>>Hello, brilliant humans and welcome back to Las Vegas, Nevada, where we are live from the AWS Reinvent Show floor here with the cube. My name is Savannah Peterson, joined with Dave Valante, and we have a very exciting conversation with you. Two, two companies you may have heard of. We've got AWS and Red Hat in the house. Manu and Joel, thank you so much for being here. Love this little fist bump. Started off, that's right. Before we even got rolling, Manu, you said that you wanted this to be the best segment of, of the cubes airing. We we're doing over a hundred segments, so you're gonna have to bring the heat. >>We're ready. We're did go. Are we ready? Yeah, go. We're ready. Let's bring it on. >>We're ready. All right. I'm, I'm ready. Dave's ready. Let's do it. How's the show going for you guys real quick before we dig in? >>Yeah, I think after Covid, it's really nice to see that we're back into the 2019 level and, you know, people just want to get out, meet people, have that human touch with each other, and I think a lot of trust gets built as a functional that, so it's super amazing to see our partners and customers here at Reedman. Yeah, >>And you've got a few in the house. That's true. Just a few maybe, maybe a couple >>Very few shows can say that, by the way. Yeah, it's maybe a handful. >>I think one of the things we were saying, it's almost like the entire Silicon Valley descended in the expo hall area, so >>Yeah, it's >>For a few different reasons. There's a few different silicon defined. Yeah, yeah, yeah. Don't have strong on for you. So far >>It's, it's, it is amazing. It's the 10th year, right? It's decade, I think I've been to five and it's, it grows every single year. It's the, you have to be here. It's as simple as that. And customers from every single industry are here too. You don't get, a lot of shows have every single industry and almost every single location around the globe. So it's, it's a must, must be >>Here. Well, and the personas evolved, right? I was at reinvent number two. That was my first, and it was all developers, not all, but a lot of developers. And today it's a business mix, really is >>Totally, is a business mix. And I just, I've talked about it a little bit down the show, but the diversity on the show floor, it's the first time I've had to wait in line for the ladies' room at a tech conference. Almost a two decade career. It is, yeah. And it was really refreshing. I'm so impressed. So clearly there's a commitment to community, but also a commitment to diversity. Yeah. And, and it's brilliant to see on the show floor. This is a partnership that is robust and has been around for a little while. Money. Why don't you tell us a little bit about the partnership here? >>Yes. So Red Hand and AWS are best friends, you know, forever together. >>Aw, no wonder we got the fist bumps and all the good vibes coming out. I know, it's great. I love that >>We have a decade of working together. I think the relationship in the first phase was around running rail bundled with E two. Sure. We have about 70,000 customers that are running rail, which are running mission critical workloads such as sap, Oracle databases, bespoke applications across the state of verticals. Now, as more and more enterprise customers are finally, you know, endorsing and adopting public cloud, I think that business is just gonna continue to grow. So a, a lot of progress there. The second titration has been around, you know, developers tearing Red Hat and aws, Hey, listen, we wanna, it's getting competitive. We wanna deliver new features faster, quicker, we want scale and we want resilience. So just entire push towards devs containers. So that's the second chapter with, you know, red Hat OpenShift on aws, which launched as a, a joint manage service in 2021 last year. And I think the third phase, which you're super excited about, is just bringing the ease of consumption, one click deployment, and then having our customers, you know, benefit from the joint committed spend programs together. So, you know, making sure that re and Ansible and JBoss, the entire portfolio of Red Hat products are available on AWS marketplace. So that's the 1, 2, 3, it of our relationship. It's a decade of working together and, you know, best friends are super committed to making sure our customers and partners continue successful. >>Yeah, that he said it, he said it perfectly. 2008, I know you don't like that, but we started with Rel on demand just in 2008 before E two even had a console. So the partnership has been there, like Manu says, for a long time, we got the partnership, we got the products up there now, and we just gotta finalize that, go to market and get that gas on the fire. >>Yeah. So Graviton Outpost, local zones, you lead it into all the new stuff. So that portends, I mean, 2008, we're talking two years after the launch of s3. >>That's right. >>Right. So, and now look, so is this a harbinger of things to come with these new innovations? >>Yeah, I, I would say, you know, the innovation is a key tenant of our partnership, our relationship. So if you look at from a product standpoint, red Hat or Rel was one of the first platforms that made a support for graviton, which is basically 40% better price performance than any other distribution. Then that translated into making sure that Rel is available on all of our regions globally. So this year we launched Switzerland, Spain, India, and Red Hat was available on launch there, support for Nitro support for Outpost Rosa support on Outpost as well. So I think that relationship, that innovation on the product side, that's pretty visible. I think that innovation again then translates into what we are doing on marketplace with one click deployments we spoke about. I think the third aspect of the know innovation is around making sure that we are making our partners and our customers successful. So one of the things that we've done so far is Joe leads a, you know, a black belt team that really goes into each customer opportunity, making sure how can we help you be successful. We launched and you know, we should be able to share that on a link. After this, we launched like a big playlist, which talks about every single use case on how do you get successful and running OpenShift on aws. So that innovation on behalf of our customers partners to make them successful, that's been a key tenant for us together as >>Well. That's right. And that team that Manu is talking about, we're gonna, gonna 10 x that team this year going into January. Our fiscal yield starts in January. Love that. So yeah, we're gonna have a lot of no hiring freeze over here. Nope. No ma'am. No. Yeah, that's right. Yeah. And you know what I love about working with aws and, and, and Manu just said it very, all of that's customer driven. Every single event that we, that he just talked about in that timeline, it's customer driven, right? Customers wanted rail on demand, customers want JBoss up in the cloud, Ansible this week, you know, everything's up there now. So it's just getting that go to market tight and we're gonna, we're gonna get that done. >>So what's the algorithm for customer driven in terms of taking the input? Because if every customers saying, Hey, I this a >>Really similar >>Question right up, right? I, that's what I want. And if you know, 95% of the customers say it, Jay, maybe that's a good idea. >>Yeah, that's right. Trends. But >>Yeah. You know, 30% you might be like, mm, you know, 20%, you know, how do you guys decide when to put gas on the fire? >>No, that, I think, as I mentioned, there are about 70,000 large customers that are running rail on Easy Two, many of these customers are informing our product strategy. So we have, you know, close to about couple of thousand power users. We have customer advisory booths, and these are the, you know, customers are informing us, Hey, let's get all of the Red Hat portfolio and marketplace support for graviton, support for Outpost. Why don't we, why are we not able to dip into the consumption committed spend programs for both Red Hat and aws? That's right. So it's these power users both at the developer level as well as the guys who are actually doing large commercial consumption. They are the ones who are informing the roadmap for both Red Hat and aws. >>But do, do you codify the the feedback? >>Yeah, I'm like, I wanna see the database, >>The, I think it was, I don't know, it was maybe Chasy, maybe it was Besos, that that data beats intuition. So do you take that information and somehow, I mean, it's global, 70,000 customers, right? And they have different weights, different spending patterns, different levels of maturity. Yeah. Do you, how do you codify that and then ultimately make the decision? Yeah, I >>If, I mean, well you, you've got the strategic advisory boards, which are made up of customers and partners and you know, you get, you get a good, you gotta get a good slice of your customer base to get, and you gotta take their feedback and you gotta do something with it, right? That's the, that's the way we do it and codify it at the product level, I'm sure open source. That's, that's basically how we work at the product level, right? The most elegant solution in open source wins. And that's, that's pretty much how we do that at the, >>I would just add, I think it's also just the implicit trust that the two companies had built with each other, working in the trenches, making our customers and partners successful over the last decade. And Alex, give an example. So that manifests itself in context of like, you know, Amazon and Red Hat just published the entire roadmap for OpenShift. What are the new features that are becoming over the next six to nine to 12 months? It's open source available on GitHub. Customers can see, and then they can basically come back and give feedback like, Hey, you know, we want hip compliance. We just launched. That was a big request that was coming from our >>Customers. That is not any process >>Also for Graviton or Nvidia instances. So I I I think it's a, >>Here's the thing, the reason I'm pounding on this is because you guys have a pretty high hit rate, and I think as a >>Customer, mildly successful company >>As, as a customer advocate, the better, you know, if, if you guys make bets that pay off, it's gonna pay off for customers. Right. And because there's a lot of failures in it. Yeah. I mean, let's face it. That's >>Right. And I think, I think you said the key word bets. You place a lot of small bets. Do you have the, the innovation engine to do that? AWS is the perfect place to place those small bets. And then you, you know, pour gas on the fire when, when they take off. >>Yeah, it's a good point. I mean, it's not expensive to experiment. Yeah. >>Especially in the managed service world. Right? >>And I know you love taking things to market and you're a go to market guy. Let's talk gtm, what's got your snow pumped about GTM for 2023? >>We, we are gonna, you know, 10 x the teams that's gonna be focused on these products, right? So we're gonna also come out with a hybrid committed spend program for our customers that meet them where they want to go. So they're coming outta the data center going into a cloud. We're gonna have a nice financial model for them to do that. And that's gonna take a lot of the friction out. >>Yeah. I mean, you've nailed it. I, I think the, the fact that now entire Red Hat portfolio is available on marketplace, you can do it on one click deployment. It's deeply integrated with Amazon services and the most important part that Joel was making now customers can double dip. They can drive benefit from the consumption committed spend programs, both from Red Hat and from aws, which is amazing. Which is a game changer That's right. For many of our large >>Customers. That's right. And that, so we're gonna, we're gonna really go to town on that next year. That's, and all the, all the resources that I have, which are the technology sellers and the sas, you know, the engineers we're growing this team the most out that team. So it's, >>When you say 10 x, how many are you at now? I'm >>Curious to see where you're headed. Tell you, okay. There's not right? Oh no, there's not one. It's triple digit. Yeah, yeah. >>Today. Oh, sweet. Awesome. >>So, and it's a very sizable team. They're actually making sure that each of our customers are successful and then really making sure that, you know, no customer left behind policy. >>And it's a great point that customers love when Amazonians and Red Hats show up, they love it and it's, they want to get more of it, and we're gonna, we're gonna give it to 'em. >>Must feel great to be loved like that. >>Yeah, that's right. Yeah. Yeah. I would say yes. >>Seems like it's safe to say that there's another decade of partnership between your two companies. >>Hope so. That's right. That's the plan. >>Yeah. And I would say also, you know, just the IBM coming into the mix here. Yeah. I, you know, red Hat has informed the way we have turned around our partnership with ibm, essentially we, we signed the strategic collaboration agreement with the company. All of IBM software now runs on Rosa. So that is now also providing a lot of tailwinds both to our rail customers and as well as Rosa customers. And I think it's a very net creative, very positive for our partnership. >>That's right. It's been very positive. Yep. Yeah. >>You see the >>Billboards positive. Yeah, right. Also that, that's great. Great point, Dave. Yep. We have a, we have a new challenge, a new tradition on the cube here at Reinvent where we're, well, it's actually kind of a glamor moment for you, depending on how you leverage it. We're looking for your 32nd hot take your Instagram reel, your sizzle thought leadership, biggest takeaway, most important theme from this year's show. I know you want, right, Joel? I mean, you TM boy, I feel like you can spit the time. >>Yeah. It is all about Rosa for us. It is all in on that, that's the native OpenShift offering on aws and that's, that's the soundbite we're going go to town with. Now, I don't wanna forget all the other products that are in there, but Rosa is a, is a very key push for us this year. >>Fantastic. All right. Manu. >>I think our customers, it's getting super competitive. Our customers want to innovate just a >>Little bit. >>The enterprise customers see the cloud native companies. I wanna do what these guys are doing. I wanna develop features at a fast clip. I wanna scale, I wanna be resilient. And I think that's really the spirit that's coming out. So to Joel's point, you know, move to worlds containers, serverless, DevOps, which was like, you know, aha, something that's happening on the side of an enterprise is not becoming mainstream. The business is demanding it. The, it is becoming the centerpiece in the business strategy. So that's been really like the aha. Big thing that's happening here. >>Yeah. And those architectures are coming together, aren't they? That's correct. Right. You know, VMs and containers, it used to be one architecture and then at the other end of the spectrum is serverless. People thought of those as different things and now it's a single architecture and, and it's kind of right approach for the right job. >>And, and a compliments say to Red Hat, they do an incredible job of hiding that complexity. Yeah. Yes. And making sure that, you know, for example, just like, make it easier for the developers to create value and then, and you know, >>Yeah, that's right. Those, they were previously siloed architectures and >>That's right. OpenShift wanna be place where you wanna run containers or virtual machines. We want that to be this Yeah. Single place. Not, not go bolt on another piece of architecture to just do one or the other. Yeah. >>And hey, the hybrid cloud vision is working for ibm. No question. You know, and it's achievable. Yeah. I mean, I just, I've said unlike, you know, some of the previous, you know, visions on fixing the world with ai, hybrid cloud is actually a real problem that you're attacking and it's showing the results. Agreed. Oh yeah. >>Great. Alright. Last question for you guys. Cause it might be kind of fun, 10 years from now, oh, we're at another, we're sitting here, we all look the same. Time has passed, but we are not aging, which is a part of the new technology that's come out in skincare. That's my, I'm just throwing that out there. Why not? What do you guys hope that you can say about the partnership and, and your continued commitment to community? >>Oh, that's a good question. You go first this time. Yeah. >>I think, you know, the, you know, for looking into the future, you need to look into the past. And Amazon has always been driven by working back from our customers. That's like our key tenant, principle number 1 0 1. >>Couple people have said that on this stage this week. Yeah. >>Yeah. And I think our partnership, I hope over the next decade continues to keep that tenant as a centerpiece. And then whatever comes out of that, I think we, we are gonna be, you know, working through that. >>Yeah. I, I would say this, I think you said that, well, the customer innovation is gonna lead us to wherever that is. And it's, it's, it's gonna be in the cloud for sure. I think we can say that in 10 years. But yeah, anything from, from AI to the quant quantum computing that IBM's really pushing behind that, you know, those are, those are gonna be things that hopefully we show up on a, on a partnership with Manu in 10 years, maybe sooner. >>Well, whatever happens next, we'll certainly be covering it here on the cube. That's right. Thank you both for being here. Joel Manu, fantastic interview. Thanks to see you guys. Yeah, good to see you brought the energy. I think you're definitely ranking high on the top interviews. We >>Love that for >>The day. >>Thank >>My pleasure >>Job, guys. Now that you're competitive at all, and thank you all for tuning in to our live coverage here from AWS Reinvent in Las Vegas, Nevada, with Dave Valante. I'm Savannah Peterson. You're watching The Cube, the leading source for high tech coverage.

>> Well, thanks for joining us here on theCUBE. I'm John Walls. We're at Reinvent AWS's big show going on here in Las Vegas at the Venetian. Going to be here all week, so be sure to tune in here to theCUBE as we continue our executive summit sponsored by Accenture today. Joined now by Steve Steuart, who's the worldwide principal on mainframe migration at Go to Market at AWS. Steve, good to see you, sir. >> Nice to meet you. >> Just found out we're neighbors, as a matter of fact, down in northeastern Florida. >> That's right. >> So we'll exchange addresses later, I'm sure. And Joel Rosenberger, who is a global mainframe monetization lead for the Accenture AWS business group. Joel, good to see you. >> Nice to meet you. >> Thanks for joining us here on theCUBE. >> Absolutely. >> All right, so what's up with the mainframe? We're kind of kidding about 64 Corvette's versus 22 Teslas and making that old Corvette. Dress it up, take it out for the street ride. Make it nice and fun. But let's just set the stage here first off for our viewers about mainframe and kind of the status in terms of modernization and getting it up to 22 standards. >> Right, I mean, I think the big thing is that, you know modernization for mainframes is different for every customer based on their drivers and where they want to go. You know, at AWS we like to say transform with AWS, augmentation pattern, hybrid pattern, working coexisting or transform too. So move some of those workloads into the cloud. And it's not that, you know mainframes are fantastic machines, but they are in dire need of modernization with their applications. And that's really the driving force and the business needs to make a decision based on their drivers, what's best fit for them. And we're here to help. >> So how, Joel, go ahead. >> Oh, I was going to say, and we're saying that too is basically the mainframe is a great technology platform but it's the processes around that that not kept up. So making changes to the mainframe applications can take a couple years, for the simplest changes. And so when Steve talks about modernizing with or on the mainframe it's really how do we improve those processes? And from our perspective and companies are really struggling with that right now. >> Yeah, and how do you go about this, because the mainframe is so center, right? It is so integral, right? >> Oh it's center. >> Oh yeah, absolutely. >> Absolutely essential. And yet you're talking about changes being made over a period of time of years. A lot of sensitivity there, right? >> Oh absolutely. >> Lot of complexity there. So how do you start factoring all that in and selling that to somebody that this journey might take you till 2025 to get it done? >> Well it could be a multi-year process. The selling is really the business drivers. You have to, businesses today need to leverage the cloud to be competitive. >> Absolutely. >> Right, that's just a fact. Right? So, how do you transform with modernize in place, or transform over. But it is a transformational change. If you look at the number one drivers is agility. The CEO say, I want this green next week and well we can't get it to you next week. We can get it to you Q2 of next year. Born in the cop companies... >> That's probably not the answer they want to hear. >> No, they don't want hear that. >> That is not the answer they want to hear. >> Our number one issue is that there are CEOs saying that we can't be agile, but mainframes can't be agile, if you develop, adopt DevOps for your mainframe. >> Yep. >> IBM has an offering, we have an offering as well. And so they need to start looking at that. So what are your drivers? Go to market responsiveness, competitive, what are the drivers? And then you make a decision as to where you want to move the workload. >> Joel: Yep. >> Is it hard though, Joel, just because as you know this environment is so dynamic now, right? >> Yep. >> And change is rapid, and I mean like capital R. >> Yep, absolutely. Yep. >> So all of a sudden you set this two/three year trajectory and yet opportunities, solutions, options can vary in year one or year two and all of a sudden this path you had set is going to have to take a left turn instead of a right turn because of a new development. Right? So it's... >> Absolutely, I mean, and that's one of the biggest struggles that people have is with business agility. Exactly what you're saying is the market is changing faster, like Steve said, it might be a year or so before I can deliver that but the market has already changed from that perspective. >> Right. >> And so I think a lot of people are trying to modernize with that. So they're connecting a lot of web properties to mainframes but that causes additional problems. >> Right. >> And those problems are the mainframe now scales unpredictably, because I don't know, how do I predict web traffic and from that perspective, so a lot of people are struggling do I have enough capacity on the mainframe to do that? Cause it's not elastic like the cloud from that perspective. So there's a lot of patterns that have to be reinvented, or already been invented with the cloud and how do we do that with the mainframe now? >> So you could get benefits not waiting three to four years. >> Absolutely. >> You get benefit pretty much immediately by doing augmentation patterns consuming processing on the mainframe, consuming it maybe certain movements, certain workloads, bringing on down quicker. You know, if you're a large estate it'll take you time but you are able to drive that. Part of our assessments is bottom up what you currently have, and what are your business drivers. >> Yep, absolutely. >> What are the big boulder items you need to do and tackle those. And so it's a process that we work together with our customers to start transforming their mainframe. >> Right. >> Yeah I hear about, I'm sorry, go on Joel. >> Yeah, and a key thing on that is a lot of people look at the mainframe is this big monolith. >> John: Right. >> It's basically the this big thing, I don't know what to do with, I don't want to touch because if I touch it I might break it. I don't have people to fix it. And so there's a lot of concern around that, but one of the things like Steve said is how Accenture and AWS work together is figuring out how do I take that monolith, divide it into smaller pieces either through data augmentation, through an analysis, and figure out a roadmap through that application or that monolithic applications and figure out how to move. >> Well that's, how you get an elephant, right? Leverage is one part at a time. >> Exactly, one part at a time. >> It's just one. >> Right, it's just leverage AI, leverage or AI and our platforms and machine learning. All these things are available and you can coexist with that. >> All right, so tell me about technical debt. I read about technical debt and you know, it kind of comes with the territory, >> Right. >> in terms of mainframe. So how do you, first off, you know, how do you define that and then how do you deal with that? How do you make that go away as far as concerns go? >> Well, you know, you have to look at your, for my definition for technical debts is the same thing when my wife says I have to do something in the backyard and I push it, I'll do it next time. Right? So it starts piling up, right? There's a lot of to-dos at the house. >> Absolutely. >> Right, it's the same thing, it's the IT to-dos that you just put off. >> I'll catch up to that some other time. >> Yep. >> And there you are, they keep on... And so next thing you know, you have this, oh my gosh I got all this work I got to do. >> Right. And that's part of the technical debt. And then so you got to look at how does that resolving that meet my needs for the cloud. So leveraging the cloud, if you're under mainframe you have limited solutions for addressing your technical debt. Leveraging the cloud with the mainframe, now you have multiple options for you. to tackle and eliminate your technical debt. So that's one of the benefits of leveraging the cloud for that. >> And I would add on to what Steve said about technical debt. It's exactly that, it's I haven't done that yet, but one of the things that I've seen is there's multiple ways to solve any problem, any programming problem technical problem from that, there's a shortcut way to get it done quickly, >> John: Right. >> that may not be clean and scalable and that. And what happens is, especially on the mainframe over 40 or 50 years, a lot of those shortcuts have been taken. And so it's not even as easy as, it's basically, you think about it, I didn't do it but now my grass is this high, >> John: Right. >> And now I got to do it, type of thing. So it's really about... >> And you can't use a lawnmower >> You can't use a lawnmower so you have to figure out different ways. >> You can't bag it, >> No, no. >> No, no, a whole nother >> Absolutely. >> Right. >> So understanding technical debt and overcoming it is realizing that those shortcuts need to be re-architected, redesigned, modernized, >> All right. >> from that perspective. And you need to take that perspective on. >> So you guys have to be kind of sometimes the bearer of bad news in a way, right? Because they have these, you said monolithic of systems in place that need revised they got to be modernized. >> Yep. >> And they've been kicking that can down the road. We've talked about some big companies for a long time. So they got a lot of baggage on that side and they have to get up to speed. So if, if you were talking to a prospective client, about understanding why it's time to start doing that necessary housekeeping, how do you convince people that this is the time? >> What are your top three absolutely mission critical applications that you have today, right? What is the staff that maintains it? What is the average age of those resources? And what is your succession strategy? >> Joel: Yep. >> It's as simple as that. >> Oh. >> I would add on to that. A lot of times we don't have to convince the customer right now. >> John: Right. >> The customers are coming to us, because what's happened is this whole digital transformation that's happening in the web and all that kind of stuff. Their competitors are already moving off of that. Or have come up with something else. So the business is coming and saying, why can't I move that fast? >> Right. >> And then, like Steve said is those are the reasons why you can't move that fast. So let's address those reasons. >> All right, the born of the cloud company is coming in, but also another driving force that's happening, If you look at a lot of our new customers. Are the digital natives arriving in the C-suites. So the folks that have always known the internet understand the benefits of the cloud, or where there's a new CIO, new CEO. >> Yep. >> And so we're seeing that changing of the guard type scenario. >> Because a lot of those people grew up with a mainframe. >> Right. Right. >> And of the old guard. >> Sure. >> And they're like, well it's worked for the last 30 years, why don't I just keep it working the same way it is. >> And don't we need it to work? >> Yeah >> Right? The way it has been? >> Yeah, exactly. >> Yeah. >> Well, and that's the other key thing, is the core applications. So what has happened with the cloud is over the last you know, 10, 15 years is a lot of the applications that could move moved. Now we're left with the core applications on the mainframe and those are the ones that a multi-billion dollar company, if they get that wrong, they're out business. So there's a lot of scrutiny and a lot of other things. So a lot of the stuff that we're doing now is to help understand that risk and get over that risk. >> And do companies have the expertise in house, to do this? And where do you find it outside? Because it, you know, might not be the sexiest thing to do. >> That's a great question because, you know Steve and I talk about this all the time which is running the mainframe is different than modernizing the mainframe. >> Steve: Right. And so I might have a lot of skills in house to run the mainframe, but how do I figure out to get, to break up that monolith into pieces. >> Steve: Right. How do I figure out, you know, how the best way to put that on AWS? How do I figure that out? You need to leverage people like AWS and Accenture and others to be able to do that. >> This is, there's a psychology to this and more technical, there's more psychological than technical. So you got to find your unicorns. People should have gas in the tank that want to adopt. >> Joel: Yep, absolutely. >> And the ones that don't, then, you know, they're out. You know, nothing like passive aggressive people showing up to help, to really cause havoc. (all laugh) And that's really what you got to kind of focus on. >> Yes. We see that a lot. >> Right. Right. But that's where the managing service comes in too right? >> Absolutely. >> You can get people there. You can, this is a worry they can check the box and move on and get help in that. >> Yeah. AWS, this is an industry first, where you have a managed service within your console to provision tooling to analyze, develop for the mainframe or deploy onto AWS. But the running of it, specific servers that have been you know, optimized for mainframe workloads with your monitoring and security and all those things it's an industry first. I've been in this business 30 years it's fantastic with what I'm seeing over here. >> And do you have any kind of a guess about what share is still out there to be had, in terms of modernizing mainframes, in terms of businesses? I mean, are there still, well, you know it might be hard to put a, to quantify it with a number, but there's still a lot of folks... >> Oh yeah. >> who haven't made that commitment yet. >> Well, they're beginning to, so if you look at, I think, I'm going to throw a number out, I think it's like 80% of the Fortune 100 companies have mainframes. >> Absolutely. >> Is that right? >> So yeah, if you paid your mortgage today, if you used your cell phone today, if you've done any of those things, core stuff is run on mainframe. >> Financial transactions are huge. >> Oh huge, huge, you've got airlines, manufacturing, >> Insurance. >> healthcare. >> John: Right. >> Pretty much everything runs on a mainframe, if you go deep enough in the organization. >> And so that's all, you know people are making those decisions. And what we've done is what I call an earn trust moment. You know, AWS standing up and saying, 'hey we're here to help our customers to move' we're a large organization, we're doing heavy investments in this. We have R&Dand staff, to help our customers transform with or to AWS. >> And we're seeing that resonated in the marketplace. So last year AWS announced the mainframe modernization service Over the last year, we've seen clients, like I said is they're coming to us now. >> Right. >> Saying we want to go mainframe zero, for lack of a better expression. And so we're just seeing a lot of activity. So what AWS did last year has really resonated within the marketplace and changed that dynamic. >> Well, the mainframe ain't dead yet. >> No. >> It isn't. >> It's not going to die. I think there's going to be a different >> Too big, two powerful and too necessary. >> Absolutely. >> Yeah I think we're going to coexist with it and some will leave, so. >> But you still need that same functionality, just somewhere else. >> All right. >> That's right. Well, appreciate the conversation, neighbor. >> Thank you. (all laugh) >> And have a great show. Look forward to seeing you down the road here. >> Thank you very much. >> Thanks John, appreciate it. >> Thanks for joining us here. You are watching theCUBE here at Reinvent 22. And theCUBE, as I remind you is the leader in high tech coverage. (soothing music)

