watching this is the cubes coverage wall-to-wall coverage two days of the vertical Vertica virtual Big Data conference keep her at their very back right after this short break

>> from Austin, Texas. It's Theo Cube, covering pure storage. Accelerate 2019. Brought to you by pure storage. >> Welcome back to the Cube. Lisa Martin Day Volante is my co host were a pure accelerate 2019 in Austin, Texas. A couple of guests joining us. Next. Please welcome Barack elected director product management for slunk. Welcome back to the Cube. Thank you. And guess who's back. Von Stewart. V. P. A. Technology from pure Avon. Welcome back. >> Hey, thanks for having us guys really excited about this topic. >> We are too. All right, so But we'll start with you. Since you're so excited in your nice orange pocket square is peeking out of your jacket there. Talk about the Splunk, your relationship. Long relationship, new offerings, joint value. What's going on? >> Great set up. So Splunk impure have had a long relationship around accelerating customers analytics The speed at which they can get their questions answered the rate at which they could ingest data right to build just more sources. Look at more data, get faster time to take action. However, I shouldn't be leading this conversation because Split Split has released a new architecture, a significant evolution if you will from the traditional Splunk architectural was built off of Daz and a shared nothing architecture. Leveraging replicas, right? Very similar what you'd have with, like, say, in H D. F s Work it load or H c. I. For those who aren't in the analytic space, they've released the new architecture that's disaggregated based off of cashing and an object store construct called Smart Store, which Broth is the product manager for? >> All right, tell us about that. >> So we release a smart for the future as part of spunk Enterprise. $7 to about a near back back in September Timeframe. Really Genesis or Strong Smart Strong goes back to the key customer problem that we were looking to solve. So one of our customers, they're already ingesting a large volume of data, but the need to retain the data for twice, then one of Peter and in today's architecture, what it required was them to kind of lean nearly scale on the amount of hardware. What we realized it. Sooner or later, all customers are going to run into this issue. But if they want in just more data or reading the data for longer periods, of time, they're going to run into this cost ceiling sooner or later on. The challenge is that into this architecture, today's distributes killer dark picture that we have today, which of all, about 10 years back, with the evolution of the Duke in this particular architecture, the computer and story Jacqui located. And because computer storage acqua located, it allows us to process large volumes of data. But if you look at the demand today, we can see that the demand for storage or placing the demand for computer So these are, too to directly opposite trans that we're seeing in the market space. If you need to basically provide performance at scale, there needs to be a better model. They need a better solution than what we had right now. So that's the reason we basically brought Smart store on denounced availability last September. What's Marceau brings to the table is that a D couples computer and storage, So now you can scale storage independent of computers, so if you need more storage or if you need to read in for longer periods of time, you can just kill independent on the storage and with level age, remote object stores like Bill Flash bid to provide that data depository. But most of your active data said still decides locally on the indexers. So what we did was basically broke the paradigm off computer storage location, and we had a small twist. He said that now the computer stories can be the couple, but you bring comfort and stories closer together only on demand. So that means that when you were running a radio, you know, we're running a search, and whenever the data is being looked for that only when we bring the data together. The other key thing that we do is we have an active data set way ensure that the smart store has ah, very powerful cash manager that allows that ensures that the active data set is always very similar to the time when your laptop, the night when your laptop has active data sets always in the cash always on memory. So very similar to that smarts for cash allows you to have active data set always locally on the index. Start your search performance is not impact. >> Yes, this problem of scaling compute and storage independently. You mentioned H. D. F s you saw it early on there. The hyper converged guys have been trying to solve this problem. Um, some of the database guys like snowflakes have solved it in the cloud. But if I understand correctly, you're doing this on Prem. >> So we're doing this board an on Prem as well as in Cloud. So this smart so feature is already available on tramp were also already using a host all off our spun cloud deployments as well. It's available for customers who want obviously deploy spunk on AWS as well. >> Okay, where do you guys fit in? So we >> fit in with customers anywhere from on the hate say this way. But on the small side, at the hundreds of terabytes up into the tens and hundreds of petabytes side. And that's really just kind of shows the pervasiveness of Splunk both through mid market, all the way up through the through the enterprise, every industry and every vertical. So where we come in relative to smart store is we were a coat co developer, a launch partner. And because our object offering Flash Blade is a high performance object store, we are a little bit different than the rest of the Splunk s story partner ecosystem who have invested in slow more of an archive mode of s tree right, we have always been designed and kind of betting on the future would be based on high performance, large scale object. And so we believe smart store is is a ah, perfect example, if you will, of a modern analytics