(upbeat music) >> Welcome back everyone to The Cubes coverage here in Seattle, Washington. For AWS's Marketplace Seller Conference. It's the big news within the Amazon partner network, combining with marketplace, forming the Amazon partner organization. Part of a big reorg as they grow to the next level, NextGen cloud, mid-game on the chessboard. Cube's got it covered. I'm John Furry, your host at Cube. Great guests here from Data bricks. Both cube alumni's. Jack Anderson, GM and VP of the Databricks partnership team for AWS. You handle that relationship and Joel Minick vice president of product and partner marketing. You guys have the keys to the kingdom with Databricks and AWS. Thanks for joining. Good to see you again. >> Thanks for having us back. >> Yeah, John, great to be here. >> So I feel like we're at Reinvent 2013. Small event, no stage, but there's a real shift happening with procurement. Obviously it's a no brainer on the micro, you know, people should be buying online. Self-service, Cloud Scale. But Amazon's got billions being sold through their marketplace. They've reorganized their partner network. You can see kind of what's going on. They've kind of figured it out. Like let's put everything together and simplify and make it less of a website, marketplace. Merge our partner organizations, have more synergy and frictionless experiences so everyone can make more money and customer's are going to be happier. >> Yeah, that's right. >> I mean, you're running relationship. You're in the middle of it. >> Well, Amazon's mental model here is that they want the world's best ISVs to operate on AWS so that we can collaborate and co architect on behalf of customers. And that's exactly what the APO and marketplace allow us to do, is to work with Amazon on these really, you know, unique use cases. >> You know, I interviewed Ali many times over the years. I remember many years ago, maybe six, seven years ago, we were talking. He's like, "we're all in on AWS." Obviously now the success of Databricks, you've got multiple clouds, see that. Customers have choice. But I remember the strategy early on. It was like, we're going to be deep. So this is, speaks volumes to the relationship you have. Years. Jack, take us through the relationship that Databricks has with AWS from a partner perspective. Joel, and from a product perspective. Because it's not like you guys are Johnny come lately, new to the scene. >> Right. >> You've been there, almost president creation of this wave. What's the relationship and how does it relate to what's going on today? >> So most people may not know that Databricks was born on AWS. We actually did our first $100 million of revenue on Amazon. And today we're obviously available on multiple clouds. But we're very fond of our Amazon relationship. And when you look at what the APN allows us to do, you know, we're able to expand our reach and co-sell with Amazon, and marketplace broadens our reach. And so, we think of marketplace in three different aspects. We've got the marketplace private offer business, which we've been doing for a number of years. Matter of fact, we were driving well over a hundred percent year over year growth in private offers. And we have a nine figure business. So it's a very significant business. And when a customer uses a private offer, that private offer counts against their private pricing agreement with AWS. So they get pricing power against their private pricing. So it's really important it goes on their Amazon bill. In may we launched our pay as you go, on demand offering. And in five short months, we have well over a thousand subscribers. And what this does, is it really reduces the barriers to entry. It's low friction. So anybody in an enterprise or startup or public sector company can start to use Databricks on AWS, in a consumption based model, and have it go against their monthly bill. And so we see customers, you know, doing rapid experimentation, pilots, POCs. They're really learning the value of that first, use case. And then we see rapid use case expansion. And the third aspect is the consulting partner, private offer, CPPO. Super important in how we involve our partner ecosystem of our consulting partners and our resellers that are able to work with Databricks on behalf of customers. >> So you got the big contracts with the private offer. You got the product market fit, kind of people iterating with data, coming in with the buyers you get. And obviously the integration piece all fitting in there. >> Exactly. >> Okay, so those are the offers, that's current, what's in marketplace today. Is that the products... What are people buying? >> Yeah. >> I mean, I guess what's the... Joel, what are people buying in the marketplace? And what does it mean for them? >> So fundamentally what they're buying is the ability to take silos out of their organization. And that is the problem that Databricks is out there to solve. Which is, when you look across your data landscape today, you've got unstructured data, you've got structured data, you've got real time streaming data. And your teams are trying to use all of this data to solve really complicated problems. And as Databricks, as the Lakehouse Company, what we're helping customers do is, how do they get into the new world? How do they move to a place where they can use all of that data across all of their teams? And so we allow them to begin to find, through the marketplace, those rapid adoption use cases where they can get rid of these data warehousing, data lake silos they've had in the past. Get their unstructured and structured data onto one data platform, an open data platform, that is no longer adherent to any proprietary formats and standards and something they can, very much, very easily, integrate into the rest of their data environment. Apply one common data governance layer on top of that. So that from the time they ingest that data, to the time they use that data, to the time they share that data, inside and outside of their organization, they know exactly how it's flowing. They know where it came from. They know who's using it. They know who has access to it. They know how it's changing. And then with that common data platform, with that common governance solution, they'd being able to bring all of those use cases together. Across their real time streaming, their data engineering, their BI, their AI. All of their teams working on one set of data. And that lets them move really, really fast. And it also lets them solve challenges they just couldn't solve before. A good example of this, you know, one of the world's now largest data streaming platforms runs on Databricks with AWS. And if you think about what does it take to set that up? Well, they've got all this customer data that was historically inside of data warehouses. That they have to understand who their customers are. They have all this unstructured data, they've built their data science model, so they can do the right kinds of recommendation engines and forecasting around. And then they've got all this streaming data going back and forth between click stream data, from what the customers are doing with their platform and the recommendations they want to push back out. And if those teams were all working in individual silos, building these kinds of platforms would be extraordinarily slow and complex. But by building it on Databricks, they were able to release it in record time and have grown at a record pace to now be the number one platform. >> And this product, it's impacting product development. >> Absolutely. >> I mean, this is like the difference between lagging months of product development, to like days. >> Yes. >> Pretty much what you're getting at. >> Yes. >> So total agility. >> Mm-hmm. >> I got that. Okay, now, I'm a customer I want to buy in the marketplace, but you got direct Salesforce up there. So how do you guys look at this? Is there channel conflict? Are there comp programs? Because one of the things I heard today in on the stage from AWS's leadership, Chris, was up there speaking, and Mona was, "Hey, he's a CRO conference chief revenue officer" conversation. Which means someone's getting compensated. So, if I'm the sales rep at Databricks, what's my motion to the customer? Do I get paid? Does Amazon sell it? Take us through that. Is there channel conflict? Or, how do you handle it? >> Well, I'd add what Joel just talked about with, you know, with the solution, the value of the solution our entire offering is available on AWS marketplace. So it's not a subset, it's the entire Data Bricks offering. And- >> The flagship, all the, the top stuff. >> Everything, the flagship, the complete offering. So it's not segmented. It's not a sub segment. >> Okay. >> It's, you know, you can use all of our different offerings. Now when it comes to seller compensation, we view this two different ways, right? One is that AWS is also incented, right? Versus selling a native service to recommend Databricks for the right situation. Same thing with Databricks, our sales force wants to do the right thing for the customer. If the customer wants to use marketplace as their procurement vehicle. And that really helps customers because if you get Databricks and five other ISVs together, and let's say each ISV is spending, you're spending a million dollars. You have $5 million of spend. You put that spend through the flywheel with AWS marketplace, and then you can use that in your negotiations with AWS to get better pricing overall. So that's how we view it. >> So customers are driving. This sounds like. >> Correct. For sure. >> So they're looking at this as saying, Hey, I'm going to just get purchasing power with all my relationships. Because it's a solution architectural market, right? >> Yeah. It makes sense. Because if most customers will have a primary and secondary cloud provider. If they can consolidate, you know, multiple ISV spend through that same primary provider, you get pricing power. >> Okay, Joel, we're going to date ourselves. At least I will. So back in the old days, (group laughter) It used to be, do a Barney deal with someone, Hey, let's go to market together. You got to get paper, you do a biz dev deal. And then you got to say, okay, now let's coordinate our sales teams, a lot of moving parts. So what you're getting at here is that the alternative for Databricks, or any company is, to go find those partners and do deals, versus now Amazon is the center point for the customer. So you can still do those joint deals, but this seems to be flipping the script a little bit. >> Well, it is, but we still have vars and consulting partners that are doing implementation work. Very valuable work, advisory work, that can actually work with marketplace through the CPPO offering. So the marketplace allows multiple ways to procure your solution. >> So it doesn't change your business structure. It just makes it more efficient. >> That's correct. >> That's a great way to say it. >> Yeah, that's great. >> Okay. So, that's it. So that's just makes it more efficient. So you guys are actually incented to point customers to the marketplace. >> Yes. >> Absolutely. >> Economically. >> Economically, it's the right thing to do for the customer. It's the right thing to do for our relationship with Amazon. Especially when it comes back to co-selling, right? Because Amazon now is leaning in with ISVs and making recommendations for, you know, an ISV solution. And our teams are working backwards from those use cases, you know, to collaborate and land them. >> Yeah. I want to get that out there. Go ahead, Joel. >> So one of the other things I might add to that too, you know, and why this is advantageous for companies like Databricks to work through the marketplace. Is it makes it so much easier for customers to deploy a solution. It's very, literally, one click through the marketplace to get Databricks stood up inside of your environment. And so if you're looking at how do I help customers most rapidly adopt these solutions in the AWS cloud, the marketplace is a fantastic accelerator to that. >> You know, it's interesting. I want to bring this up and get your reaction to it because to me, I think this is the future of procurement. So from a procurement standpoint, I mean, again, dating myself, EDI back in the old days, you know, all that craziness. Now this is all the internet, basically through the console. I get the infrastructure side, you know, spin up and provision some servers, all been good. You guys have played well there in the marketplace. But now as we get into more of what I call the business apps, and they brought this up on stage. A little nuanced. Most enterprises aren't yet there of integrating tech, on the business apps, into the stack. This is where I think you guys are a use case of success where you guys have been successful with data integration. It's an integrators dilemma, not an innovator's dilemma. So like, I want to integrate. So now I have integration points with Databricks, but I want to put an app in there. I want to provision an application, but it has to be built. It's not, you don't buy it. You build, you got to build stuff. And this is the nuance. What's your reaction to that? Am I getting this right? Or am I off because, no one's going to be buying software like they used to. They buy software to integrate it. >> Yeah, no- >> Because everything's integrated. >> I think AWS has done a great job at creating a partner ecosystem, right? To give customers the right tools for the right jobs. And those might be with third parties. Databricks is doing the same thing with our partner connect program, right? We've got customer partners like Five Tran and DBT that, you know, augment and enhance our platform. And so you're looking at multi ISV architectures and all of that can be procured through the AWS marketplace. >> Yeah. It's almost like, you know, bundling and un bundling. I was talking about this with, with Dave Alante about Supercloud. Which is why wouldn't a customer want the best solution in their architecture? Period. In its class. If someone's got API security or an API gateway. Well, you know, I don't want to be forced to buy something because it's part of a suite. And that's where you see things get sub optimized. Where someone dominates a category and they have, oh, you got to buy my version of this. >> Joel and I were talking, we were actually saying, what's really important about Databricks, is that customers control the data, right? You want to comment on that? >> Yeah. I was going to say, you know, what you're pushing on there, we think is extraordinarily, you know, the way the market is going to go. Is that customers want a lot of control over how they build their data stack. And everyone's unique in what tools are the right ones for them. And so one of the, you know, philosophically, I think, really strong places, Databricks and AWS have lined up, is we both take an approach that you should be able to have maximum flexibility on the platform. And as we think about the Lakehouse, one thing we've always been extremely committed to, as a company, is building the data platform on an open foundation. And we do that primarily through Delta Lake and making sure that, to Jack's point, with Databricks, the data is always in your control. And then it's always stored in a completely open format. And that is one of the things that's allowed Databricks to have the breadth of integrations that it has with all the other data tools out there. Because you're not tied into any proprietary format, but instead are able to take advantage of all the innovation that's happening out there in the open source ecosystem. >> When you see other solutions out there that aren't as open as you guys, you guys are very open by the way, we love that too. We think that's a great strategy, but what am I foreclosing if I go with something else that's not as open? What's the customer's downside as you think about what's around the corner in the industry? Because if you believe it's going to be open, open source, which I think open source software is the software industry, and integration is a big deal. Because software's going to be plentiful. >> Sure. >> Let's face it. It's a good time to be in software business. But Cloud's booming. So what's the downside, from your Databricks perspective? You see a buyer clicking on Databricks versus that alternative. What's potentially should they be a nervous about, down the road, if they go with a more proprietary or locked in approach? >> Yeah. >> Well, I think the challenge with proprietary ecosystems is you become beholden to the ability of that provider to both build relationships and convince other vendors that they should invest in that format. But you're also, then, beholden to the pace at which that provider is able to innovate. >> Mm-hmm. >> And I think we've seen lots of times over history where, you know, a proprietary format may run ahead, for a while, on a lot of innovation. But as that market control begins to solidify, that desire to innovate begins to degrade. Whereas in the open formats- >> So extract rents versus innovation. (John laughs) >> Exactly. Yeah, exactly. >> I'll say it. >> But in the open world, you know, you have to continue to innovate. >> Yeah. >> And the open source world is always innovating. If you look at the last 10 to 15 years, I challenge you to find, you know, an example where the innovation in the data and AI world is not coming from open source. And so by investing in open ecosystems, that means you are always going to be at the forefront of what is the latest. >> You know, again, not to date myself again, but you look back at the eighties and nineties, the protocol stacked with proprietary. >> Yeah. >> You know, SNA and IBM, deck net was digital. You know the rest. And then TCPIP was part of the open systems interconnect. >> Mm-hmm. >> Revolutionary (indistinct) a big part of that, as well as my school did. And so like, you know, that was, but it didn't standardize the whole stack. It stopped at IP and TCP. >> Yeah. >> But that helped inter operate, that created a nice defacto. So this is a big part of this mid game. I call it the chessboard, you know, you got opening game and mid-game, then you get the end game. You're not there at the end game yet at Cloud. But Cloud- >> There's, always some form of lock in, right? Andy Jazzy will address it, you know, when making a decision. But if you're going to make a decision you want to reduce- You don't want to be limited, right? So I would advise a customer that there could be limitations with a proprietary architecture. And if you look at what every customer's trying to become right now, is an AI driven business, right? And so it has to do with, can you get that data out of silos? Can you organize it and secure it? And then can you work with data scientists to feed those models? >> Yeah. >> In a very consistent manner. And so the tools of tomorrow will, to Joel's point, will be open and we want interoperability with those tools. >> And choice is a matter too. And I would say that, you know, the argument for why I think Amazon is not as locked in as maybe some other clouds, is that they have to compete directly too. Redshift competes directly with a lot of other stuff. But they can't play the bundling game because the customers are getting savvy to the fact that if you try to bundle an inferior product with something else, it may not work great at all. And they're going to be, they're onto it. This is the- >> To Amazon's credit by having these solutions that may compete with native services in marketplace, they are providing customers with choice, low price- >> And access to the core value. Which is the hardware- >> Exactly. >> Which is their platform. Okay. So I want to get you guys thought on something else I see emerging. This is, again, kind of Cube rumination moment. So on stage, Chris unpacked a lot of stuff. I mean this marketplace, they're touching a lot of hot buttons here, you know, pricing, compensation, workflows, services behind the curtain. And one of those things he mentioned was, they talk about resellers or channel partners, depending upon what you talk about. We believe, Dave and I believe on the Cube, that the entire indirect sales channel of the industry is going to be disrupted radically. Because those players were selling hardware in the old days and software. That game is going to change. You mentioned you guys have a program, let me get your thoughts on this. We believe that once this gets set up, they can play in this game and bring their services in. Which means that the old reseller channels are going to be rewritten. They're going to be refactored with this new kinds of access. Because you've got scale, you've got money and you've got product. And you got customers coming into the marketplace. So if you're like a reseller that sold computers to data centers or software, you know, a value added reseller or VAB or business. >> You've got to evolve. >> You got to, you got to be here. >> Yes. >> Yeah. >> How are you guys working with those partners? Because you say you have a product in your marketplace there. How do I make money if I'm a reseller with Databricks, with Amazon? Take me through that use case. >> Well I'll let Joel comment, but I think it's pretty straightforward, right? Customers need expertise. They need knowhow. When we're seeing customers do mass migrations to the cloud or Hadoop specific migrations or data transformation implementations. They need expertise from consulting and SI partners. If those consulting and SI partners happen to resell the solution as well. Well, that's another aspect of their business. But I really think it is the expertise that the partners bring to help customers get outcomes. >> Joel, channel big opportunity for Amazon to reimagine this. >> For sure. Yeah. And I think, you know, to your comment about how do resellers take advantage of that, I think what Jack was pushing on is spot on. Which is, it's becoming more and more about the expertise you bring to the table. And not just transacting the software. But now actually helping customers make the right choices. And we're seeing, you know, both SIs begin to be able to resell solutions and finding a lot of opportunity in that. >> Yeah. And I think we're seeing traditional resellers begin to move into that SI model as well. And that's going to be the evolution that this goes. >> At the end of the day, it's about services, right? >> For sure. Yeah. >> I mean... >> You've got a great service. You're going to have high gross profits. >> Yeah >> Managed service provider business is alive and well, right? Because there are a number of customers that want that type of a service. >> I think that's going to be a really hot, hot button for you guys. I think being the way you guys are open, this channel, partner services model coming in, to the fold, really kind of makes for kind of that Supercloud like experience, where you guys now have an ecosystem. And that's my next question. You guys have an ecosystem going on, within Databricks. >> For sure. >> On top of this ecosystem. How does that work? This is kind of like, hasn't been written up in business school and case studies yet. This is new. What is this? >> I think, you know, what it comes down to is, you're seeing ecosystems begin to evolve around the data platforms. And that's going to be one of the big, kind of, new horizons for us as we think about what drives ecosystems. It's going to be around, well, what's the data platform that I'm using? And then all the tools that have to encircle that to get my business done. And so I think there's, you know, absolutely ecosystems inside of the AWS business on all of AWS's services, across data analytics and AI. And then to your point, you are seeing ecosystems now arise around Databricks in its Lakehouse platform as well. As customers are looking at well, if I'm standing these Lakehouses up and I'm beginning to invest in this, then I need a whole set of tools that help me get that done as well. >> I mean you think about ecosystem theory, we're living a whole nother dream. And I'm not kidding. It hasn't yet been written up and for business school case studies is that, we're now in a whole nother connective tissue, ecology thing happening. Where you have dependencies and value proposition. Economics, connectedness. So you have relationships in these ecosystems. >> And I think one of the great things about the relationships with these ecosystems, is that there's a high degree of overlap. >> Yeah. >> So you're seeing that, you know, the way that the cloud business is evolving, the ecosystem partners of Databricks, are the same ecosystem partners of AWS. And so as you build these platforms out into the cloud, you're able to really take advantage of best of breed, the broadest set of solutions out there for you. >> Joel, Jack, I love it because you know what it means? The best ecosystem will win, if you keep it open. >> Sure, sure. >> You can see everything. If you're going to do it in the dark, you know, you don't know the outcome. I mean, this is really kind of what we're talking about. >> And John, can I just add that when I was at Amazon, we had a theory that there's buyers and builders, right? There's very innovative companies that want to build things themselves. We're seeing now that that builders want to buy a platform. Right? >> Yeah. >> And so there's a platform decision being made and that ecosystem is going to evolve around the platform. >> Yeah, and I totally agree. And the word innovation gets kicked around. That's why, you know, when we had our Supercloud panel, it was called the innovators dilemma, with a slash through it, called the integrater's dilemma. Innovation is the digital transformation. So- >> Absolutely. >> Like that becomes cliche in a way, but it really becomes more of a, are you open? Are you integrating? If APIs are connective tissue, what's automation, what's the service messages look like? I mean, a whole nother set of, kind of thinking, goes on in these new ecosystems and these new products. >> And that thinking is, has been born in Delta Sharing, right? So the idea that you can have a multi-cloud implementation of Databricks, and actually share data between those two different clouds, that is the next layer on top of the native cloud solution. >> Well, Databricks has done a good job of building on top of the goodness of, and the CapEx gift from AWS. But you guys have done a great job taking that building differentiation into the product. You guys have great customer base, great growing ecosystem. And again, I think a shining example of what every enterprise is going to do. Build on top of something, operating model, get that operating model, driving revenue. >> Mm-hmm. >> Yeah. >> Whether, you're Goldman Sachs or capital one or XYZ corporation. >> S and P global, NASDAQ. >> Yeah. >> We've got, you know, the biggest verticals in the world are solving tough problems with Databricks. I think we'd be remiss because if Ali was here, he would really want to thank Amazon for all of the investments across all of the different functions. Whether it's the relationship we have with our engineering and service teams. Our marketing teams, you know, product development. And we're going to be at Reinvent. A big presence at Reinvent. We're looking forward to seeing you there, again. >> Yeah. We'll see you guys there. Yeah. Again, good ecosystem. I love the ecosystem evolutions happening. This NextGen Cloud is here. We're seeing this evolve, kind of new economics, new value propositions kind of scaling up. Producing more. So you guys are doing a great job. Thanks for coming on the Cube and taking the time. Joel, great to see you at the check. >> Thanks for having us, John. >> Okay. Cube coverage here. The world's changing as APN comes together with the marketplace for a new partner organization at Amazon web services. The Cube's got it covered. This should be a very big, growing ecosystem as this continues. Billions of being sold through the marketplace. And of course the buyers are happy as well. So we've got it all covered. I'm John Furry. your host of the cube. Thanks for watching. (upbeat music)