platform. When you look at the architecture with smart store as brush here with you, you want to suffice a majority of your queries out of cash because the performance difference between reading out a cash that let's say, that's NAND based or envy. Emmy based or obtain, if you will. When you fall, you have to go read a data data out of the Objects store, right. You could have a significant performance. Trade off wean mix significantly minimized that performance drop because you're going to a very high bandwith flash blade. We've done comparison test with other other smart store search results have been published in other vendors, white papers and we show Flash blade. When we run the same benchmark is 80 times faster and so what you can now have without architecture is confidence that should you find yourself in a compliance or regulatory issue, something like Maybe GDP are where you've got 72 hours to notify everyone who's been impacted by a breach. Maybe you've got a cybersecurity case where the average time to find that you've been penetrated occurs 206 days after the event. And now you gotta go dig through your old data illegal discovery, you know, questions around, you know, customer purchases, purchases or credit card payments. Any time where you've got to go back in the history, we're gonna deliver those results and order of magnitude faster than any other object store in the market today. That translates from ours. Today's days, two weeks, and we think that falls into our advantage. Almost two >> orders of magnitude. >> Can this be Flash Player >> at 80%? Sorry, Katie. Time 80 x. Yes, that's what I heard. >> Do you display? Consider what flashlight is doing here. An accelerant of spunk, workloads and customer environment. >> Definitely, because the forward with the smart, strong cash way allow high performance at scale for data that's recites locally in the cash. But now, by using a high performance object store like your flash played. Customers can expect the same high performing board when data is in the cash as well as invented sin. Remorseful >> sparks it. Interesting animal. Um, yeah, you have a point before we >> subjects. Well, I don't want to cut you off. It's OK. So I would say commenting on the performance is just part of the equation when you look at that, UM, common operational activities that a splitting, not a storage team. But a Splunk team has to incur right patch management, whether it's at the Splunk software, maybe the operating system, like linen store windows, that spunk is running on, or any of the other components on side on that platform. Patch Management data Re balancing cause it's unequal. Equally distributed, um, hardware refreshes expansion of the cluster. Maybe you need more computer storage. Those operations in terms of time, whether on smart store versus the classic model, are anywhere from 100 to 1000 times faster with smart store so you could have a deployment that, for example, it takes you two weeks to upgrade all the notes, and it gets done in four hours when it's on Smart store. That is material in terms of your operational costs. >> So I was gonna say, Splunk, we've been watching Splunk for a long time. There's our 10th year of doing the Cube, not our 10th anniversary of our 10th year. I think it will be our ninth year of doing dot com. And so we've seen Splunk emerged very cool company like like pure hip hip vibe to it. And back in the day, we talked about big data. Splunk never used that term, really not widely in its marketing. But then when we started to talk about who's gonna own the big data, that space was a cloud era was gonna be mad. We came back. We said, It's gonna be spunk and that's what's happened. Spunk has become a workload, a variety of workloads that has now permeated the organization, started with log files and security kind of kind of cumbersome. But now it's like everywhere. So I wonder if you could talk to the sort of explosion of Splunk in the workloads and what kind of opportunity this provides for you guys. >> So a very good question here, Right? So what we have seen is that spunk has become the de facto platform for all of one structure data as customers start to realize the value of putting their trying to Splunk on the watch. Your spunk is that this is like a huge differentiate of us. Monk is the read only skim on reed which allows you to basically put all of the data without any structure and ask questions on the flight that allows you to kind of do investigations in real time, be more reactive. What's being proactive? We be more proactive. Was being reactive scaleable platform the skills of large data volumes, highly available platform. All of that are the reason why you're seeing an increase that option. We see the same thing with all other customers as well. They start off with one data source with one use case and then very soon they realize the power of Splunk and they start to add additional use cases in just more and more data sources. >> But this no >> scheme on writer you call scheme on Reed has been so problematic for so many big data practitioners because it just became the state of swamp. >> That didn't >> happen with Splunk. Was that because you had very defined use cases obviously security being one or was it with their architectural considerations as well? >> They just architecture, consideration for security and 90 with the initial use cases, with the fact that the scheme on Reid basically gives open subject possibilities for you. Because there's no structure to the data, you can ask questions on the fly on. You can use that to investigate, to troubleshoot and allies and take remedial actions on what's happening. And now, with our new acquisitions, we have added additional capabilities where we can talk, orchestrate the whole Anto and flow with Phantom, right? So a lot of these acquisitions also helping unable the market. >> So we've been talking about TAM expansion all week. We definitely hit it with Charlie pretty hard. I have. You know, I think it's a really important topic. One of things we haven't hit on is