>>Welcome back everyone to the cubes coverage here in Seattle, Washington, AWS's marketplace seller conference. It's the big news within the Amazon partner network, combining with marketplaces, forming the Amazon partner organization, part of a big reorg as they grow the next level NextGen cloud mid-game on the chessboard. Cube's got cover. I'm John fur, host of Cub, a great guests here from data bricks, both cube alumnis, Jack Anderson, GM of the and VP of the data bricks partnership team. For ADOS, you handle that relationship and Joel Minick vice president of product and partner marketing. You guys are the, have the keys to the kingdom with data, bricks, and AWS. Thanks for joining. Thanks for good to see you again. Thanks for >>Having us back. Yeah, John, great to be here. >>So I feel like we're at reinvent 2013 small event, no stage, but there's a real shift happening with procurement. Obviously it makes it's a no brainer on the micro, you know, people should be buying online self-service cloud scale, but Amazon's got billions being sold to their marketplace. They've reorganized their partner network. You can see kind of what's going on. They've kind of figured it out. Like let's put everything together and simplify and make it less of a website marketplace merge our partner to have more synergy and friction, less experiences so everyone can make more money and customer's gonna be happier. >>Yeah, that's right. >>I mean, you're run relationship. You're in the middle of it. >>Well, Amazon's mental model here is that they want the world's best ISVs to operate on AWS so that we can collaborate and co architect on behalf of customers. And that's exactly what the APO and marketplace allow us to do is to work with Amazon on these really, you know, unique use cases. >>You know, I interviewed Ali many times over the years. I remember many years ago, I think six, maybe six, seven years ago, we were talking. He's like, we're all in ons. Obviously. Now the success of data bricks, you've got multiple clouds. See that customers have choice, but I remember the strategy early on. It was like, we're gonna be deep. So this is speaks volumes to the, the relationship you have years. Jack take us through the relationship that data bricks has with AWS from a, from a partner perspective, Joel, and from a product perspective, because it's not like you got to Johnny come lately new to the new, to the scene, right? We've been there almost president creation of this wave. What's the relationship and has it relate to what's going on today? >>So, so most people may not know that data bricks was born on AWS. We actually did our first 100 million of revenue on Amazon. And today we're obviously available on multiple clouds, but we're very fond of our Amazon relationship. And when you look at what the APN allows us to do, you know, we're able to expand our reach and co-sell with Amazon and marketplace broadens our reach. And so we think of marketplace in three different aspects. We've got the marketplace, private offer business, which we've been doing for a number of years. Matter of fact, we we're driving well over a hundred percent year over year growth in private offers and we have a nine figure business. So it's a very significant business. And when a customer uses a private offer that private offer counts against their private pricing agreement with AWS. So they get pricing power against their, their private pricing. >>So it's really important. It goes on their Amazon bill in may. We launched our pay as you go on demand offering. And in five short months, we have well over a thousand subscribers. And what this does is it really reduces the barriers to entry it's low friction. So anybody in an enterprise or startup or public sector company can start to use data bricks on AWS and pay consumption based model and have it go against their monthly bill. And so we see customers, you know, doing rapid experimentation pilots, POCs, they're, they're really learning the value of that first use case. And then we see rapid use case expansion. And the third aspect is the consulting partner, private offers C P O super important in how we involve our partner ecosystem of our consulting partners and our resellers that are able to work with data bricks on behalf of customers. >>So you got the big contracts with the private offer. You got the product market fit, kind of people iterating with data coming in with, with the buyers you go. And obviously the integration piece all fitting in there. Exactly. Exactly. Okay. So that's that those are the offers that's current and what's in marketplace today. Is that the products, what are, what are people buying? I mean, I guess what's the Joel, what are, what are people buying in the marketplace and what does it mean for >>Them? So fundamentally what they're buying is the ability to take silos out of their organization. And that's, that is the problem that data bricks is out there to solve, which is when you look across your data landscape today, you've got unstructured data, you've got structured data, you've got real time streaming data, and your teams are trying to use all of this data to solve really complicated problems. And as data bricks as the lake house company, what we're helping customers do is how do they get into the new world? How do they move to a place where they can use all of that data across all of their teams? And so we allow them to begin to find through the marketplace, those rapid adoption use cases where they can get rid of these data, warehousing data lake silos they've had in the past, get their unstructured and structured data onto one data platform and open data platform that is no longer adherent to any proprietary formats and standards and something. >>They can very much, very easily integrate into the rest of their data environment, apply one common data governance layer on top of that. So that from the time they ingest that data to the time they use that data to the time they share that data inside and outside of their organization, they know exactly how it's flowing. They know where it came from. They know who's using it. They know who has access to it. They know how it's changing. And then with that common data platform with that common governance solution, they'd being able to bring all of those use cases together across their real time, streaming their data engineering, their BI, their AI, all of their teams working on one set of data. And that lets them move really, really fast. And it also lets them solve challenges. They just couldn't solve before a good example of this, you know, one of the world's now largest data streaming platforms runs on data bricks with AWS. >>And if you think about what does it take to set that up? Well, they've got all this customer data that was historically inside of data warehouses, that they have to understand who their customers are. They have all this unstructured data, they've built their data science model, so they can do the right kinds of recommendation engines and forecasting around. And then they've got all this streaming data going back and forth between click stream data from what the customers are doing with their platform and the recommendations they wanna push back out. And if those teams were all working in individual silos, building these kinds of platforms would be extraordinarily slow and complex, but by building it on data bricks, they were able to release it in record time and have grown at, at record pace >>To not be that's product platform that's impacting product development. Absolutely. I mean, this is like the difference between lagging months of product development to like days. Yes. Pretty much what you're getting at. Yeah. So total agility. I got that. Okay. Now I'm a customer I wanna buy in the marketplace, but I also, you got direct Salesforce up there. So how do you guys look at this? Is there channel conflict? Are there comp programs? Because one of the things I heard today in on the stage from a Davis's leadership, Chris was up there speaking and, and, and moment I was, Hey, he's a CRO conference, chief revenue officer conversation, which means someone's getting compensated. So if I'm the sales rep at data bricks, what's my motion to the customer. Do I get paid? Does Amazon sell it? Take us through that. Is there channel conflict? Is there or an audio lift? >>Well, I I'd add what Joel just talked about with, with, you know, what the solution, the value of the solution our entire offering is available on AWS marketplace. So it's not a subset, the entire data bricks offering and >>The flagship, all the, the top, >>Everything, the flagship, the complete offering. So it's not, it's not segmented. It's not a sub segment. It's it's, you know, you can use all of our different offerings. Now when it comes to seller compensation, we, we, we view this two, two different ways, right? One is that AWS is also incented, right? Versus selling a native service to recommend data bricks for the right situation. Same thing with data bricks. Our Salesforce wants to do the right thing for the customer. If the customer wants to use marketplace as their procurement vehicle. And that really helps customers because if you get data bricks and five other ISVs together, and let's say each ISV is spending, you're spending a million dollars, you have $5 million of spend, you put that spend through the flywheel with AWS marketplace. And then you can use that in your negotiations with AWS to get better pricing overall. So that's how we, >>We do it. So customers are driving. This sounds like, correct. For sure. So they're looking at this as saying, Hey, I'm gonna just get purchasing power with all my relationships because it's a solution architectural market, right? >>Yeah. It makes sense. Because if most customers will have a primary and secondary cloud provider, if they can consolidate, you know, multiple ISV spend through that same primary provider, you get pricing >>Power, okay, Jill, we're gonna date ourselves. At least I will. So back in the old days, it used to be, do a Barney deal with someone, Hey, let's go to market together. You gotta get paper, you do a biz dev deal. And then you gotta say, okay, now let's coordinate our sales teams, a lot of moving parts. So what you're getting at here is that the alternative for data bricks or any company is to go find those partners and do deals versus now Amazon is the center point for the customer so that you can still do those joint deals. But this seems to be flipping the script a little bit. >>Well, it is, but we still have VAs and consulting partners that are doing implementation work very valuable work advisory work that can actually work with marketplace through the C PPO offering. So the marketplace allows multiple ways to procure your >>Solution. So it doesn't change your business structure. It just makes it more efficient. That's >>Correct. >>That's a great way to say it. Yeah, >>That's great. So that's so that's it. So that's just makes it more efficient. So you guys are actually incented to point customers to the marketplace. >>Yes, >>Absolutely. Economically. Yeah. >>E economically it's the right thing to do for the customer. It's the right thing to do for our relationship with Amazon, especially when it comes back to co-selling right? Because Amazon now is leaning in with ISVs and making recommendations for, you know, an ISV solution and our teams are working backwards from those use cases, you know, to collaborate, land them. >>Yeah. I want, I wanna get that out there. Go ahead, Joel. >>So one of the other things I might add to that too, you know, and why this is advantageous for, for companies like data bricks to, to work through the marketplace, is it makes it so much easier for customers to deploy a solution. It's, it's very, literally one click through the marketplace to get data bricks stood up inside of your environment. And so if you're looking at how do I help customers most rapidly adopt these solutions in the AWS cloud, the marketplace is a fantastic accelerator to that. You >>Know, it's interesting. I wanna bring this up and get your reaction to it because to me, I think this is the future of procurement. So from a procurement standpoint, I mean, again, dating myself EDI back in the old days, you know, all that craziness. Now this is all the, all the internet, basically through the console, I get the infrastructure side, you know, spin up and provision. Some servers, all been good. You guys have played well there in the marketplace. But now as we get into more of what I call the business apps, and they brought this up on stage little nuance, most enterprises aren't yet there of integrating tech on the business apps, into the stack. This is where I think you guys are a use case of success where you guys have been successful with data integration. It's an integrator's dilemma, not an innovator's dilemma. So like, I want to integrate, so now I have integration points with data bricks, but I want to put an app in there. I want to provision an application, but it has to be built. It's not, you don't buy it. You build, you gotta build stuff. And this is the nuance. What's your reaction to that? Am I getting this right? Or, or am I off because no, one's gonna be buying software. Like they used to, they buy software to integrate it. >>Yeah, >>No, I, cause everything's integrated. >>I think AWS has done a great job at creating a partner ecosystem, right. To give customers the right tools for the right jobs. And those might be with third parties, data bricks is doing the same thing with our partner connect program. Right. We've got customer, customer partners like five tra and D V T that, you know, augment and enhance our platform. And so you, you're looking at multi ISV architectures and all of that can be procured through the AWS marketplace. >>Yeah. It's almost like, you know, bundling and unbundling. I was talking about this with, with Dave ante about Supercloud, which is why wouldn't a customer want the best solution in their architecture period. And it's class. If someone's got API security or an API gateway. Well, you know, I don't wanna be forced to buy something because it's part of a suite and that's where you see things get suboptimized where someone dominates a category and they have, oh, you gotta buy my version of this. Yeah. >>Joel, Joel. And that's Joel and I were talking, we're actually saying what what's really important about Databricks is that customers control the data. Right? You wanna comment on that? >>Yeah. I was say the, you know what you're pushing on there we think is extraordinarily, you know, the way the market is gonna go is that customers want a lot of control over how they build their data stack. And everyone's unique in what tools are the right ones for them. And so one of the, you know, philosophically I think really strong places, data, bricks, and AWS have lined up is we both take an approach that you should be able to have maximum flexibility on the platform. And as we think about the lake house, one thing we've always been extremely committed to as a company is building the data platform on an open foundation. And we do that primarily through Delta lake and making sure that to Jack's point with data bricks, the data is always in your control. And then it's always stored in a completely open format. And that is one of the things that's allowed data bricks to have the breadth of integrations that it has with all the other data tools out there, because you're not tied into any proprietary format, but instead are able to take advantage of all the innovation that's happening out there in the open source ecosystem. >>When you see other solutions out there that aren't as open as you guys, you guys are very open by the way, we love that too. We think that's a great strategy, but what's the, what am I foreclosing? If I go with something else that's not as open what what's the customer's downside as you think about what's around the corner in the industry. Cuz if you believe it's gonna be open, open source, which I think opens our software is the software industry and integration is a big deal, cuz software's gonna be plentiful. Let's face it. It's a good time to be in software business, but cloud's booming. So what's the downside from your data bricks perspective, you see a buyer clicking on data bricks versus that alternative what's potentially is should they be a nervous about down the road if they go with a more proprietary or locked in approach? Well, >>I think the challenge with proprietary ecosystems is you become beholden to the ability of that provider to both build relationships and convince other vendors that they should invest in that format. But you're also then beholden to the pace at which that provider is able to innovate. And I think we've seen lots of times over history where, you know, a proprietary format may run ahead for a while on a lot of innovation. But as that market control begins to solidify that desire to innovate begins to, to degrade, whereas in the open format. So >>Extract rents versus innovation. Exactly. >>Yeah, exactly. >>But >>I'll say it in the open world, you know, you have to continue to innovate. Yeah. And the open source world is always innovating. If you look at the last 10 to 15 years, I challenge you to find, you know, an example where the innovation in the data and AI world is not coming from open source. And so by investing in open ecosystems, that means you were always going to be at the forefront of what is the >>Latest, you know, again, not to date myself again, but you look back at the eighties and nineties, the protocol stacked for proprietary. Yeah. You know, SNA at IBM deck net was digital, you know, the rest is, and then TCP, I P was part of the open systems, interconnect, revolutionary Oly, a big part of that as well as my school did. And so like, you know, that was, but it didn't standardize the whole stack. It stopped at IP and TCP. Yeah. But that helped interoperate, that created a nice defacto. So this is a big part of this mid game. I call it the chessboard, you know, you got opening game and mid game. Then you got the end game and we're not there. The end game yet cloud the cloud. >>There's, there's always some form of lock in, right. Andy jazzy will, will address it, you know, when making a decision. But if you're gonna make a decision you want to reduce as you don't wanna be limited. Right. So I would advise a customer that there could be limitations with a proprietary architecture. And if you look at what every customer's trying to become right now is an AI driven business. Right? And so it has to do with, can you get that data outta silos? Can you, can you organize it and secure it? And then can you work with data scientists to feed those models? Yeah. In a, in a very consistent manner. And so the tools of tomorrow will to Joel's point will be open and we want interoperability with those >>Tools and, and choice is a matter too. And I would say that, you know, the argument for why I think Amazon is not as locked in as maybe some other clouds is that they have to compete directly too. Redshift competes directly with a lot of other stuff, but they can't play the bundling game because the customers are getting savvy to the fact that if you try to bundle an inferior product with something else, it may not work great at all. And they're gonna be they're onto it. This is >>The Amazon's credit by having these, these solutions that may compete with native services in marketplace, they are providing customers with choice, low >>Price and access to the S and access to the core value. Exactly. Which the >>Hardware, which is their platform. Okay. So I wanna get you guys thought on something else. I, I see emerging, this is again kind of cube rumination moment. So on stage Chris unpacked, a lot of stuff. I mean this marketplace, they're touching a lot of hot buttons here, you know, pricing compensation, workflows services behind the curtain. And one of the things he mentioned was they talk about resellers or channel partners, depending upon what you talk about. We believe Dave and I believe on the cube that the entire indirect sales channel of the industry is gonna be disrupted radically because those players were selling hardware in the old days and software, that game is gonna change. You know, you mentioned you guys have a program, want to get your thoughts on this. We believe that once this gets set up, they can play in this game and bring their services in which means that the old reseller channels are gonna be rewritten. They're gonna be refactored with this new kinds of access. Cuz you've got scale, you've got money and you've got product and you got customers coming into the marketplace. So if you're like a reseller that sold computers to data centers or software, you know, value added reseller or V or business, >>You've gotta evolve. >>You gotta, you gotta be here. Yes. How are you guys working with those partners? Cuz you say you have a part in your marketplace there. How do I make money? If I'm a reseller with data bricks with eight Amazon, take me through that use case. >>Well I'll let Joel comment, but I think it's, it's, it's pretty straightforward, right? Customers need expertise. They need knowhow. When we're seeing customers do mass migrations to the cloud or Hadoop specific migrations or data transformation implementations, they need expertise from consulting and SI partners. If those consulting SI partners happen to resell the solution as well. Well, that's another aspect of their business, but I really think it is the expertise that the partners bring to help customers get outcomes. >>Joel, channel big opportunity for re re Amazon to reimagine this. >>For sure. Yeah. And I think, you know, to your comment about how to resellers take advantage of that, I think what Jack was pushing on is spot on, which is it's becoming more about more and more about the expertise you bring to the table and not just transacting the software, but now actually helping customers make the right choices. And we're seeing, you know, both SI begin to be able to resell solutions and finding a lot of opportunity in that. Yeah. And I think we're seeing traditional resellers begin to move into that SI model as well. And that's gonna be the evolution that >>This gets at the end of the day. It's about services for sure, for sure. You've got a great service. You're gonna have high gross profits. And >>I think that the managed service provider business is alive and well, right? Because there are a number of customers that want that, that type of a service. >>I think that's gonna be a really hot, hot button for you guys. I think being the way you guys are open this channel partner services model coming in to the fold really kind of makes for kind of that super cloudlike experience where you guys now have an ecosystem. And that's my next question. You guys have an ecosystem going on within data bricks for sure. On top of this ecosystem, how does that work? This is kinda like hasn't been written up in business school and case studies yet this is new. What is this? >>I think, you know, what it comes down to is you're seeing ecosystems begin to evolve around the data platforms and that's gonna be one of the big kind of new horizons for us as we think about what drives ecosystems it's going to be around. Well, what is the, what's the data platform that I'm using and then all the tools that have to encircle that to get my business done. And so I think there's, you know, absolutely ecosystems inside of the AWS business on all of AWS's services, across data analytics and AI. And then to your point, you are seeing ecosystems now arise around data bricks in its Lakehouse platform, as well as customers are looking at well, if I'm standing these Lakehouse up and I'm beginning to invest in this, then I need a whole set of tools that help me get that done as well. >>I mean you think about ecosystem theory, we're living a whole nother dream and I'm, and I'm not kidding. It hasn't yet been written up and for business school case studies is that we're now in a whole nother connective tissue ecology thing happening where you have dependencies and value proposition economics connectedness. So you have relationships in these ecosystems. >>And I think one of the great things about relationships with these ecosystems is that there's a high degree of overlap. Yeah. So you're seeing that, you know, the way that the cloud business is evolving, the, the ecosystem partners of data bricks are the same ecosystem partners of AWS. And so as you build these platforms out into the cloud, you're able to really take advantage of best of breed, the broadest set of solutions out there for >>You. Joel, Jack, I love it because you know what it means the best ecosystem will win. If you keep it open. Sure. You can see everything. If you're gonna do it in the dark, you know, you don't know the outcome. I mean, this is really kind we're talking about. >>And John, can I just add that when I was in Amazon, we had a, a theory that there's buyers and builders, right? There's very innovative companies that want to build things themselves. We're seeing now that that builders want to buy a platform. Right? Yeah. And so there's a platform decision being made and that ecosystem gonna evolve around the >>Platform. Yeah. And I totally agree. And, and, and the word innovation get kicks around. That's why, you know, when we had our super cloud panel was called the innovators dilemma with a slash through it called the integrated dilemma, innovation is the digital transformation. So absolutely like that becomes cliche in a way, but it really becomes more of a, are you open? Are you integrating if APIs are the connective tissue, what's automation, what's the service message look like. I mean, a whole nother set of kind of thinking goes on and these new ecosystems and these new products >>And that, and that thinking is, has been born in Delta sharing. Right? So the idea that you can have a multi-cloud implementation of data bricks, and actually share data between those two different clouds, that is the next layer on top of the native cloud >>Solution. Well, data bricks has done a good job of building on top of the goodness of, and the CapEx gift from AWS. But you guys have done a great job taking that building differentiation into the product. You guys have great customer base, great grow ecosystem. And again, I think in a shining example of what every enterprise is going to do, build on top of something operating model, get that operating model, driving revenue. >>Yeah. >>Well we, whether whether you're Goldman Sachs or capital one or XYZ corporation >>S and P global NASDAQ, right. We've got, you know, these, the biggest verticals in the world are solving tough problems with data breaks. I think we'd be remiss cuz if Ali was here, he would really want to thank Amazon for all of the investments across all of the different functions, whether it's the relationship we have with our engineering and service teams. Yeah. Our marketing teams, you know, product development and we're gonna be at reinvent the big presence of reinvent. We're looking forward to seeing you there again. >>Yeah. We'll see you guys there. Yeah. Again, good ecosystem. I love the ecosystem evolutions happening this next gen cloud is here. We're seeing this evolve kind of new economics, new value propositions kind of scaling up, producing more so you guys are doing a great job. Thanks for coming on the Cuban, taking time. Chill. Great to see you at the check. Thanks for having us. Thanks. Going. Okay. Cube coverage here. The world's changing as APN comes to give the marketplace for a new partner organization at Amazon web services, the Cube's got a covered. This should be a very big growing ecosystem as this continues, billions of being sold through the marketplace. Of course the buyers are happy as well. So we've got it all covered. I'm John furry, your host of the cube. Thanks for watching.