tam expansion through partnerships and that flywheel effect. So how do you see the partners ship with Splunk Just in terms of supporting that tam expansion the next 10 years? >> So, uh, analytics, particularly log and Alex have really taken off for us in the last year. As we put more focus on it, we want to double down on our investments as we go through the end of this year and in the next year with with a focus on Splunk um, a zealous other alliances. We think we are in a unique position because the rollout of smart store right customers are always on a different scale in terms of when they want to adopt a new architecture right. It is a significant decision that they have to make. And so we believe between the combination of flash array for the hot tear and flash played for the cold is a nice way for customers with classic Splunk architecture to modernize their platform. Leverage the benefits of data reduction to drive down some of the cost leverage. The benefits of Flash to increase the rate at which they can ask questions and get answers is a nice stepping stone. And when customers are ready because Flash Blade is one of the few storage platforms in the market at this scale out band with optimized for both NFS and object, they can go through a rolling nondestructive upgrade to smart store, have you no investment protection, and if they can't repurpose that flash rate, they can use peers of service to have the flesh raise the hot today and drop it back off just when they're done within tomorrow. >> And what about C for, you know, big workloads, like like big data workloads. I mean, is that a good fit here? You really need to be more performance oriented. >> So flash Blade is is high bandwith optimization, which really is designed for workload. Like Splunk. Where when you have to do a sparse search, right, we'll find that needle in the haystack question, right? Were you breached? Where were you? Briefed. How were you breached? Go read as much data as possible. You've gotta in just all that data, back to the service as fast as you can. And with beast Cloud blocked, Teresi is really optimized it a tear to form of NAND for that secondary. Maybe transactional data base or virtual machines. >> All right, I want more, and then I'm gonna shut up sick. The signal FX acquisition was very interesting to me for a lot of reasons. One was the cloud. The SAS portion of Splunk was late to that game, but now you're sort of making that transition. You saw Tableau you saw Adobe like rip the band Aid Off and it was somewhat painful. But spunk is it. So I wonder. Any advice that you spend Splunk would have toe von as pure as they make that transition to that sass model. >> So I think definitely, I think it's going to be a challenging one, but I think it's a much needed one in there in the environment that we are in. The key thing is to always because two more focus and I'm sure that you're already our customer focus. But the key is key thing is to make sure that any service is up all the time on make sure that you can provide that up time, which is going to be crucial for beating your customers. Elise. >> That's good. That's good guidance. >> You >> just wanted to cover that for you favor of keeping you date. >> So you gave us some of those really impressive stats In terms of performance. >> They're almost too good to be true. >> Well, what's customer feedback? Let's talk about the real world when you're talking to customers about those numbers. What's the reaction? >> So I don't wanna speak for Broth, so I will say in our engagements within their customer base, while we here, particularly from customers of scale. So the larger the environment, the more aggressive they are to say they will adopt smart store right and on a more aggressive scale than the smaller environments. And it's because the benefits of operating and maintaining the indexer cluster are are so great that they'll actually turn to the stores team and say, This is the new architecture I want. This is a new storage platform and again. So when we're talking about patch management, cluster expansion Harbor Refresh. I mean, you're talking for a large sum. Large installs weeks, not two or 3 10 weeks, 12 weeks on end so it can be. You can reduce that down to a couple of days. It changes your your operational paradigm, your staffing. And so it has got high impact. >> So one of the message that we're hearing from customers is that it's far so they get a significant reduction in the infrastructure spent it almost dropped by 2/3. That's really significant file off our large customers for spending a ton of money on infrastructure, so just dropping that by 2/3 is a significant driver to kind of move too smart. Store this in addition to all the other benefits that get smart store with operational simplicity and the ability that it provides. You >> also have customers because of smart store. They can now actually bursts on demand. And so >> you can think of this and kind of two paradigms, right. Instead of >> having to try to avoid some of the operational pain, right, pre purchase and pre provisional large infrastructure and hope you fill it up. They could do it more of a right sides and kind of grow in increments on demand, whether it's storage or compute. That's something that's net new with smart store um, they can also, if they have ah, significant event occur. They can fire up additional indexer notes and search clusters that can either be bare metal v ems or containers. Right Try to, you know, push the flash, too. It's Max. Once they found the answers that they need gotten through. Whatever the urgent issues, they just deep provisionals assets on demand and return back down to a steady state. So it's very flexible, you know, kind of cloud native, agile platform >> on several guys. I wish we had more time. But thank you so much fun. And Deron, for joining David me on the Cube today and sharing all of the innovation that continues to come from this partnership. >> Great to see you appreciate it >> for Dave Volante. I'm Lisa Martin, and you're watching the Cube?