>>We'll come back to the cubes coverage of AWS reinvent 2021, the industry's most important hybrid event. Very few hybrid events, of course, in the last two years. And the cube is excited to be here. Uh, this is our ninth year covering AWS reinvent this the 10th reinvent we're here with Joel Minnick, who the vice president of product and partner marketing at smoking hot company, Databricks and Greg Rokita, who is executive director of technology at Edmonds. If you're buying a car or leasing a car, you gotta go to Edmund's. We're gonna talk about busting data silos, guys. Great to see you again. >>Welcome. Welcome. Glad to be here. >>All right. So Joel, what the heck is a lake house? This is all over the place. Everybody's talking about lake house. What is it? >>And it did well in a nutshell, a Lakehouse is the ability to have one unified platform to handle all of your traditional analytics workloads. So your BI and reporting Trisha, the lake, the workloads that you would have for your data warehouse on the same platform as the workloads that you would have for data science and machine learning. And so if you think about kind of the way that, uh, most organizations have built their infrastructure in the cloud today, what we have is generally customers will land all their data in a data lake and a data lake is fantastic because it's low cost, it's open. It's able to handle lots of different kinds of data. Um, but the challenges that data lakes have is that they don't necessarily scale very well. It's very hard to govern data in a data lake house. It's very hard to manage that data in a data lake, sorry, in a, in a data lake. >>And so what happens is that customers then move the data out of a data lake into downstream systems and what they tend to move it into our data warehouses to handle those traditional reporting kinds of workloads that they have. And they do that because data warehouses are really great at being able to have really great scale, have really great performance. The challenge though, is that data warehouses really only work for structured data. And regardless of what kind of data warehouse you adopt, all data warehouse and platforms today are built on some kind of proprietary format. So once you've put that data into the data warehouse, that's, that is kind of what you're locked into. The promise of the data lake house was to say, look, what if we could strip away all of that complexity and having to move data back and forth between all these different systems and keep the data exactly where it is today and where it is today is in the data lake. >>And then being able to apply a transaction layer on top of that. And the Databricks case, we do that through a technology and open source technology called data lake, or sorry, Delta lake. And what Delta lake allows us to do is when you need it, apply that performance, that reliability, that quality, that scale that you would expect out of a data warehouse directly on your data lake. And if I can do that, then what I'm able to do now is operate from one single source of truth that handles all of my analytics workloads, both my traditional analytics workloads and my data science and machine learning workloads, and being able to have all of those workloads on one common platform. It means that now not only do I get much, much more simple in the way that my infrastructure works and therefore able to operate at much lower costs, able to get things to production much, much faster. >>Um, but I'm also able to now to leverage open source in a much bigger way being that lake house is inherently built on an open platform. Okay. So I'm no longer locked into any kind of data format. And finally, probably one of the most, uh, lasting benefits of a lake house is that all the roles that have to take that have to touch my data for my data engineers, to my data analyst, my data scientists, they're all working on the same data, which means that collaboration that has to happen to go answer really hard problems with data. I'm now able to do much, much more easy because those silos that traditionally exist inside of my environment no longer have to be there. And so Lakehouse is that is the promise to have one single source of truth, one unified platform for all of my data. Okay, >>Great. Thank you for that very cogent description of what a lake house is now. Let's I want to hear from the customer to see, okay, this is what he just said. True. So actually, let me ask you this, Greg, because the other problem that you, you didn't mention about the data lake is that with no schema on, right, it gets messy and Databricks, I think, correct me if I'm wrong, has begun to solve that problem, right? Through series of tooling and AI. That's what Delta liked us. It's a man, like it's a managed service. Everybody thought you were going to be like the cloud era of spark and Brittany Britain, a brilliant move to create a managed service. And it's worked great. Now everybody has a managed service, but so can you paint a picture at Edmonds as to what you're doing with, maybe take us through your journey the early days of a dupe, a data lake. Oh, that sounds good. Throw it in there, paint a picture as to how you guys are using data and then tie it into what y'all just said. >>As Joel said, that they'll the, it simplifies the architecture quite a bit. Um, in a modern enterprise, you have to deal with a variety of different data sources, structured semi-structured and unstructured in the form of images and videos. And with Delta lake and built a lake, you can have one system that handles all those data sources. So what that does is that basically removes the issue of multiple systems that you have to administer. It lowers the cost, and it provides consistency. If you have multiple systems that deal with data, you always arise as the issue as to which data has to be loaded into which system. And then you have issues with consistency. Once you have issues with consistency, business users, as analysts will stop trusting your data. So that was very critical for us to unify the system of data handling in the one place. >>Additionally, you have a massive scalability. So, um, I went to the talk with from apple saying that, you know, he can process two years worth of data. Instead of just two days in an Edmonds, we have this use case of backfilling the data. So often we changed the logic and went to new. We need to reprocess massive amounts of data with the lake house. We can reprocess months worth of data in, in a matter of minutes or hours. And additionally at the data lake houses based on open, uh, open standards, like parquet that allowed us, allowed us to basically hope open source and third-party tools on top of the Delta lake house. Um, for example, a Mattson, we use a Matson for data discovery, and finally, uh, the lake house approach allows us for different skillsets of people to work on the same source data. We have analysts, we have, uh, data engineers, we have statisticians and data scientists using their own programming languages, but working on the same core of data sets without worrying about duplicating data and consistency issues between the teams. >>So what, what is, what are the primary use cases where you're using house Lakehouse Delta? >>So, um, we work, uh, we have several use cases, one of them more interesting and important use cases as vehicle pricing, you have used the Edmonds. So, you know, you go to our website and you use it to research vehicles, but it turns out that pricing and knowing whether you're getting a good or bad deal is critical for our, uh, for our business. So with the lake house, we were able to develop a data pipeline that ingests the transactions, curates the transactions, cleans them, and then feeds that curated a curated feed into the machine learning model that is also deployed on the lake house. So you have one system that handles this huge complexity. And, um, as you know, it's very hard to find unicorns that know all those technologies, but because we have flexibility of using Scala, Java, uh, Python and SQL, we have different people working on different parts of that pipeline on the same system and on the same data. So, um, having Lakehouse really enabled us to be very agile and allowed us to deploy new sources easily when we, when they arrived and fine tune the model to decrease the error rates for the price prediction. So that process is ongoing and it's, it's a very agile process that kind of takes advantage of the, of the different skill sets of different people on one system. >>Because you know, you guys democratized by car buying, well, at least the data around car buying because as a consumer now, you know, I know what they're paying and I can go in of course, but they changed their algorithms as well. I mean, the, the dealers got really smart and then they got kickbacks from the manufacturer. So you had to get smarter. So it's, it's, it's a moving target, I guess. >>Great. The pricing is actually very complex. Like I, I don't have time to explain it to you, but knowing, especially in this crazy market inflationary market where used car prices are like 38% higher year over year, and new car prices are like 10% higher and they're changing rapidly. So having very responsive pricing model is, is extremely critical. Uh, you, I don't know if you're familiar with Zillow. I mean, they almost went out of business because they mispriced their, uh, their houses. So, so if you own their stock, you probably under shorthand of it, but, you know, >>No, but it's true because I, my lease came up in the middle of the pandemic and I went to Edmonds, say, what's this car worth? It was worth like $7,000. More than that. Then the buyout costs the residual value. I said, I'm taking it, can't pass up that deal. And so you have to be flexible. You're saying the premises though, that open source technology and Delta lake and lake house enabled that flexible. >>Yes, we are able to ingest new transactions daily recalculate our model within less than an hour and deploy the new model with new pricing, you know, almost real time. So, uh, in this environment, it's very critical that you kind of keep up to date and ingest their latest transactions as they prices change and recalculate your model that predicts the future prices. >>Because the business lines inside of Edmond interact with the data teams, you mentioned data engineers, data scientists, analysts, how do the business people get access to their data? >>Originally, we only had a core team that was using Lakehouse, but because the usage was so powerful and easy, we were able to democratize it across our units. So other teams within software engineering picked it up and then analysts picked it up. And then even business users started using the dashboarding and seeing, you know, how the price has changed over time and seeing other, other metrics within the, >>What did that do for data quality? Because I feel like if I'm a business person, I might have context of the data that an analyst might not have. If they're part of a team that's servicing all these lines of business, did you find that the data quality, the collaboration affected data? >>Th the biggest thing for us was the fact that we don't have multiple systems now. So you don't have to load the data. Whenever you have to load the data from one system to another, there is always a lag. There's always a delay. There is always a problematic job that didn't do the copy correctly. And the quality is uncertain. You don't know which system tells you the truth. Now we just have one layer of data. Whether you do reports, whether you're data processing or whether you do modeling, they all read the same data. And the second thing is that with the dashboarding capabilities, people that were not very technical that before we could only use Tableau and Tableau is not the easiest thing to use as if you're not technical. Now they can use it. So anyone can see how our pricing data looks, whether you're an executive, whether you're an analyst or a casual business users, >>But Hey, so many questions, you guys are gonna have to combat. I'm gonna run out of time, but you now allow a consumer to buy a car directly. Yes. Right? So that's a new service that you launched. I presume that required new data. We give, we >>Give consumers offers. Yes. And, and that offer you >>Offered to buy my league. >>Exactly. And that offer leverages the pricing that we develop on top of the lake house. So the most important thing is accurately giving you a very good offer price, right? So if we give you a price, that's not so good. You're going to go somewhere else. If we give you price, that's too high, we're going to go bankrupt like Zillow debt, right. >>It took to enable that you're working off the same dataset. Yes. You're going to have to spin up a, did you have to inject new data? Was there a new data source that we're working on? >>Once we curate the data sources and once we clean it, we see the directly to the model. And all of those components are running on the lake house, whether you're curating the data, cleaning it or running the model. The nice thing about lake house is that machine learning is the first class citizen. If you use something like snowflake, I'm not going to slam snowflake here, but you >>Have two different use case. You have >>To, you have to load it into a different system later. You have to load it into a different system. So like good luck doing machine learning on snowflake. Right. >>Whereas, whereas Databricks, that's kind of your raison d'etre >>So what are your, your, your data engineer? I feel like I should be a salesman or something. Yeah. I'm not, I'm not saying that. Just, just because, you know, I was told to, like, I'm saying it because of that's our use case, >>Your use case. So question for each of you, what, what business results did you see when you went to kind of pre lake house, post lake house? What are the, any metrics you can share? And then I wonder, Joel, if you could share a sort of broader what you're seeing across your customer base, but Greg, what can you tell us? Well, >>Uh, before their lake house, we had two different systems. We had one for processing, which was still data breaks. And the second one for serving and we iterated over Nateeza or Redshift, but we figured that maintaining two different system and loading data from one to the other was a huge overhead administration security costs. Um, the fact that you had to consistency issues. So the fact that you can have one system, um, with, uh, centralized data, solves all those issues. You have to have one security mechanism, one administrative mechanism, and you don't have to load the data from one system to the other. You don't have to make compromises. >>It's scale is not a problem because of the cloud, >>Because you can spend clusters at will for different use cases. So your clusters are independent. You have processing clusters that are not affecting your serving clusters. So, um, in the past, if you were running a serving, say on Nateeza or Redshift, if you were doing heavy processing, your reports would be affected, but now all those clusters are separated. So >>Consumer data consumer can take that data from the producer independ >>Using its own cluster. Okay. >>Yeah. I'll give you the final word, Joel. I know it's been, I said, you guys got to come back. This is what have you seen broadly? >>Yeah. Well, I mean, I think Greg's point about scale. It's an interesting one. So if you look at cross the entire Databricks platform, the platform is launching 9 million VMs every day. Um, and we're in total processing over nine exabytes a month. So in terms of just how much data the platform is able to flow through it, uh, and still maintain a extremely high performance is, is bar none out there. And then in terms of, if you look at just kind of the macro environment of what's happening out there, you know, I think what's been most exciting to watch or what customers are experiencing traditionally or, uh, on the traditional data warehouse and kinds of workloads, because I think that's where the promise of lake house really comes into its own is saying, yes, I can run these traditional data warehousing workloads that require a high concurrency high scale, high performance directly on my data lake. >>And, uh, I think probably the two most salient data points to raise up there is, uh, just last month, Databricks announced it's set the world record for the, for the, uh, TPC D S 100 terabyte benchmark. So that is a place where Databricks at the lake house architecture, that benchmark is built to measure data warehouse performance and the lake house beat data warehouse and sat their own game in terms of overall performance. And then what's that spends from a price performance standpoint, it's customers on Databricks right now are able to enjoy that level of performance at 12 X better price performance than what cloud data warehouses provide. So not only are we jumping on this extremely high scale and performance, but we're able to do it much, much more efficiently. >>We're gonna need a whole nother section second segment to talk about benchmarking that guys. Thanks so much, really interesting session and thank you and best of luck to both join the show. Thank you for having us. Very welcome. Okay. Keep it right there. Everybody you're watching the cube, the leader in high-tech coverage at AWS reinvent 2021

>> Announcer: From around the globe, it's theCUBE with digital coverage of AWS Public Sector Partner Awards brought to you by Amazon web services. >> Hi, and welcome back. I'm Stu Miniman. This is theCUBE coverage of the AWS Public Sector Partner Awards. We going to be talking about the Customer Obsession Mission award winner. So happy to welcome to the program. First of all, welcoming back Joel Lipkin. He is the chief operating officer of Four Points Technologies, which is the winner of the aforementioned award and joining him one of his customers, Ryan Hillard, who is a assistant developer with the United States, Small Business Administration, and of course the SBA, an organization that a lot of people in the United States have gotten more familiar with this year. Joel and Ryan, thanks so much for joining us. >> Hi Stu? >> Hey Stu; Thank you. >> All right, so Ryan, I'm sorry, Joel, as I mentioned, you've been on the program, but maybe just give us a sketch if you would, Four Points, your role, your partnership with AWS. >> Sure, I'm Joel Lipkin. I'm the chief operating officer at Four Points Technology, Four Points is a value added technology reseller focused on the federal government and we've been working with federal customer since 2002. We're a service disabled veteran owned small business, and we've been in a Amazon partner since 2012. >> Wonderful; Ryan, if you could, obviously, as I mentioned, the SBA, a lot of people know for the PPP in 2020, if you could tell us a little bit about your role in your organization and tee up for us, if you would, the project that Four Points was involved with that you worked on. >> Sure; so I worked for the chief information officer and I don't have this official title, but I am the de facto manager of our Amazon Web Services presence. This year, we've had a very exciting time with what's been happening in the world, the Paycheck Protection Program, and the SBA have been kind of leveraged to help the US economy recover in the face of the pandemic. And a key part of that has been using Amazon Web Services and our partnership with Four Points Technology to launch new applications to address those requirements. >> Wonderful; Joel, maybe a connect for us. How long has Four Points been working with the SBA and start to give us a little bit more about the projects that you're working together, which I understand was predated the COVID incidents. >> Sure; we've been with SBA for several years now. And SBA was one of the earlier federal agencies that really saw the value in separating their procurement for cloud capacity, from the development implementation and managed services that they either did internally or use third party contractors for. So, Four Points came in as a true value added reseller of cloud to SBA providing cloud capacity and also Amazon professionals services. >> All right; so Ryan bring us in a little bit, the project that we're talking about here, what was the challenge? What were the goals you were looking to accomplish? Help flush out a little bit, what you're doing there? >> Yeah, so most recently Four Points partnered with us to deliver Lender Gateway. Lender Gateway is an application for small community oriented lenders to submit Paycheck Protection loans. So some of these lenders don't have giant established IT departments like big banks do, and they needed an easier way to help their customers. We built that application in six days and I called the Four Points cloud manager on a Saturday, and I said, help, help, I need two accounts by three o'clock and Four Points was there for us. We got new accounts set up. We were able to build the application and deploy it literally in a week and meet the requirements set for us. And that system has now moved billions of dollars of loans. I don't know the exact amount, but has done an incredible amount of work and it wouldn't have been possible without our partnership with Four Points. So we're really excited about that. >> Yeah, If I could drill in there for a second. Absolutely it's been an unprecedented, how fast that amount of money move through the legislature to out to the end user. Help us understand a little bit, how much were you using AWS technologies and solutions that Four Points had helped you with, and how much of this was kind of a net new, you said you built a new application, you had to activate some things fast, help us understand a little bit more. >> Yeah, that's so that's a great question. So we have five major systems in AWS today. And so we're very comfortable with AWS service offerings. What's interesting about Lender Gateway is that it's the first application we've built from scratch in a totally serverless capacity. So one of the hard technical requirements of the Paycheck Protection Program is that, it has huge amounts of demand. So when we're launching a system, we need to know that that system will not go down no matter how much traffic it receives or how many requests it has to handle. So we leaned on services like AWS Lambda, S3, dynamoDB, all of their serverless offerings to make sure that under no circumstances could this application fail. And it never did. We never even actually saw a performance degradation. So a massive success from my perspective as the program manager. >> well, that's wonderful. Joel, of course, you talk about scalability, you talk about uptime. Those are really the promise the public cloud has brought. Ryan did a good job of teeing out some of the services from AWS, but help us understand architecturally how you help put that together, and, the various pieces underneath. >> Yes Stu, it's interesting. Four Points is really focused on delivering capacity. Our delivery model is very much built around giving our customers like Ryan full control over their cloud environments so that they can use it as transparently as though they were working with Amazon directly. They have access to all of the 200+ services that AWS has. They also have a direct access to billing and usage information that lets them really optimize things. So this is sort of a perfect example of how well that works because SBA and Ryan knew their requirements better than anyone. And they were able to leverage exactly the right AWS tools without having to apply to use them. It was as though they were working directly with AWS and the AWS environment on the technology side. And I will say SBA has been really a leader in using of variety of AWS services beyond standard compute and storage, not just in a tested environment, but in a live very, very robust, really large environment. >> Yeah, right, and I was excited to hear about your Lambda usage, how you're building with the serverless architecture there. Could you just bring us through a little bit, how you ramped up on that, any tools or community solutions that you were leveraging to make sure you understood that and any lessons you learned along the way as you were building that application and rolling it out? >> Yeah, that's a great question. So I think one of the mistakes that I see program managers make all the time is thinking that they can migrate a workload to the cloud and keep it architecturally the same way it was. And what they quickly find out is that their old architecture that ran in their on premise data center might actually be more expensive in the cloud than it was in their data center. And so when you're thinking about migrating a workload, you really need to come in with the assumption that you will actually be redesigning that workload and building the system in cloud native technology. You know, the concept of Lambda is so powerful, but it didn't exist for, you know, it didn't exist 20 years ago when some of these systems and applications were being written and now being able to leverage Lambda to only use exactly the compute you need, means you can literally pay pennies on the dollar. One of the interesting things about the PPP program and everything happening in the world is that our main website, sba.gov is now serving a a hundred or a thousand times more traffic daily than it was used to doing. But because we lean on serverless technology like Lambda, we have scaled non-linearly in terms of costs. So we're only paying like two or three times more than we used to pay per month, but we're doing a hundred or a thousand times more work. That's a win, that's a huge victory for cloud technology, in my opinion. >> Yeah, and on that point, I think the other thing that SBA did really amazingly well was take advantage of first reserved instances. But I think it was the day that Amazon announced savings plans as a cost control mechanism. Ryan and SBA were on them. They were our first customer to use savings plans. And I think there were probably the first customer in the federal space to use them. So it's not just using the technology smart, it's using the cost control tools really well also. >> Yeah, so Stu, I wanted to jump in here just because I'm so glad Joel brought that up. I was describing how workloads need to morph and transform as they move from legacy setups into more cloud native ones. Well, we were the first federal agency to buy savings plans. And for folks who don't know savings plans essentially make your reserved instances fungible across services. So if you had a workload that was running on EC2 before, now instead of buying a reserved instance at a certain instant size, a certain family, you can instead buy a savings plan. And when your workload is ready to be moved from EC2 to something a little bit more containerized or cloud native, like Fargate or Lambda, then you don't actually forego your reserved instance. I see program managers get into this weird spot where they bought reserved instances, so they feel like they need to use them for a whole year. So they don't upgrade their system until their reserved instances expire. And that's really the tail wagging the dog. We were very excited about savings plans. I think we bought them four days after they came out and they have enabled us to do things like, be very ambitious with how we rethink our systems and how we rebuild them. And I'm so glad you brought that up to all because it's been such a key thing over this last year. >> Yeah, it's been a really interesting discussion point I've been having the last few years, is that the role between developers and that, that finance piece. So, Ryan, who is it that advises you on this? Is there somebody on the finance team from the SBA? is it Four Points? You know, being aware of savings plan, it was something that was announced at Reinvent, but it takes a while for that to trickle and oftentimes developers don't need to think about or think that they don't need to think about the financial implications of how they're architecting things. So how, how does that communication and decision making happen? >> That's such a great question. I think it goes back to how Four Points is customer obsessed. One of our favorite things about using a small business reseller like Four Points instead of dealing directly with our cloud service provider is that Four Points provides us a service where every quarter they do an independent assessment of our systems, how much we're spending and what that looks like from a service breakdown. And then we get that perspective and that opinion, and we enrich it with our conversation with our AWS account manager, with our finance people. But having that third party independent person come in and say, "Hey, this is what we think" has been so powerful because Joel and Dana and team have always had observations that nobody else has had. And those kinds of insights are nice to have, when you have people who are suspicious of a vendor telling you to buy more things with them, because they're the vendor >> From the lessons you've learned there, any final advice that you'd give to your peers out there, and how will you take what you've learned working on this project to other things, either in the SBA or in talking with your peers in other organizations. >> So I have two big things. So one is go use a small business reseller. I would be remiss if I didn't use this opportunity to tell you as a member of the US Small Business Administration, that there are some really, really great service providers out there. They are part of our programs like Four Points, and they can help you achieve that balance between trusting your cloud service provider and having that a third party entity that can come in and, call bowl and also call Yahtzee. So recognize good things and recognize bad things. So that would be number one. And then number two is moving to the cloud is so often sold as a technology project. And it's like 20% technology and 80% culture and workforce change. And so be honest with yourselves and your executive teams that this isn't a technology project. This is, we going to change how we do business project, and we going to change the culture of this organization kind of project. >> All right; and Joel, I'll let you have the final word on lessons learned here and also about Four Points and congratulations again, the Customer Obsession Mission award winner. >> Great, thanks Stu, we're so appreciative to Amazon for their recognition and to Ryan and SBA for giving us the opportunity to support such an important program. We are a small business, we are very much focused on delivering what our customers need in the cloud. And it's just such a tremendous feeling to be able to work on a program like this that has such, such payoff for the whole country. >> All right, Well, Joel and Ryan, thank you so much for sharing your updates, such an important project this year. Thanks so much. >> Thank you Stu. >> Thanks >> Stay with us for more covered from the AWS Public Sector Partner awards. I'm Stu Miniman, and thank you for watching theCUBE.

>> Narrator: From around the globe, it's theCUBE with digital coverage of AWS Public Sector Partner Awards brought to you by Amazon Web Services. Hello everyone, welcome back to theCUBE's Coverage of "AWS Public Sector Partner Awards Program". I'm John Furrier your host of theCUBE here in Palo Alto, California doing the remote interviews, during this pandemic we have our remote crews and getting all the stories and celebrating the award winners and here to feature the most Innovative Connect Deployment. We have Accenture of Canada and the Department of Employment and Social Development of Canada known as ESDC. Guys, congratulations Joel Marchildon, Accenture Canada, managing director and Benoit Long, ESDC of Canada chief transformation officer. Gentlemen, thanks for coming on, and congratulations on the award. >> Thank you. >> Thank you and nice to be here >> So obviously, during this pandemic, a lot of disruption and a lot of business still needs to go on including government services. But the citizens and people need to still do their thing you got a business got to run, and you got to get things going. But the disruptions caused a little bit of how the user experiences are. So this Connect has been interesting. Its been a featured part of what we've been hearing at the Public Sector Summit with Teresa Carlson. You guys, this is a key product. Tell us about the award. What is the solution that is starving and deserving of the award? >> Maybe I'll go first and then pass it over to Benoit. But I think the solution is Amazon Connect based Virtual Contact Center that was stood up fairly quickly, over the course of about four days and really in support of benefit that the Government of Canada was was releasing as part of its economic response to the pandemic. And in the end, its a fully functioning featured contact center solution. Includes an IVR. And, we stood it up for about 1500 to 2000 agents. So that's the the crux of the solution. And maybe Benoit can give a bit of insight as to how it came about so quickly. >> Yeah, we're happy to actually, we were obviously like every other government facing enormous pressures at that time to deliver benefits directly to people who were in true need. The jobs are being lost, our current systems were in trouble because of their age and their archaic nature. And so the challenge was quickly how do we actually support a lot of people really fast. And so it came through immediately that after our initial payments were made under what was called the Canada emergency response benefit that we had to support clients directly and so people turn to the transformation team of all teams. If you wish during a firestorm, to say, well, what could you do? And how could you help. And so we had an established relationship with a number of our system integrators, including Accenture. And we were able to run a competition very rapidly, and Accenture won. And then we deployed in, as Joel said, in a matter of four days, what for us was an exceptional and high quality solution to a significant client problem. And I say that because I think you can imagine how people feel in a pandemic of all things, but with the uncertainty that comes with loss of income, loss of jobs, the question of being able to deal with somebody a real; a human being, as well as to be able to efficiently answer a very simple but straightforward questions rapidly and with high quality, was pretty fundamental for us. So the the people in the groups that we're talking through here we're speaking to millions of people, who were literally being asked to accept the payment rapidly and to be able to connect with us quickly. And without this solution, which was exceptionally well done and of high quality personally as a technology solution, it would not have been possible to even answer any of these queries quickly. >> And well, that's a great point. One of the things that you see with the pandemic, its a disaster in the quote disaster kind of readiness thing. Unforeseen, right. So like other things, you can kind of plan for things, hypothetical, you got scenarios. But this is truly a case where every day counts, every minute counts, because humans are involved. There's no ROI calculation. Its not like, well, what's the payback of our system? The old kind of way to think. This is real results, fast. This is what cloud is all about. This is the promise of cloud, can I stand up something quick, and you did it with a partner, okay. This is like not, like normal. Its like, its like unheard of, right. Four days, with critical infrastructure, critical services that were unforeseen. Take us through what was going on in the war room. As you guys knew this was here. Take us through through what happened. >> So I think I can start. As you can imagine the set of executives that were overseeing the payment process was an exceptional, it was like a bunker, frankly, for about two weeks. We had to suspend the normal operations of the vast majority of our programming. We had to launch brand new payments and benefits systems and programs that nobody has seen before the level of simplicity was maximized in order to deliver the funds quickly. So you can imagine its a Warpath if you wish, because the campaign is really around timing. Timing is fundamental. People are literally losing their jobs, there is no support, there is no funding money for them to be able to buy groceries. So, and the trust that people have in the government is pretty much at risk right there. And there is straightforward but extraordinarily powerful magic moment, if you wish. If you can deliver a solution, then you make a difference for a long time. And so the speed is unheard of on all fronts. When it came to the call center capability and the ability for us to support in a service context, the clients that were desperate to reach us, and we're talking hundreds of thousands of calls a day. We're not talking a few thousand here, ultimately, at some point we were literally getting in overtaken by volumes, call centers, because we had our regular ones still operating. Over a million calls were coming in the day. With the capacity to answer 10s of thousands and so the reality is that the Call Centers that we put up here, very quickly became capable of answering more calls than our regular call centers. And that speaks to the the speed of delivery, the quality of the solution, of course, but the scalability of it. And I have to say maybe unheard of, it may be difficult to replicate the conditions to lead to this are rare. But I have to say that my bosses and most of the government is probably now wondering why we can't do this more often. Why can't we operate with that kind of speed and agility. So I think what you've got is a client in our case, under extreme circumstances, now realizing the new normal will never be the same. That these types of solutions and technology and their scalability, their agility, their speed of deployment, is frankly something we want we want all the time. Now we'd like to be able to do them during normal timeline conditions, but even those will be a fraction of what it used to take. It would have taken us a while I can actually tell you because I was the lead technologist to deploy at scale for the government, Canada, all the call center capabilities under a single software as a service platform. It took us two years to design it two years to procure it, and five years to install it. That's the last experience we have of call center, enterprise scale capabilities. And in this case we went from years, to literally days. >> Well, it takes a crisis sometimes to kind of wire up the simplicity solution that you say, why didn't we do this before? The waterfall meetings getting everyone arguing kind of gets in the way and the old software model, I want to come back to the transformation Benoit a minute, Because I think that's going to be a great success story and some learnings and I want to get your thoughts on that. But I want to go to Joel, because Joel, we've talked to many Accenture executives over the years and most recently, this past 24 months. And the message we've been hearing is, "We're going to be faster. We're not going to be seen as that, a consulting firm, taking our times trying to get a pound of flesh from the client." This is an example of my opinion of a partner working with a problem statement that kind of matches the cloud speed. So you guys have been doing this is not new to Accenture. So take us through how you guys reacted, because one, you got to sync up and get the cadence of what Benoit was trying to do sync up and execute take us through what happened on your side. >> Yeah, I mean, so its an unprecedented way of operating for us as well, frankly. And, we've had to look at, to get this specific solution out the door and respond to an RFP and the commercial requirements that go with that we had to get pretty agile ourselves internally on, how we go through approvals, etc, to make sure that we were there to support Benoit and his team and I think that we saw this as a broader opportunity to really respond to it. To help Canada in a time of need. So I think we had to streamline a lot of our internal processes and make quick decisions that normally even for our organization would have taken, could have taken weeks, right, and we were down to hours and a lot of instances. So it forces us to react and act differently as well. But I mean to Benoit's point I think this is really going to hopefully change the way... It illustrates the art of the possible and hopefully will change how quickly we can look at problems and we reduce deployment timeframes from years to months and months to weeks, etc. For solutions like this. And I think the AWS platform specifically in this case, Benoit touched on a lot of things beat the market scalability, but just as the benefit itself has to be simplified to do this quickly. I think one of the one of the benefits of the solution itself is, its simple to use technologically. I mean, we trained, as I said, I think 1600 agents on how to use the platform over the course of a weekend. And they're not normal agents. These were people who were furloughed from other jobs potentially within the government. So they're not necessarily contact center agents, by training, but they became contact center agents over the course of 48 hours. And I think, from that perspective, that was important as well to have something that people could use to answer those calls that we know that we knew were going to come. >> Benoit this is the transformation dream scenario in the sense of capabilities. I know its under circumstances of the pandemic and you guys did solve a big problem really fast and saved lives and then help people get on with their day. But transformation is about having people closest to the problem, execute. And also the people equation people process technology, as they say, is kind of playing out in real time. This is kind of the playbook. Amazon came in and said, "Hey, you want to stand something up?" You wired it together the solution quickly, you have close to it. Looking back now its almost like, hey, why aren't we doing this before, as you said, and then you had to bring people in, who weren't trained and stood them up and they were delivering the service. This is the playbook to share your thoughts on this because this is what you're you're thinking about all the time, and it actually is playing out in real time. >> Well, I would definitely endorse the idea that its a playbook. Its I would say its an ideal and dream playbook to bid like showing up on a basketball court with all the best players in the entire league playing together magically. It is exactly that. So a lot of things had to happen quickly but also correctly, because you can't pull all these things properly together without that. So I would say the partnership with the private sector here was fundamental. And I have to applaud the work that Accenture did particularly I think, as Canadians we were very proud of the fact that we needed to respond quickly. Everyone was in this our neighbors, we knew people who were without support and Accenture's team, I mean, all the way up and down across the organization was fundamental in and delivering this but also literally putting themselves into these roles and to make sure that we would be able to respond and quickly do so. I think the playbook around the readiness for change, I was shocked into existence. I mean, I won't talk about quantum physics, but clearly some higher level of energy was thrown in quickly, mobilize everybody all at once. Nobody was said he is sitting around saying, I wonder if we have changed management covered off, this was changed readiness at its best. And so I think for me from a learning perspective, apart from just the technology side, which is pretty fundamental, if you don't have ready enough technology to deploy quickly, then the best pay your plans in the world won't work. The reality is that to mobilize an organization going forward into that level of spontaneous driving change, exception, acceptance, and adoption, is really what I would aim for. And so our challenge now will be continuing that kind of progression going forward. And we now found the way and we certainly use the way to work with the private sector in an innovative capacity and innovative ways with brand new solutions that are truly agile and scalable, to be able to pull all of the organization all at once very rapidly and I have to admit that it is going to shift permanently our planning, we had 10 year plans for our big transformations, because some of our programs are the most important in the country in many ways. We support people about 8 million Canadians a month, depending on the benefits payments that we deliver. And they're the most marginal needing and requires our support from seniors, to the unemployed, to job seekers and whatnot. So if you think about that group itself, and to be able to support them clearly with the systems that we have its just unsustainable. But the new technologies are clearly going to show us a way that we had never forecast, and I have to say I had to throw up my 10 year plan. And now I'm working my way down from 10 to nine to eight year plans going forward. And so its exciting and nerve wracking sometimes, but then, obviously as a change leader, our goal is to get there as quickly as possible. So the benefits of all these solutions can make a difference in people's lives. >> What's interesting is that you can shorten that timetable, but also frees you up to be focused on what's contemporary and what's needed at the time to leverage the people and the resources you have. And take advantage of that versus having something that you're sitting on that's needs to be refreshed, you can always be on that bleeding edge. And this just brings up the DevOps kind of mindset, agility, the lean startup, the lean company, this is a team effort between Amazon Accenture and ESDC. Its, pass, shoot, score really fast. So this is the new reality. Any commentary from you guys on this, new pass, shoot, score combination because you got speed, you got agility, you're leaner, which makes you more flexible for being contemporary in solving problems? What's your thoughts? >> Yeah. So my perspective on that is most definitely right. I think what we were able to show in what's coming out of a lot of different responses to the pandemic by government is, perfection isn't the most important thing out of the gate, getting something out there that's going to reassure citizens, that's going to allow them to answer their questions or access benefits quickly, is what's becoming more important, obviously, security and privacy, those things are of the utmost importance as well. But its ability to get stuff out there, quickly, test it, change it, test it again, and just always be iterating on the solution. Like I can say what we put out on April 6, within four days, is the backbone of what's out there still today. But we've added an integrated workforce management solution from NICE, and we added some other ISVs to do outbound dialing from Acquia and things like that. So the solution has grown from that MVP. And I think that's one other thing that's going to be a big takeaway. If you're not going to do anything till you got the final end product out there, then its going to be late. So let's go quickly and let's adapt from there. >> Benoit, talk about that dynamic because that's about building blocks, on foundational things and then services. Its the cloud model. >> Yeah, I mean, before the pandemic, I had lunch with Mark Schwartz, which I believe you are quite familiar with. And, I spent an hour and a half with him. We were talking and he was so exciting and energized by what the technologies could do. And I was listening to him and I used to be the chief technology officer for the Government of Canada, right. And so I've seen a lot of stuff and I said, Well, that's really exciting. And I'm sure its possible in some other places, and maybe in some other countries where they didn't have infrastructure and legacy. I guess if I see him again soon. I'll have to apologize for not believing him enough. I think the building blocks of Agile the building blocks sprints and MVPs. I mean, they're enough fundamental to the way we're going to solve our biggest Harriers and scariest problems technologically. And then from a business perspective, service candidate itself has 18,000 employees involved in multiple channels, where the work has always been very lethargic, very difficult. Arduous you make change over years, not months, not days, for sure. And so I think that new method is not only a different way of working, its a completely revamped way of assembling solutions. And I think that the concept of engineering is probably going to be closer to what we're going to do. And I have to borrow the Lego metaphor, but the building blocks are going to be assembled. We know in working, I'm saying this in front of Joel, he doesn't know that yet. (all laughing) (indistinct) partners. We're going to be assembling MVP maps of an entire long program and its going to be iterative, it is going to be designed built, it will be agile as much as we can implement it. But more importantly, as much as we can govern it because the government is... We may have changed a lot, but the government is not necessarily caught on to most of these approaches. But the reality is that, that's where we're heading. And I will say, I'll close perhaps on this answer. The biggest reason for doing that apart from we've proved it is the fact that the appetite inside the organization for that level of mobilization, speed and solutioning, and being engaged rapidly, you just can't take that away from an organization once they've tasted that. If you let them down, well, they'll remember and frankly, they do remember now because they want more of this. And its going to be hard. But its a better hard, better challenge, than the one of having to do things over a decade, then to go fast and to kind of iterate quickly through the challenges and the issues and then move on very much to the next one as rapidly as possible. I think the the other comment I would add is most of this was driven by a client need. And that's not inconsequential because it mobilized everybody to a common focus. If it had been just about, well, we need to get people on side and solutions in place just to make our lives better as providers. Yeah, would it work perhaps, but it would have been different than the mobilization that comes when the client is put in the middle. The client is the focus, and then we drive everyone to that solution. >> Shared success and success is contagious. And when you ride the new wave, you're oh, we need a new board, right? So once you get it, it then spreads like wildfire. This is what we've been seeing. And it also translates down to the citizens because again, being contemporary, none of this just look could feel its success and performance. So as people in business start to adopt cloud. It becomes a nice synergy. This is a key! Joe, take us home here on the Accenture. The award winner, you guys did a great job. Final thoughts. >> Yeah, I mean, I think final thoughts would be happy to have had the opportunity to help. And it was a it was a complete team effort and continues to be. Its not a bunch of eccentric technologists in the background doing this. The commitment from everyone to get this in place and to continue to improve it from Benoit team and from other folks across the government has been paramount to the success. So its been a fantastic if world win like experience and look forward to continuing to build on it. And it has been well said, I think one thing that's done is its created demand for speed on some of these larger transformations. So I looking forward to continuing to innovate with with Benoit team. >> Well, congratulations for the most innovative Connect Deployment. And because you guys from Canada, I have to use the Hockey-Reference. You get multiple people working together in a cohesive manner. Its pass, shoot, score every time and its contagious. (Benoit laughs) Gentlemen, thank you very much for your time and congratulations for winning the election. Take care! >> Thanks. >> Take care. >> Okay, this is theCUBE's Coverage "AWS Public Sector Partner Awards" show. I'm John Furrier, host of theCUBE. Thanks for watching. (upbeat music)

>>from the Cube Studios in Palo Alto and Boston connecting with thought leaders all around the world. This is a cube conversation. >>Welcome back to the Cube's coverage of AWS Public Sector Partner Awards program. I'm John Furrow, your host of the Cube here in Palo Alto, California In the remote interviews during this pandemic, we have our remote crews and getting all the stories and celebrating the award winners. And here to feature the most innovative connect deployment. We have a center of Canada and the Department of Employment and Social Development of Canada, known as E S D. C guys. Congratulations, Joel. More Children Censure Canada Managing director and Ben while long sdc of Canada Chief Transformation officer. Gentlemen, thanks for coming on. And congratulations on the award. >>Thank you. >>Thank you. >>So, Ashley, during this pandemic, a lot of disruption and a lot of business still needs to go on, including government services. But the citizens and people need to still do their thing. Business got to run, and you got to get things going. But the disruptions caused a little bit of how the user experiences are. So this connect has been interesting. It's been a featured part of where you've been hearing at the Public Sector summit with Theresa Carlson. You guys, this is a key product. Tell us about the award. What is the solution? That disturbing of deserving reward? >>Maybe I'll get I'll go first and then pass it over to Benoit. But I think the solution is Amazon Connect based Virtual Contact Center that we stood up fairly quickly over the course of about four days and really in support of of benefit that the government of Canada was was releasing as part of its economic response to the pandemic. And in the end that, you know, it's a fully functioning featured contact center solution includes an I V r. And, uh, you know, we stood it up for about 1500 to 2000 agents so that that's the crux of the solution. And maybe Benoit can give a bit of insight as to to how it came about so quickly. >>Yeah, happy to actually wear obviously, like every other government, facing enormous pressures at that time to deliver benefits directly to people who were in true need, the jobs are being lost. Our current systems were in trouble because of their age and barricade cake nature. And so the challenge is was quickly how to actually support a lot of people really fast. And so it came through immediately that after our initial payments were made under what was called Canada Emergency Response Benefit, then we have to support our clients directly. And so people turn to the transformation team of all teams. If you wish during a fire firestorm to say, Well, what could you do and how could you help? And so we had an established relationship with a number of other system integrators, including Accenture, and we were able to run a competition very rapidly. Accenture one. And then we deployed. And as you all said, in a matter of four days, what for us was a new, exceptional on high quality solution to a significant client problem. And I say that because I think you can imagine how people feel in the endemic of all of all things. But with the uncertainty that comes with the loss of income, loss of jobs, the question of being able to deal with somebody really a human being, as well as to be able to be efficiently answer a very simple but straightforward questions rapidly and with high quality, with pretty fundamental for us. So the people in the groups that were talking through here are talking, speaking to millions of people who were literally being asked to to accept the pavement rapidly and to be able to connect with us quickly. And without this solution, which was exceptionally well done and deployed and of high quality personally, just a technology, uh, solution. I would not have been possible to even answer any of these queries quickly. >>And while that's a great 0.1 of the things that you see with the pandemic it's a disaster in the quote disaster kind of readiness thing. Unforeseen, right? So, like other things, you can kind of plan for things that hypothetical. You've got scenarios, but this >>is >>truly a case where every day counts. Every minute counts because humans are involved is no our ROI calculation. It's not like it's not like, Well, what's the payback of our system? The old kind of way to think this is really results fast. This is what cloud is all about. This is the promise of cloud. Can I stand up something quick and you did it with a partner. Okay, this is, like, not, like, normal again. It's like it's, you know, it's like, unheard of, right? Four days with critical infrastructure, critical services that were unforeseen. Take us through what was going on in the war room, as you guys knew this was here. Take us through the through what happened. Yeah, >>So I think I can start a Z. You can imagine the set of executives that we're seeing a payment process. Uh, was an exceptional. It was like a bunker. Frankly, for about two weeks, we had to suspend the normal operations off the vast majority of our programming. We had to launch brand new payments and benefits systems and programs that nobody had seen before. The level of simplicity was maximized to delivered the funds quickly. So you could imagine it's a warpath if you wish, because the campaign is really around. A timing. Timing is fundamental. People are are literally losing their jobs. There is no support. There's no funding money for them to be able to buy groceries. So on the trust that people have in the government, Ai's pretty much at risk right there and then in a very straightforward but extraordinarily powerful magic moment. If you wish. If you can deliver a solution, then you make a difference for a long time. And so the speed unheard off on old friends when he came to the call center capability and the ability for us to support and service context the clients that were desperate to reach us on. We're talking hundreds of thousands of calls, right? We're not talking a few 1000 year. Ultimately, at some point we were literally getting in our over over, taken by volumes, call centers. But we had a regular one still operating over a 1,000,000 calls for coming in today with the capacity to answer, um, you know, tens of thousands. And so the reality is that the counselor that we put up here very quickly became capable of answering more calls than our regular costumes. And that speaks to the speed of delivery, the quality of the solution, of course, but the scalability of it and I have to say, maybe unheard of, it may be difficult to replicate. The conditions to lead to this are rare, but I have to say that my bosses and most of the government is probably now wondering why we can't do this more often, like we can't operate with that kind of speed and agility. So I think what you've got is a client in our case, under extreme circumstances. Now, realizing the new normal will never be the same, that these types of solutions and technology. And then there's scalability. There's agility there, the speed of deployment. It's frankly, something we want. We want all the time. Now we'd like to be able to do it under your whole timeline conditions. But even those will be a fraction of what it used to take. It would have taken us well, actually, I can actually tell you because I was the lead, Ah, technologists to deploy at scale for the government. Canada all the call center capabilities under a single software as a service platform. It took us two years to design it two years to procure it and five years to install it. That's the last experience. We have a call center enterprise scale capabilities, and in this case, we went from years to literally days. >>Well, you know, it takes a crisis sometimes to kind of wire up the simplicity solution that you say. Why didn't we do this before? You know, the waterfall meetings, Getting everyone arguing gets kind of gets in the way of the old the old software model. I want to come back to the transformation been wanna minute, cause I think that's gonna be a great success story and some learnings, and I want to get your thoughts on that. But I want to go to Joel because Joel, we've talked to many Accenture executives over the years and most recently this past 24 months. And the message we've been hearing is we're going to be faster. We're not going to be seen as that. You know, a consulting firm taking our times. Try and get a pound of flesh from the client. This is an example. In my opinion of a partner working with a problem statement that kind of matches the cloud speed. So you guys have been doing this. This is not new to a censure. So take us through how you guys reacted because one you got to sync up and get the cadence of what, Ben? What I was trying to do sync up and execute. Take us through what happened on your side. >>Yeah, I mean, so it's It's Ah, it's an unprecedented way of operating for us as well, frankly, and, um and, uh and, you know, we've had to look at to get this specific solution at the door and respond to an RFP and the commercial requirements that go with that way. Had Teoh get pretty agile ourselves internally on on how we go through approvals, etcetera, to make sure that that we were there to support Ben Wan is team. And I think you know that we saw this is a broader opportunity to really respond to it, to help Canada in a time of need. So So I think we, you know, we had to streamline a lot of our internal processes that make quick decisions that normally even for our organization, would have taken, um, could it could have taken weeks, right? And we were down to hours in a lot of instances. So it helps. It forces us to react and act differently as well. But I mean, to Benoit's point, I think this is really going to to hopefully change the way it illustrates the art of the possible and hopefully will change How, How quick We can look at problems and and we reduced deployment timeframes from from years to months and months to weeks, etcetera for solutions like this. Um, and I think that the AWS platform specifically in this case but what touched on a lot of things to beat the market scale ability But just as the benefit itself was, you know has to be simplified to do this quickly. I think one of the one of the benefits of the solution itself is it's simple to use technologically. I mean, we know least retrained. As I said, I think 1600 agents on how to use the platform over the course of a weekend on and and were able, and they're not normal agents. These were people who are firm from other jobs, potentially within the government. So they're not necessarily contact center agents by training. But they became contact center agents over the course of 48 hours, and I think from that perspective, you know, that was important as well have something that people could could use. The answer those calls that we know that when you were gonna come so >>Ben what this is. This is the transformation dream scenario in the sense of capabilities. I know it's under circumstances of the pandemic, and you guys didn't solve a big, big problem really fast and saved lives and help people get on with their day. But transformations about having people closest to the problem execute and the the also the people equation people process technology, as they say, is kind of playing out in real time. This >>is >>the this is kind of the playbook, you know? Amazon came in said, Hey, you want to stand something up? You wired it together. The solution quickly. You're close to it. Looking back now, it's almost like, Hey, why aren't we doing this before? As you said and then you had to bring people in who weren't trained and stood them up and they were delivering the service. This >>is >>the playbook to share your thoughts on this, because this is what you're you're thinking about all the time and it actually playing out in real time. >>Well, I would definitely endorsed the idea that it's a playbook. It's I would say it's an ideal and dream playbook timidly showing up on the basketball court with all the best players in the entire league playing together magically, it is exactly that. So a lot of things have to happen quickly, but also, um, correctly because you know, you can't pull these things properly together without that. So I would say the partnership with the private sector here was fundamental, and I have to applaud the work that Accenture did particularly, I think, as Canadians, we're very proud of the fact that we needed to respond quickly. Everyone was in this, our neighbors, we knew people who were without support and Accenture's team, I mean, all the way up and down across the organization was fundamental and delivering this, but also literally putting themselves into, uh, these roles and to make sure that we would be able to respond quickly to do so. I think the playbook around the readiness for change I was shocked into existence every night. I won't talk about quantum physics, but clearly some some high level of energy was thrown in very quickly, mobilized everybody all at once. Nobody was said. He's sitting around saying, I wonder if we have change management covered off, you know this was changed readiness at its best. And so I think for me from a learning perspective, apart from just the technology side, which is pretty fundamental if you don't have ready enough technology to deploy quickly than the best paid plans in the world won't work. The reality is that to mobilize an organization going for it into that level of of spontaneous driving, change, exception, acceptance and adoption is really what I would aim for. And so our challenge now we'll be continuing that kind of progression going forward, and we now found the way. We certainly use the way to work with private sector in an innovative capacity in the new, innovative ways with brand new solutions that are truly agile and and and scalable to be able to pull all of the organization. All that one's very rapidly, and I have to admit that it is going to shift permanently our planning. We had 10 year plans for our big transformation, so some of our programs are the most important in the country. In many ways. We support people about eight million Canadians a month and on the benefits payments that we deliver, and they're the most marginal needed meeting and and requires our support from senior study, unemployed jobseekers and whatnot. So if you think about that group itself and to be able to support them clearly with the systems that we have is just unsustainable. But the new technologies are clearly going to show us the way that we had never for forecast. And I have to say I had to throw up, like in your plan. And now I'm working my way down from 10 denying date your plants going forward. And so it's exciting and nerve wracking sometimes, but then obviously has a change leader. Our goal is to get there as quickly as possible, so the benefit of all of these solutions could make a difference in people's lives. >>What's interesting is that you can shorten that timetable but also frees you up to be focused on what's contemporary and what's needed at the time. So leverage the people on the resource is You have and take advantage of that versus having something that you're sitting on that need to be refreshed. You can always be on that bleeding edge, and this brings up the Dev ops kind of mindset agility. The lean startup glean company. You know this is a team effort between Amazon and center and SDC. It's pass, shoot, score really fast. So this isn't the new, the new reality. Any commentary from you guys on this, you know, new pass shoot score combination. Because you got speed, you got agility. You're leaner, which makes you more flexible for being contemporary and solving problems. What's your thoughts? >>So my perspective on that is most definitely right. I think what we what we were able to show and what's. You know, what's coming out of a lot of different responses to the pandemic by government is, um, you know, perfection isn't the most important thing out of the gate. Getting something out there that's going to reassure citizens that's gonna allow them to answer their questions or access benefits quickly is what's becoming more important. Obviously, security and privacy. Those things are of the utmost importance as well. But it's ability to get stuff out there, quickly, test it, change it, tested again and and just always be iterating on the solutions. Like I can say what we put out on April 6th within four days is the backbone of what's out there still today. But we've added, you know, we added an integrated workforce management solution from Nice, and we added some other eyes views to do outbound dialing from acquisition, things like that. So the solution has grown from that M v p. And I think that's one other thing that that's going to be a big takeaways if you're not gonna do anything. So you got the final and product out there, then it's going to be here, right? So let's go quickly and let's adapt from there. >>Then we'll talk about that dynamic cause that's about building blocks, fund foundational things and then services. It's the cloud model. >>Yeah, I mean, before the pandemic, I had lunch with Mark Schwarz, which I believe you're quite familiar with, and, you know, I spent an hour and 1/2 with it. We were talking, and he was so exciting and and energized by what the technologies could do. And I was listening to him, and I used to be the chief technology officer for the government can right? And so I've seen a lot of stuff and I said, Well, that's really exciting, and I'm sure it's possible in some other places. And maybe it's some other countries where you know they didn't have infrastructure and legacy. I guess if I see him again soon, I'll have to. I apologize for not believing him enough, I think the building blocks of edge of the building, blocks of sprints and MVP's I mean they're not fundamental to the way we're gonna. So our biggest, various and scariest problems, technologically and then from a business perspective, Service candidate itself has 18,000 employees involved in multiple channels where the work has always been very lethargic, very difficult, arduous. You make change over years, not months, not days for sure. And so I think that that new method is not only a different way of working, it's a completely re HVAC way of assembly solutions, and I think the concept of engineering is probably going to be closer to what we're going to do on. And I have to borrow the Lego metaphor, but the building blocks are gonna be assembled. We now and working. I'm saying this in front of goal. He doesn't know that you should practice partners. We're gonna be assembling MPP maps of an entire long program, and it's gonna be iterative. It is gonna be designed, built. It will be agile as much as we can implement it. But more importantly, and punches weaken govern. It is, you know, the government is we may have changed. A lot of the government is not necessarily can count on to Most of these things approaches, But the reality is that that's where we're heading. And I will say, Oh, close. Perhaps on this on this answer. The biggest reason for doing that apart from we've proved it is the fact that the appetite inside the organization for that level of globalization, speed solution ing and being engaged rapidly you just can't take that away from an organization. Must be a piece of that. Uh, if you let them down, well, they'll remember. And frankly, they do remember now, cause they want more and it's gonna be hard. But it's a better heart. Ah, a better challenge that the one of having to do things over a decade, then to go fast and to kind of iterating quickly through the challenges and the issues and then move on very much to the next one as rapidly as possible. I think the other company, I would add is most of this was driven by a client need, and that's not inconsequential because it mobilized everybody to comment focused. If you have been just about, well, you know, we need to get people on side and solutions in place just to make our lives better, it providers. Yeah, it would have worked, perhaps, but it would have been different than the mobilisation It comes when the client is put in the middle, the client is the focus, and then we drive. Everyone's with that solution, >>you know, shared success and success is contagious. And when you ride the new way to oh, we need a new board, right? So once you get it, it then spreads like wildfire. This is what we've been seeing. And it also translates down to the citizens because again, being contemporary, none of us just looked could feel it's success in performance. So, as you know, people in business start to adopt cloud. It becomes a nice, nice, nice synergy. This is key. I'll take a year on a center. Um, the award winner. You guys did a great job. Final thoughts. >>Yeah. I mean, I think final thoughts would be happy to have the opportunity that help. And it was a It was a complete team effort and continues to be, um, it's not. It's not a bunch of Accenture technologists in the background in this, you know the commitment from everyone to get this in place. And can you continue to improvement from Benoit's team and from other folks across the government has been, uh, has been paramount to the success. So, um um, it's been a fantastic if world win like experience and, uh, look forward to continuing to build on it. And it has been said, I think one thing this is done is it's created demand for speed on some of these larger transformations. So I'm looking forward to continuing to innovate with with Ben wanting. >>Well, congratulations. The most innovative connect deployment. And because you guys from Canada, I have to use the hockey reference. You get multiple people working together in a cohesive manner. It's pass, shoot, score every time. And you know it's contagious. Thank you very much for your time. And congratulations for winning the >>West. Thanks. Thank you. Okay, this is the >>Cube's coverage of AWS Public Sector Partner Award show. I'm John Furrier, host of the Cube. Thanks for watching. Yeah, Yeah, yeah, yeah.

>>from the Cube Studios in Palo Alto and Boston connecting with thought leaders all around the world. This is a cube conversation. >>Welcome back to the Cube's coverage of AWS Public Sector Partner Awards program. I'm John Furrow, your host of the Cube here in Palo Alto, California In the remote interviews during this pandemic, we have our remote crews and getting all the stories and celebrating the award winners. And here to feature the most innovative connect deployment. We have a center of Canada and the Department of Employment and Social Development of Canada, known as E S D. C guys. Congratulations, Joel. More Children Censure Canada Managing director and Ben while long sdc of Canada Chief Transformation officer. Gentlemen, thanks for coming on. And congratulations on the award. >>Thank you. >>Thank you. >>So, Ashley, during this pandemic, a lot of disruption and a lot of business still needs to go on, including government services. But the citizens and people need to still do their thing. Business got to run, and you got to get things going. But the disruptions caused a little bit of how the user experiences are. So this connect has been interesting. It's been a featured part of what we've been hearing at the public sector summit with Theresa Carlson. You guys, this is a key product. Tell us about the award. What is the solution? That disturbing of deserving reward? >>Maybe I'll get I'll go first and then pass it over to Benoit. But I think the solution is Amazon. Connect a spiritual contact center that we stood up fairly quickly over the course of about four days and really in support of of benefit that the government of Canada was was releasing as part of its economic response to the pandemic. And in the end that, you know, it's a fully functioning featured contact center solution includes an ai VR and, uh, you know, we stood it up for 1500 to 2000 agents so that that's the crux of the solution. And maybe Benoit can give a bit of insight as to to how it came about so quickly. >>Yeah, I'd be happy to actually wear obviously, like every other government, facing enormous pressures at that time to deliver benefits directly to people who were in true need, the jobs are being lost. Our current systems were in trouble because of their age in the arcade cake Nature. And so the challenge is was quickly how to actually support a lot of people really fast. And so it came through immediately that after our initial payments were made under what was called Canada Emergency Response Benefit, then we have to support our clients directly. And so people turn to the transformation team of all teams. If you wish during a fire firestorm to say, Well, what could you do and how could you help? And so we had an established relationship with a number of other system integrators, including Accenture, and we were able to run a competition very rapidly. Accenture one. And then we deployed in, as you all said, in a matter of four days, what for us was a new, exceptional on high quality solution to a significant client problem. And I say that because I think you can imagine how people feel in that endemic of all of all things. But with the uncertainty that comes with the loss of income, loss of jobs, the question of being able to deal with somebody really a human being, as well as to be able to be efficiently answer a very simple but straightforward questions rapidly and with high quality, with pretty fundamental for us. So the people in the groups that were talking through here are talking, speaking to millions of people who were literally being asked to to accept the pavement rapidly and to be able to connect with us quickly. And without this solution, which was exceptionally well done and deployed and of high quality personally, just a technology, uh, solution. I would not have been possible to even answer any of these queries quickly. >>And while that's a great 0.1 of the things that you see with the pandemic it's a disaster in the quote disaster kind of readiness thing. Unforeseen, right? So, like other things, you can kind of plan for things that hypothetical. You've got scenarios, but this >>is >>truly a case where every day counts. Every minute counts because humans are involved is no our ROI calculation. It's not like it's not like, Well, what's the payback of our system? The old kind of way to think this is really results fast. This is what cloud is all about. This is the promise of cloud. Can I stand up something quick and you did it with a partner. Okay, this is, like, not, like, normal again. It's like it's, you know, it's like, unheard of right? Four days with critical infrastructure, critical services that were unforeseen. Take us through what was going on in the war room, as you guys knew this was here. Take us through the through what happened. Yeah, >>So I think I can start a Z. You can imagine the set of executives that we're seeing a payment process. Uh, was an exceptional. It was like a bunker. Frankly, for about two weeks, we had to suspend the normal operations off the vast majority of our programming. We had to launch brand new payments and benefits systems and programs that nobody had seen before. The level of simplicity was maximized to delivered the funds quickly. So you could imagine it's a warpath if you wish, because the campaign is really around. A timing. Timing is fundamental. People are are literally losing their jobs. There is no support. There's no funding money for them to be able to buy groceries. So on the trust that people have in the government, Ai's pretty much at risk right there and then, in a very straightforward but extraordinarily powerful magic moment. If you wish. If you can deliver a solution, then you make a difference for a long time. And so the speed unheard off on old friends when he came to the call center capability and the ability for us to support and service context the clients that were desperate to reach us on. We're talking hundreds of thousands of calls, right? We're not talking a few 1000 year. Ultimately, at some point we were literally getting in our over over, taken by volumes, call centers, but we had a regular one still operating over a 1,000,000 calls for coming in today. Uh, with the capacity to answer, um, you know, tens of thousands. And so the reality is that the counselor that we put up here very quickly became capable of answering more calls than our regular costumes. And that speaks to the speed of delivery, the quality of the solution, of course, but the scalability of it and I have to say, maybe unheard of, it may be difficult to replicate. The conditions to lead to this are rare, but I have to say that my bosses and most of the government is probably now wondering why we can't do this more often like we can't operate with that kind of speed and agility. So I think what you've got is a client in our case, under extreme circumstances. Now, realizing the new normal will never be the same, that these types of solutions and technology. And then there's scalability. There's agility there, the speed of deployment. It's frankly, something we want. We want all the time. Now we'd like to be able to do it under your whole timeline conditions. But even those will be a fraction of what it used to take. It would have taken us well, actually, I can actually tell you because I was the lead. Ah, technologist, to deploy at scale for the government, Canada, all the call center capabilities under a single software as a service platform. It took us two years to design it. Two years to procure it and five years to install it. That's the last experience. We have a call center enterprise scale capabilities, and in this case, we went from years to literally days. >>Well, you know, it takes a crisis sometimes to kind of wire up the simplicity solution that you say. Why didn't we do this before? You know the waterfall meetings, Getting everyone arguing gets kind of gets in the way of the old, the old software model. I want to come back to the transformation been wanna minute, cause I think that's going to be a great success story and some learnings, and I want to get your thoughts on that. But I want to go to Joel because Joel we've talked to many Accenture executives over the years and most recently this past 24 months, And the message we've been hearing is we're going to be faster. We're not going to be seen as that. You know, a consulting firm taking our times. Try and get a pound of flesh from the client. This is an example, in my opinion of a partner working with the problem statement that kind of matches the cloud speed. So you guys have been doing this. This is not new to a censure. So take us through how you guys reacted because one you got to sync up and get the cadence of what? Ben? What I was trying to do, sync up and execute. Take us through what happened on your side. >>Yeah. I mean, so it's It's Ah, It's an unprecedented way of operating for us as well, frankly, and, um and, uh and, you know, we've had to look at to get this specific solution at the door and respond to an RFP and the commercial requirements that go with that way. Had Teoh get pretty agile ourselves internally on on how we go through approvals, etcetera, to make sure that that we were there to support Ben Wan is team. And I think you know that we saw this is a broader opportunity to really respond to it, to help Canada in a time of need. So So I think we, you know, we had to streamline a lot of our internal processes and make quick decisions that normally, even for our organization, would have taken, um, could it could have taken weeks, right? And we were down to hours in a lot of instances. So it helps. It forces us to react and act differently as well. But I mean, to Benoit's point, I think this is really going to to hopefully change the way it illustrates the art of the possible and hopefully will change how, How quickly we can look at problems and and we reduce deployment timeframes from from years to months and months to weeks, etcetera for solutions like this. Um, and I think that the AWS platform specifically in this case but what touched on a lot of things to beat the market scale ability But just as the benefit itself was, you know has to be simplified to do this quickly. I think one of the one of the benefits of the solution itself is it's simple to use technologically. I mean, we know least retrained. As I said, I think 1600 agents on how to use the platform over the course of a weekend on and and were able, and they're not normal agents. These were people who are firm from other jobs, potentially within the government. So they're not necessarily contact center agents by training. But they became contact center agents over the course of 48 hours that I think from that perspective, you know, that was important as well have something that people could could use. The answer those calls that you know that when you're gonna come So, >>Ben, what this is This is the transformation dream scenario in the sense of capabilities. I know it's under circumstances of the pandemic, and you guys didn't solve a big, big problem really fast and saved lives and help people get on with their day. But transformations about having people closest to the problem execute and the the also the people equation. People process technology, as they say, is kind of playing out in real time. This >>is >>the this is kind of the playbook, you know, Amazon came in said, Hey, you want to stand something up? You wired it together. The solution quickly. You're close to it. Looking back now, it's almost like, Hey, why aren't we doing this before? As you said and then you had to bring people in who weren't trained and stood them up and they were delivering the service. This >>is >>the playbook to share your thoughts on this, because this is what you're you're thinking about all the time and it actually playing out in real time. >>Well, I would definitely endorsed the idea that it's a playbook. It's I would say it's an ideal and dream playbook to build like showing up on the basketball court with all the best players in the entire league playing together magically, it is exactly that. So a lot of things have to happen quickly, but also correctly because you know you can't pull these things properly together without that. So I would say the partnership with the private sector here was fundamental. And I have to applaud the work that Accenture did particularly, I think, as Canadians, we're very proud of the fact that we needed to respond quickly. Everyone was in this, our neighbors, we knew people who were without support and Accenture's team, I mean all the way up and down across the organization was fundamental in and delivering this, but also literally putting themselves into, uh, these roles and to make sure that we would be able to respond quickly, do so. I think the playbook around the readiness for change. I was shocked into existence every night. I won't talk about quantum physics, but clearly some some high level of energy was thrown in very quickly, mobilized everybody all at once. Nobody was said. He's sitting around saying, I wonder if we have change management covered off, you know this was changed readiness at its best. And so I think for me from a learning perspective, apart from just the technology side, which is pretty fundamental if you don't have ready enough technology to deploy quickly than the best plans in the world won't work. The reality is that to mobilize an organization going forward into that level of of spontaneous driving, change, exception, acceptance and adoption is really what I would ain't for. And so our challenge Now we'll be continuing that kind of progression going forward, and we now found a way. And we certainly use the way to work with private sector in an innovative capacity and in innovative ways with brand new solutions that are truly agile and and scalable to be able to pull all of the organization. All that one's very rapidly, and I have to admit that it is going to shift permanently our planning. We had 10 year plans for our big transformation, so some of our programs are the most important in the country. In many ways. We support people about eight million Canadians a month and on the benefits payments that we deliver, and they're the most marginal needed meeting and and requires our support from senior studio, unemployed jobseekers and whatnot. So if you think about that group itself and to be able to support them clearly with their systems that we have is just unsustainable. But the new technologies are clearly going to show us the way that we had never for forecast. And I have to say I had to throw up, like in your plan. And now I'm working my way down from 10 denying date your plants going forward. And so it's exciting and nerve wracking sometimes. But then, obviously, as a change leader, our goal is to get there as quickly as possible, so the benefit of all of these solutions could make a difference in people's lives. >>What's interesting is that you can shorten that timetable but also frees you up to be focused on what's contemporary and what's needed at the time. So leverage the people on the resource is You have and take advantage of that versus having something that you're sitting on that need to be refreshed. You can always be on that bleeding edge, and this brings up the Dev ops kind of mindset agility. The lean startup glean company. You know this is a team effort between Amazon and center and SDC. It's pass, shoot, score really fast. So this isn't the new, the new reality. Any commentary from you guys on this, you know, new pass shoot score combination. Because you got speed, you got agility. You're leaner, which makes you more flexible for being contemporary and solving problems. What's your thoughts? >>Yeah, So my perspective on that is most definitely right. I think what we what we were able to show and what's. You know, what's coming out of a lot of different responses to the pandemic by government is, um, you know, perfection isn't the most important thing out of the gate. Getting something out there that's going to reassure citizens that's going to allow them to answer their questions or access benefits quickly is what's becoming more important. Obviously, security and privacy. Those things are of the utmost importance as well. But it's ability to get stuff in there, quickly, test it, change it tested again and just always be iterating on the solutions. Like I can say what we put out on April 6th within four days is the backbone of what's out there still today. But we've added, you know, we added an integrated workforce management solution from Nice, and we added some other eyes views to do outbound dialing from acquisition, things like that. So the solution has grown from that M v p. And I think that's one other thing that that's going to be a big takeaways if you're not gonna do anything. So you got the final and product out there, then it's going to be here, right? So let's go quickly and let's adapt from there. >>Then we'll talk about that dynamic cause that's about building blocks, fund foundational things and then services. It's the cloud model. >>Yeah, I mean, before the pandemic, I had lunch with Mark Schwarz, which I believe you're quite familiar with, and, you know, I spent an hour and 1/2 with it. We were talking, and he was so exciting and and energized by what the technologies could do. And I was listening to him, and I used to be the chief technology officer for the government. Can't right. And so I've seen a lot of stuff and I said, Well, that's really exciting, and I'm sure it's possible in some other places. And maybe it's some other countries where you know they didn't have infrastructure and legacy. I guess if I see him again soon, I'll have to. I apologize for not believing him enough, I think the building blocks of agile, the building blocks of sprints and MVP's I mean, they're not fundamental to the way we're going to solve our biggest various and scariest problems technologically and then from a business perspective. Service candidate itself has 18,000 employees involved in multiple channels, where the work has always been very lethargic, very difficult, arduous. You make change over years, not months, not days for sure. And so I think that that new method is not only a different way of working, it's a completely revamped way of assembly solutions, and I think the concept of engineering is probably going to be closer to what we're going to do. Um, and I have to borrow the Lego metaphor, but the building blocks are gonna be assembled. We now and working. I'm saying this in front of goal. He doesn't know that you should practice partners. We're gonna be assembling MPP maps of an entire long program, and it's gonna be iterative. It is gonna be designed, built. It will be agile as much as we can implement it. But more importantly, and punches weaken govern. It is, you know, the government is we may have changed. A lot of the government is not necessarily can count on to Most of these things approaches. But the reality is that that's where we're headed. And I will say, Oh, close. Perhaps on this on this answer. The biggest reason for doing that apart from we've proved it is the fact that the appetite inside the organization for that level of globalization, speed solution ing and being engaged rapidly you just can't take that away from an organization. Must be a piece of that. Uh, if you let them down, well, they don't remember. And frankly, they do remember now, cause they want more and it's gonna be hard. But it's a better heart. Ah, a better challenge that the one of having to do things over a decade, then to go fast and to kind of iterating quickly through the challenges and the issues and then move on very much to the next one as rapidly as possible. I think The other company, I would add, is most of this was driven by a client need, and that's not inconsequential because it mobilized everybody to comment focused. It could have been just about well, you know, we need to get people on side and solutions in place just to make our lives better. It is his providers. Yeah, it would have worked, perhaps, but it would have been different than the mobilisation It comes when the client is put in the middle. The client is the focus. And then we drive. Everyone's with that, >>you know, shared success and and successes contagious. And when you ride the new way to oh, we need a new board, right? So once you get it, it then spreads like wildfire. This is what we've been seeing. And it also translates down to the citizens because again, being contemporary numbers just look and feel. It's success in performance. So, as you know, people in business start to adopt cloud. It becomes a nice, nice, nice synergy. This is key. I'll take a year on a center. Um, the award winner. You guys did a great job. Final thoughts. >>Yeah. I mean, I think final thoughts would be happy to have the opportunity that help. And it was a It was a complete team effort and continues to be, um, it's not. It's not a bunch of Accenture technologists in the background in this, you know the commitment from everyone to get this in place. And can you continue to improvement from Benoit's team and from other folks across the government has been has been paramount to the success. So, um um, it's been a fantastic world win like experience and, uh, look forward to continuing to build on it. And it has been said, I think one thing this is done is it's created demand for speed on some of these larger transformations. So I'm looking forward to continuing to innovate with with Ben wanting. >>Well, congratulations. The most innovative connect deployment. And because you guys from Canada, I have to use the hockey reference. You get multiple people working together in a cohesive manner. It's pass, shoot, score every time. And you know it's contagious. Thank you very much for your time. And congratulations for winning the West. Thanks. Okay, this is the Cube's coverage of AWS Public Sector Partner Award show. I'm John Furrier, host of the Cube. Thanks for watching. Yeah, Yeah, >>yeah, yeah, yeah

(upbeat music) >> From our studios, in the heart of Silicon Valley, Palo Alto, California, this is a Cube Conversation. >> Hi, I'm Peter Burris, and welcome again, to another Cube Conversation from our studios here in beautiful Palo Alto, California. With every Cube Conversation, we want to bring smart people together, and talk about something that's relevant and pertinent to the industry. Now, today we are going to be talking about the emergence of new classes of cloud provider, who may not be the absolute biggest, but nonetheless crucial in the overall ecosystem of how they're going to define new classes of cloud services to an expanding array of enterprise customers who need that. And to have that conversation, and some of the solutions that class of cloud service provider going to require, we've got Joel Dedrick with us today. Joel is the Vice President and General Manager of Networks Storage Software, Toshiba Memory America. Joel, welcome to theCube. >> Thanks, very much. >> So let's start by, who are you? >> My name's Joel Dedrick, I'm managing a new group at Toshiba Memory America, involved with building software that will help our customers create a cloud infrastructure that's much more like those of the Googles and Amazons of the world. But, but without the enormous teams that are required if you're building it all yourself. >> Now, Toshiba is normally associated with a lot of hardware. The software angle is, how does software play into this? >> Well, Flash is changing rapidly, more rapidly than maybe the average guy on the street realizes, and one way to think about this is inside of a SSD there's a processor that is not too far short of the average Xeon in compute power, and it's busy. So there's a lot more work going on in there than you might think. We're really bringing that up a level and doing that same sort of management across groups of SSDs to provide a network storage service that's simple to use and simple to understand, but under the hood, we're pedaling pretty fast. Just as we are today in the SSDs. >> So the problem that I articulated up front was the idea that we're going to see, as we greater specialization and enterprise needs from cloud there's going to be greater numbers of different classes of cloud service provider. Whether that be Saas or whether that be by location, by different security requirements, whatever else it might be. What is the specific issue that this emerging class of cloud service provider faces as they try to deliv really high quality services to these new, more specialized end users. >> Well let me first, kind of define terms. I mean, cloud service provider can mean many things. In addition to someone who sells infrastructure, as a service or platform as a service, we can also think about companies that deliver a service to consumers through their phone, and have a data center backing that, because of the special requirements of those applications. So we're serving that panoply of customers. They face a couple of issues that are a result of trajectory of Flash and storage of late. And one of those is that, we as Flash manufactures have a innovators dilemma, that's a term we use here in the valley, that I think most people will know. Our products are too good, they're too big, they're too fast, they're too expensive, to be a good match to a single compute node. And so you want to share them. And so the game here is can we find a way to share this really performant, you know this million IOP Dragon across multiple computers without losing that performance. So that's sort of step one, is how do we share this precious resource. Behind that is even a bigger one, that takes a little longer to explain. And that is, how do we optimize the use of all the resources in the data center in the same way that the Googles and Amazons do by moving work around between machines in a very fluid and very rapid way. To do that, you have to have the storage visible from everywhere and you have be able to run any instance anywhere. That's a tall order, and we don't solve the whole problem, but we're a necessary step. And the step we provide is we'll take the storage out of the individual compute nods and serve it back to you over your network, but we won't lose the performance that you're used to having it locally attached. >> Okay, so let's talk about the technical elements required to do this. Describe from the SSD, from the Flash node, up. I presume it's NVME? >> Um hm, so, NVME, I'm not sure if all of our listeners today really know how big a deal that is. There have been two block storage command sets. Sets of fundamental commands that you give to a block storage device, in my professional lifetime. SCSI was invented in 1986, back when high performance storage was two hard drives attached to your ribbon cable in your PC. And it's lasted up until now, and it's still, if you go to a random data center, and take a random storage wire, it's going to be transporting the SCSI command set. NVME, what, came out in 2012? So 25 years later, the first genuinely new command set. There's an alphabet soup of transports. The interfaces and formats that you can use to transport SCSI around would fill pages, and we would sort of tune them out, and we should. We're now embarking on that same journey again, except with a command set that's ideal for Flash. And we've sort of given up on or left behind the need to be backward compatible with hard discs. And we said, let's build a command set and interface that's optimum for this new medium, and then let's transport that around. NVME over Fabrics is the first transport for the NVME command set, and so what we're doing is building software that allows you to take a conventional X86 compute node with a lot of NVME drives and wrap our software around it and present it out to your compute infrastructure, and make it look like locally attached SSDs, at the same performance as locally attached SSDs, which is the big trick, but now you get to share them optimality. We do a lot of optimal things inside the box, but they ultimately don't matter to customers. What customers see is, I get to have the exact size and performance of Flash that I need at every node, for the exactly the time I need it. >> So I'm a CTO at one of these emerging cloud companies, I know that I'm not going to be adding million machines a year, maybe I'm only going to be adding 10,000 maybe I'm only adding 50,000, 100,000. So I can't afford the engineering staff required to build my own soup to nuts set of software. >> You can't roll it all yourself. >> Okay, so, how does this fit into that? >> This is the assembly kit for the lowest layer of that. We take the problem of turning raw SSDs into a block storage service and solve it for you. We have a very sharp line there. We aren't trying to be a filer or we're not trying to be EMC here. It's a very simple, but fast and rugged storage service box. It interfaces to your provisioning system, to your orchestration system, to your telemetry systems and no two of those are a like. So there's a fair amount of customization still involved, but we stand ready to do that. You can Tinker Toy this together yourself. >> Toshiba. >> Yeah, Toshiba does, yes. So, that's the problem we're solving. Is we're enabling the optimum use of Flash, and maybe subtly, but more importantly in the end we're allowing you to dis-aggregate it, so that you no longer have storage pinned to a compute node, and that enables a lot of other things, that we've talked about in the past. >> Well, that's a big feature of the cloud operating model, is the idea that any application can address any resource and any resource can address any application. And you don't end up with dramatic or significant barriers in the infrastructure, is how you provision those instances and operate those instances. >> Absolutely, the example that we see all the time, and the service providers that are providing some service through your phone, is they all have a time of day rush, or a Christmas rush, some sort of peaks to their work loads, and how do they handle the peaks, how do they handle the demand peaks? Well today, they buy enough compute hardware to handle the peak, and the rest of the year it sits idle. And this can be 300% pretty easily, and you can imagine the traffic to a shopping site Black Friday versus the rest of the year. If the customer gets frustrated and goes away, they don't come back. So you have data centers worth of machines doing nothing. And then over on the other side of the house you have the machine learning crew, who could use infinite compute resource, but the don't have a time demand, it just runs 24/7. And they can't get enough machines, and they're arguing for more budget, and yet we have 100s of 1,000s of machines doing nothing. I mean that's a pretty big piece of bait right there. >> Which is to say that, the ML guys can't use the retail guys or retail resources and the retail resources can't use the ML, and what we're trying to do is make it easier for both sides to be able to utilize the resources that are available on both sides. >> Exactly so, exactly so, and that requires more than, one of the things that requires is any given instances storage can't be pinned to some compute node. Otherwise you can't move that instance. It has to be visible from anywhere. There's some other things that need need to work in order to, move instances around your data center under load, but this is a key one, and it's a tough one. And it's one that to solve it, without ruining performance is the hard part. We've had, network storage isn't a new thing, that's been goin' on for a long time. Network storage at the performance of a locally mounted NVME drive is a tough trick. And that's the new thing here. >> But it's also a tool kit, so that, that, what appears to be a locally mounted NVME drive, even though it may be remote, can also be oriented into other classes of services. >> Yes >> So how does this, for example, I'm thinking of Kubernetes Clusters, stainless, still having storage` that's really fast, still really high performin', very reliable, very secure. How do you foresee this technology supporting and even catalyzing changes to that Kubernetes, that darker class retainer workloads. >> Sure, so for one, we implement the interface to Kubernetes. And Kubernetes is a rapidly moving target. I love their approach. They have a very fast version clock. Every month or two there's a new version. And their support attitude is if you're not within the last version or two, don't call. You know, keep up, this is. And that's sort of not the way the storage world has worked. So our commitment is to connect to that, and make that connection stay put, as you follow a moving target. But then, where this is really going is the need for really rapid provisioning. In other words, it's not the model of the IT guy sitting at a keyboard attaching a disc to a stack of machines that's running some application, and coming back in six months to see if it's still okay. As we move from containerized services to serverless kind of ideas. In the serverless world, the average lifespan of an application's 20 seconds. So we better spool it up, load the code, get it state, run, and kill it pretty quickly, millions of times a minute. And so, you need to be light of foot to do that. So we're poured in a lot of energy behind the scenes, into making software that can handle that sort of a dynamic environment. >> So how does this, the resource that allows you to present a distant NVME drive, as mounting it locally, how does that catalyze other classes of workloads? Or how does that catalyze new classes of workloads? You mentioned ML, are there other workloads that you see on the horizon that will turn into services from this new class of cloud provider? >> Well I think one big one is the serverless notion. And to digress on that a little bit. You know we went from the classic enterprise the assignment of work to machines lasts for the life of the machine. That group of machines belong to engineering, those are accounting machines, and so on. And no IT guy in his right mind. would think of running engineering code on the accounting machine or whatever. In the cloud we don't have a permanent assignment there, anymore. You rent a machine for a while, and then you give it back. But the user's still responsible for figuring out how many machines or VMs he needs. How much storage he needs, and doing the calculation, and provisioning all of that. In the serverless world, the user gives up all of that. And says, here's the set of calculations I want to do, trigger it when this happens, and you Mr. Cloud Provider figure out does this need to be sharded out 500 ways or 200 ways to meet my performance requirements. And as soon as these are done, turn 'em back off again, on a timescale of 10ths of seconds. And so, what we're enabling is the further movement in the direction of taking the responsibility for provisioning and scaling out of the user's hands and making it automatic. So we let users focus on what they want to do, not how to get it done. >> This really is not an efficiency play, when you come right down to it. This is really changing the operating model, so new classes of work can be performed, so that the overall computer infrastructure, the overall infrastructure becomes more effective and matches to the business needs better. >> It's really both. There's a tremendous efficiency gain, as we talked about with the ML versus the marketplace. But there's also, things you just can't do without an infrastructure that works this way, and so, there's an aspect of efficiency and an aspect of, man this just something we have to do to get to the next level of the cloud. >> Excellent, so do you anticipate this is portents some changes to the Toshiba's relationship with different classes of suppliers? >> I really don't. Toshiba Memory Corporation is a major supplier of both Flash and SSDs, to basically every class of storage customer, and that's not going to change. They are our best friends, and we're not out to compete with them. We're serving really an unmet need right now. We're serving a relatively small group of customers who are cloud first, cloud always. They want to operate in the sort of cloud style. But they really can't, as you said earlier, they can't invent it all soup to nuts with their own engineering, they need some pieces to come from outside. And we're just trying to fill that gap. That's the goal here. >> Got it, Joel Dedrick, Vice President and General Manager Networks Storage Software, Toshiba Memory America. Thanks very much for being on theCube. >> My pleasure, thanks. >> Once again this is Peter Burris, it's been another Cube Conversation, until next time.

(soaring orchestral music) >> Everyone, welcome to this CUBE Conversation here at Palo Alto, California. I'm John Furrier, host of theCUBE. We are here with Joel Horwitz, who's the CMO of WANdisco, Joel, great to see you, formerly of IBM, we've known you for many years, we've had great conversations when you were at IBM, rising star, now at WANdisco, congratulations. >> Thank you, yeah, it's really great to be at WANdisco, and great to be here with theCUBE. So we've had many conversations, again, goin' back, you were a rising star in data, you know the cloud real well, why WANdisco, why leave IBM for WANdisco, what attracted you to the opportunity? >> Yeah, really three things. First and foremost, the people. I've known the WANdisco team now for years. Back in my Hadoop days, when I was at Datamere, I used to, hang out with the WANdisco team at Data After Dark, in New York, which was great, and they had the best marketing there at the time. Two, the product, I mean I won't join a company unless the product is really legit, and they have an absolutely great technology, and they are applying it to some really tough problems. And third is just the potential, really, the potential of this company is not even close to being tapped. So there's a ton of runway there, and so, for me, I'm just totally grateful, and totally honored, to be a part of WANdisco. >> What's the tailwind for them, that wave that they're on, if you will, because you mentioned, there's a lot of runway or headroom, a lot of market growth. Certainly cloud, David Richards will talk about that. But what attracted you, 'cause you knew the cloud game too. >> Yeah, yeah. >> IBM made a big run at the cloud. >> Yeah well, I came in, at IBM, through the data door, so to speak, and then I walked through the cloud door, as well, while I was there. And the reality is that data continues to be the lifeblood of an enterprise, no matter what. And so, what I saw in WANdisco was that they had technology that allowed people to enlarge enterprise to, frankly replicate or manage their data across Hadoop clusters from cluster to cluster. And then we ended up, when I spoke with you last, with David here, we also recognize the opportunity that just how copying data, large-scale data from one Hadoop cluster to another, is challenging, copying data, it's really not that different of copying data from, say, HDFS to an object storage or S3, as pretty similar problem. And so that's why, just this past week, we announced live data for multicloud. >> Explain live data for multicloud, I've read it in the news, got some buzz, it's this great trend, live. We're doing you a lot of live videos on theCUBE, live implies real time. Data's data. Multicloud is clearly becoming one of those enterprise categories. >> Yeah. >> First it was public cloud, then hybrid cloud. >> Yeah. >> Now it's multicloud. How does live data fit into multicloud? >> Yeah, so multicloud, and live data, as I just mentioned, we have live data for Hadoop, so that's fairly obvious, so if you're going multi-cluster you can do that. As well as from, even on-prem, data center to data center, so, multi-site if you will. But multicloud is a really interesting phrase that's kind of cropped up this year. We're seeing it used quite a lot. The focus in multicloud has been mainly focus on applications. And so, talking about, how do you have a container strategy? Or a virtualization strategy, for your applications? And so, I think of it really as a multicloud strategy, as opposed to a multicloud architecture. So we're helping our enterprise clients think about their multicloud strategy. So they're not locked in to any one vendor, so they're able to take advantage of all the great innovations that are happening, if you ask me, on the cloud first, and then ultimately comes down to, at times, on-prem. >> What's the pitfalls between multicloud strategy and multicloud architecture, you just said, customers don't want to get locked in, obviously, no-one wants to get locked in, multi-vendor used to be a big buzzword, during that last wave of computer-to-client server. >> Yeah. >> Now multicloud seems like multi-vendor, what do you mean by architecture versus strategy, how do you parse that? Yeah, so like I said, in terms of your data, right, and it all comes back to your data. If you go all in on, say, one vendor, and you're architecting for that vendor only and you're choosing your migration, your data management tools, for a particular cloud vendor, and, said a different way, if you're only using the native tools from that vendor, then it's very difficult to ever move off of that cloud, or to take advantage of other clouds as they, for example, maybe have new IOT offerings, or have new blockchain offerings, or have new AI offerings, as many others come on the scene. And so, that's what I mean by strategy, is if you choose one vendor for, your certain toolset, then it's going to be very difficult to maintain arbitrage between the different vendors. >> Talk about how you guys are attacking the market, obviously, it's clear that data, has been a fundamental part of WANdisco's value proposition. Moving data around has been a top concern, even back in the Hadoop days, now it's in the cloud. >> Yeah. >> Moving data across the network, whether it's cloud to cloud, or cloud to data center, or to the edge of the network-- >> Yep. Yep. >> Is a challenge. >> You know at IBM, when I was there in 2016, and we're coming up with our strategy when I was in Corp Dev. We talked about four different areas of data, we talked about data gravity, so data has gravity. We talked about data movement, and we talked about data science. And we talked about data governance. And I still think those are still relatively the four major themes around this topic of data. And so, absolutely data has gravity, and not just in terms of the absolute size and weight, if you will. But it also has applications that depend on it, the business itself depends on it, and so, the types of strategies that we've seen to migrate data, say, to the cloud, or have a hybrid data management strategy, has been lift and shift, or to load it on to the back of, I always picture that image of the forklift lifting all those tape drives onto the airplane, you know, the IBM version of that. And that's like a century old at this point, so, we have a way to replicate data continuously, using our patented consensus technology, that's in the lifeblood of our company, which is distributed computing. And so having a way, to migrate data to the cloud, without disrupting your business, is not just marketing speak, but it's really what we are able to do for our clients. How do you guys go to market, how do you guys serve customers, what's the strategy? >> So, primarily we've formed a number of strategic partnerships, obviously one with IBM that I helped spearhead while I was there, we actually just recently announced that we now support Big SQL, so it's actually the first opportunity where, if you are using a database, provided by IBM, you can actually replicate across different databases and still query it with Big SQL. Which is a big deal, right, it means you can still have access to your data while it's in motion, right, that's pretty cool. And then so IBM is there, and then secondly, we've formed a number of other strategic partnerships with the other cloud vendors, of course, Alibaba we have an OEM, Microsoft, we have preferred selling motion with them, AWS, of course, we're in their marketplace. So primarily, we sell through a number of our key partnerships, because, we are, fairly integrated, like I said, into the architecture of these platforms, and, just to comment more deeply on that, when you look at, object storage, on each of these various public cloud vendors. They may look similar on the surface, maybe they all use the same APIs or have some level of, similar interaction, they look like they're the same, the pricing might be the same. We go like one level deeper, and they're all very different, they're all very different flavors of object storage. And so while it might seem like, "Oh, that's trivial to work with," it really isn't, it's extremely non-trivial, so, we help, not only our customers solve that, but we also help our partners significantly, help their clients move to the cloud, to their cloud, faster. >> So you basically work through people who sell your product, to the end user customer, or through their application or service. >> Yeah, that's our main route to market, I would say, the other, obviously, the main, we have a direct sales force, who's out there, working with the best clients in the world. AMD is a great customer of ours, who we recently helped migrate to Microsoft Azure. And we have a number of other large enterprise customers, in retail, and finance, and media. And so really, when it comes down to it, yeah it's those two majors motions, one through the cloud vendors themselves, 'cause frankly, in most cases, they don't have this technology to do it, you know, they're trying to basically take snapshots of data, and they're struggling to convince their customers to move to their cloud. >> It becomes a key feature in platforms. >> Yes it does. >> So that's obviously what attracts sellers, what other things would attract sellers or partners, for you, what motivates them, obviously the IP, clearly, is the number one, economics, what's the other value proposition? >> The end goal isn't to move data to the cloud, the end goal is to move business processes to the cloud, and then be able to take advantage of the other value adds that already exist in the cloud. And so if you're saying, what's the benefit there, well, once you do that move, then you can sell into, clients with all your additional value adds. So that's really powerful, if you are stuck with this stage of "Eh, how do we actually migrate data to the cloud?" >> So IBM Think is coming up, what's your view of what's happening there, what are you guys going to be doing there, as are you, on the IBM side-- >> Yeah. >> Now you're on the other side of the table. You've been on both sides of the table. >> Yeah. >> So what's goin' on at Think, and how does WANdisco, vector, and certainly CUBE will be there. >> Yeah, we'll be there, so WANdisco is a sponsor of IBM Think as well, clearly, as I mentioned, we'll be talking about Big Replicate, which is our Hadoop replication offering, that's sold with IBM. The other one, as I mentioned, is Big SQL, so that's a new offering that we just announced this past month. So we'll be talking about that, and showing a number of great examples of how that actually works, so if you're going to be at Think, come by our booth, and check that out. In addition to that, I mean, clearly, IBM is also talking about multicloud and hybrid cloud, so hybrid data management, hybrid cloud is a big topic. You can expect to see, at IBM Think, a lot of conversations on the application side. In terms of, obviously with their acquisition of Red Hat, you can well imagine they're going to be talking a lot about the software stack, there. But I would say that, we'll be talking, and spending most of our time talking about, how to manage your data across different environments. >> Where's the product roadmap heading, I know you guys don't like to go into specifics in public- >> Yeah. >> Sensitive information, but, generally speaking, where's the main trendlines that you guys are going to be building on, obviously, cloud data, they'll come in together, good core competency there for WANdisco, what's next, what's the next level for you guys? >> So what's really fascinating, and I actually didn't realize this when I joined WANdisco, just to be completely transparent. WANdisco has a core piece of technology called DConE, Distributed Coordination Engine. It essentially is a form of blockchain, really, it's a consensus technology, it's an algorithm. And that's been their secret sauce since the founding of the company. And so they originally applied that to code, through source code management, and then only in this last few years they've applied it to data. So you can guess, at other areas that we might apply it to, and already this past year, we actually filed two patents, in the area of blockchain, or really, distributed ledger technology, as we're starting to hear it called in the actual enterprise that's using it. But you can expand that to any other enterprise asset, really. That's big, right, that has value, and that you want to manage across different environments, so you can imagine, lots of other assets that we could apply this to, not only code, not only data, not only ledgers, but what are the other assets? And so that's essentially what we're working on. >> Is that protectable IP the patents, so those are filed on the blockchain? >> Yeah, yeah. >> For instance? >> So DConE is certainly patented, I'm sure Jagane'll talk more about this. >> Yeah, we'll get into it. >> There's probably a handful of people in the world, and they might all be working at WANdisco at this point. (chuckles) Who actually know how that works, and it's essentially Paxos, which is a really gnarly problem to solve, a really difficult math problem. And as David mentioned earlier, Google, the other smartest company in the world, published their paper on Spanner, and as you said, they used brute force, really, to solve the problem. Where we have a very elegant solution, using software, right? So it's a really great time to be at WANdisco, because I just see that there's so many applications of our technology, but, right now, we're mainly focused on what our customers are asking for. >> You've said a great quote, thanks Joe, final question for you, where do you see it going, WANdisco, what are your plans, do you have anything in mind, do you want to share anything notable, around what you're doing, and what you think WANdisco will be in a few years. >> We have an incredible team, as I mentioned, the people that are joining WANdisco, as David mentioned, I myself, not to say too much there, but, the new folks that have joined our Research and Development Team, but we've been making some great hires, to WANdisco. So I'm really excited about the team, I'm going, actually, to visit, we have a great team in Europe, in the UK, in the United Kingdom, so I'm going to go see them next week. But we have just the company culture is what drives me, I think that's just one of those hard things, really, to find. And so that's what I'm really excited about, so there's a lot of cool stuff happening there. You know, on that note, it's actually kind of funny, because on one of the articles that talked about live data for multicloud, asked the question, and her headline was "Are You Down to Boogie?" So, disco continues to be a great meme for us, with our name. (John chuckles) Unintentional, so, as a marketer, it's a pretty fun time to be at WANdisco. >> Seventies and eighties were great times, certainly I'm an eighties guy, Joel, thanks for comin' on, appreciate the update, Joel Horowitz, CMO, Chief Marketing Officer, WANdisco, really on a nice wave right now, cloud growth, data growth, all comin' together, real IP, lookin' forward to hearing more, what comes down the pipe for those guys, you'll see him at IBM Think. I'm John Furrier here, in the studios at Palo Alto, thanks for watching. (soaring orchestral music)

(techno music) >> Announcer: Live, from downtown San Francisco, it's theCUBE. Covering IBM Chief Data Officer Strategy Summit 2018. Brought to you by IBM. >> Welcome back to San Francisco everybody, this is theCUBE, the leader in live tech coverage. We're here at the Parc 55 in San Francisco covering the IBM CDO Strategy Summit. I'm here with Joel Horwitz who's the Vice President of Digital Partnerships & Offerings at IBM. Good to see you again Joel. >> Thanks, great to be here, thanks for having me. >> So I was just, you're very welcome- It was just, let's see, was it last month, at Think? >> Yeah, it's hard to keep track, right. >> And we were talking about your new role- >> It's been a busy year. >> the importance of partnerships. One of the things I want to, well let's talk about your role, but I really want to get into, it's innovation. And we talked about this at Think, because it's so critical, in my opinion anyway, that you can attract partnerships, innovation partnerships, startups, established companies, et cetera. >> Joel: Yeah. >> To really help drive that innovation, it takes a team of people, IBM can't do it on its own. >> Yeah, I mean look, IBM is the leader in innovation, as we all know. We're the market leader for patents, that we put out each year, and how you get that technology in the hands of the real innovators, the developers, the longtail ISVs, our partners out there, that's the challenging part at times, and so what we've been up to is really looking at how we make it easier for partners to partner with IBM. How we make it easier for developers to work with IBM. So we have a number of areas that we've been adding, so for example, we've added a whole IBM Code portal, so if you go to developer.ibm.com/code you can actually see hundreds of code patterns that we've created to help really any client, any partner, get started using IBM's technology, and to innovate. >> Yeah, and that's critical, I mean you're right, because to me innovation is a combination of invention, which is what you guys do really, and then it's adoption, which is what your customers are all about. You come from the data science world. We're here at the Chief Data Officer Summit, what's the intersection between data science and CDOs? What are you seeing there? >> Yeah, so when I was here last, it was about two years ago in 2015, actually, maybe three years ago, man, time flies when you're having fun. >> Dave: Yeah, the Spark Summit- >> Yeah Spark Technology Center and the Spark Summit, and we were here, I was here at the Chief Data Officer Summit. And it was great, and at that time, I think a lot of the conversation was really not that different than what I'm seeing today. Which is, how do you manage all of your data assets? I think a big part of doing good data science, which is my kind of background, is really having a good understanding of what your data governance is, what your data catalog is, so, you know we introduced the Watson Studio at Think, and actually, what's nice about that, is it brings a lot of this together. So if you look in the market, in the data market, today, you know we used to segment it by a few things, like data gravity, data movement, data science, and data governance. And those are kind of the four themes that I continue to see. And so outside of IBM, I would contend that those are relatively separate kind of tools that are disconnected, in fact Dinesh Nirmal, who's our engineer on the analytic side, Head of Development there, he wrote a great blog just recently, about how you can have some great machine learning, you have some great data, but if you can't operationalize that, then really you can't put it to use. And so it's funny to me because we've been focused on this challenge, and IBM is making the right steps, in my, I'm obviously biased, but we're making some great strides toward unifying the, this tool chain. Which is data management, to data science, to operationalizing, you know, machine learning. So that's what we're starting to see with Watson Studio. >> Well, I always push Dinesh on this and like okay, you've got a collection of tools, but are you bringing those together? And he flat-out says no, we developed this, a lot of this from scratch. Yes, we bring in the best of the knowledge that we have there, but we're not trying to just cobble together a bunch of disparate tools with a UI layer. >> Right, right. >> It's really a fundamental foundation that you're trying to build. >> Well, what's really interesting about that, that piece, is that yeah, I think a lot of folks have cobbled together a UI layer, so we formed a partnership, coming back to the partnership view, with a company called Lightbend, who's based here in San Francisco, as well as in Europe, and the reason why we did that, wasn't just because of the fact that Reactive development, if you're not familiar with Reactive, it's essentially Scala, Akka, Play, this whole framework, that basically allows developers to write once, and it kind of scales up with demand. In fact, Verizon actually used our platform with Lightbend to launch the iPhone 10. And they show dramatic improvements. Now what's exciting about Lightbend, is the fact that application developers are developing with Reactive, but if you turn around, you'll also now be able to operationalize models with Reactive as well. Because it's basically a single platform to move between these two worlds. So what we've continued to see is data science kind of separate from the application world. Really kind of, AI and cloud as different universes. The reality is that for any enterprise, or any company, to really innovate, you have to find a way to bring those two worlds together, to get the most use out of it. >> Fourier always says "Data is the new development kit". He said this I think five or six years ago, and it's barely becoming true. You guys have tried to make an attempt, and have done a pretty good job, of trying to bring those worlds together in a single platform, what do you call it? The Watson Data Platform? >> Yeah, Watson Data Platform, now Watson Studio, and I think the other, so one side of it is, us trying to, not really trying, but us actually bringing together these disparate systems. I mean we are kind of a systems company, we're IT. But not only that, but bringing our trained algorithms, and our trained models to the developers. So for example, we also did a partnership with Unity, at the end of last year, that's now just reaching some pretty good growth, in terms of bringing the Watson SDK to game developers on the Unity platform. So again, it's this idea of bringing the game developer, the application developer, in closer contact with these trained models, and these trained algorithms. And that's where you're seeing incredible things happen. So for example, Star Trek Bridge Crew, which I don't know how many Trekkies we have here at the CDO Summit. >> A few over here probably. >> Yeah, a couple? They're using our SDK in Unity, to basically allow a gamer to use voice commands through the headset, through a VR headset, to talk to other players in the virtual game. So we're going to see more, I can't really disclose too much what we're doing there, but there's some cool stuff coming out of that partnership. >> Real immersive experience driving a lot of data. Now you're part of the Digital Business Group. I like the term digital business, because we talk about it all the time. Digital business, what's the difference between a digital business and a business? What's the, how they use data. >> Joel: Yeah. >> You're a data person, what does that mean? That you're part of the Digital Business Group? Is that an internal facing thing? An external facing thing? Both? >> It's really both. So our Chief Digital Officer, Bob Lord, he has a presentation that he'll give, where he starts out, and he goes, when I tell people I'm the Chief Digital Officer they usually think I just manage the website. You know, if I tell people I'm a Chief Data Officer, it means I manage our data, in governance over here. The reality is that I think these Chief Digital Officer, Chief Data Officer, they're really responsible for business transformation. And so, if you actually look at what we're doing, I think on both sides is we're using data, we're using marketing technology, martech, like Optimizely, like Segment, like some of these great partners of ours, to really look at how we can quickly A/B test, get user feedback, to look at how we actually test different offerings and market. And so really what we're doing is we're setting up a testing platform, to bring not only our traditional offers to market, like DB2, Mainframe, et cetera, but also bring new offers to market, like blockchain, and quantum, and others, and actually figure out how we get better product-market fit. What actually, one thing, one story that comes to mind, is if you've seen the movie Hidden Figures- >> Oh yeah. >> There's this scene where Kevin Costner, I know this is going to look not great for IBM, but I'm going to say it anyways, which is Kevin Costner has like a sledgehammer, and he's like trying to break down the wall to get the mainframe in the room. That's what it feels like sometimes, 'cause we create the best technology, but we forget sometimes about the last mile. You know like, we got to break down the wall. >> Where am I going to put it? >> You know, to get it in the room! So, honestly I think that's a lot of what we're doing. We're bridging that last mile, between these different audiences. So between developers, between ISVs, between commercial buyers. Like how do we actually make this technology, not just accessible to large enterprise, which are our main clients, but also to the other ecosystems, and other audiences out there. >> Well so that's interesting Joel, because as a potential partner of IBM, they want, obviously your go-to-market, your massive company, and great distribution channel. But at the same time, you want more than that. You know you want to have a closer, IBM always focuses on partnerships that have intrinsic value. So you talked about offerings, you talked about quantum, blockchain, off-camera talking about cloud containers. >> Joel: Yeah. >> I'd say cloud and containers may be a little closer than those others, but those others are going to take a lot of market development. So what are the offerings that you guys are bringing? How do they get into the hands of your partners? >> I mean, the commonality with all of these, all the emerging offerings, if you ask me, is the distributed nature of the offering. So if you look at blockchain, it's a distributed ledger. It's a distributed transaction chain that's secure. If you look at data, really and we can hark back to say, Hadoop, right before object storage, it's distributed storage, so it's not just storing on your hard drive locally, it's storing on a distributed network of servers that are all over the world and data centers. If you look at cloud, and containers, what you're really doing is not running your application on an individual server that can go down. You're using containers because you want to distribute that application over a large network of servers, so that if one server goes down, you're not going to be hosed. And so I think the fundamental shift that you're seeing is this distributed nature, which in essence is cloud. So I think cloud is just kind of a synonym, in my opinion, for distributed nature of our business. >> That's interesting and that brings up, you're right, cloud and Big Data/Hadoop, we don't talk about Hadoop much anymore, but it kind of got it all started, with that notion of leave the data where it is. And it's the same thing with cloud. You can't just stuff your business into the public cloud. You got to bring the cloud to your data. >> Joel: That's right. >> But that brings up a whole new set of challenges, which obviously, you're in a position just to help solve. Performance, latency, physics come into play. >> Physics is a rough one. It's kind of hard to avoid that one. >> I hear your best people are working on it though. Some other partnerships that you want to sort of, elucidate. >> Yeah, no, I mean we have some really great, so I think the key kind of partnership, I would say area, that I would allude to is, one of the things, and you kind of referenced this, is a lot of our partners, big or small, want to work with our top clients. So they want to work with our top banking clients. They want, 'cause these are, if you look at for example, MaRisk and what we're doing with them around blockchain, and frankly, talk about innovation, they're innovating containers for real, not virtual containers- >> And that's a joint venture right? >> Yeah, it is, and so it's exciting because, what we're bringing to market is, I also lead our startup programs, called the Global Entrepreneurship Program, and so what I'm focused on doing, and you'll probably see more to come this quarter, is how do we actually bridge that end-to-end? How do you, if you're startup or a small business, ultimately reach that kind of global business partner level? And so kind of bridging that, that end-to-end. So we're starting to bring out a number of different incentives for partners, like co-marketing, so I'll help startups when they're early, figure out product-market fit. We'll give you free credits to use our innovative technology, and we'll also bring you into a number of clients, to basically help you not burn all of your cash on creating your own marketing channel. God knows I did that when I was at a start-up. So I think we're doing a lot to kind of bridge that end-to-end, and help any partner kind of come in, and then grow with IBM. I think that's where we're headed. >> I think that's a critical part of your job. Because I mean, obviously IBM is known for its Global 2000, big enterprise presence, but startups, again, fuel that innovation fire. So being able to attract them, which you're proving you can, providing whatever it is, access, early access to cloud services, or like you say, these other offerings that you're producing, in addition to that go-to-market, 'cause it's funny, we always talk about how efficient, capital efficient, software is, but then you have these companies raising hundreds of millions of dollars, why? Because they got to do promotion, marketing, sales, you know, go-to-market. >> Yeah, it's really expensive. I mean, you look at most startups, like their biggest ticket item is usually marketing and sales. And building channels, and so yeah, if you're, you know we're talking to a number of partners who want to work with us because of the fact that, it's not just like, the direct kind of channel, it's also, as you kind of mentioned, there's other challenges that you have to overcome when you're working with a larger company. for example, security is a big one, GDPR compliance now, is a big one, and just making sure that things don't fall over, is a big one. And so a lot of partners work with us because ultimately, a number of the decision makers in these larger enterprises are going, well, I trust IBM, and if IBM says you're good, then I believe you. And so that's where we're kind of starting to pull partners in, and pull an ecosystem towards us. Because of the fact that we can take them through that level of certification. So we have a number of free online courses. So if you go to partners, excuse me, ibm.com/partners/learn there's a number of blockchain courses that you can learn today, and will actually give you a digital certificate, that's actually certified on our own blockchain, which we're actually a first of a kind to do that, which I think is pretty slick, and it's accredited at some of the universities. So I think that's where people are looking to IBM, and other leaders in this industry, is to help them become experts in their, in this technology, and especially in this emerging technology. >> I love that blockchain actually, because it's such a growing, and interesting, and innovative field. But it needs players like IBM, that can bring credibility, enterprise-grade, whether it's security, or just, as I say, credibility. 'Cause you know, this is, so much of negative connotations associated with blockchain and crypto, but companies like IBM coming to the table, enterprise companies, and building that ecosystem out is in my view, crucial. >> Yeah, no, it takes a village. I mean, there's a lot of folks, I mean that's a big reason why I came to IBM, three, four years ago, was because when I was in start-up land, I used to work for H20, I worked for Alpine Data Labs, Datameer, back in the Hadoop days, and what I realized was that, it's an opportunity cost. So you can't really drive true global innovation, transformation, in some of these bigger companies because there's only so much that you can really kind of bite off. And so you know at IBM it's been a really rewarding experience because we have done things like for example, we partnered with Girls Who Code, Treehouse, Udacity. So there's a number of early educators that we've partnered with, to bring code to, to bring technology to, that frankly, would never have access to some of this stuff. Some of this technology, if we didn't form these alliances, and if we didn't join these partnerships. So I'm very excited about the future of IBM, and I'm very excited about the future of what our partners are doing with IBM, because, geez, you know the cloud, and everything that we're doing to make this accessible, is bar none, I mean, it's great. >> I can tell you're excited. You know, spring in your step. Always a lot of energy Joel, really appreciate you coming onto theCUBE. >> Joel: My pleasure. >> Great to see you again. >> Yeah, thanks Dave. >> You're welcome. Alright keep it right there, everybody. We'll be back. We're at the IBM CDO Strategy Summit in San Francisco. You're watching theCUBE. (techno music) (touch-tone phone beeps)

>> Narrator: Live from Las Vegas, it's theCUBE! Covering IBM Think 2018. Brought to you by IBM. >> Hello everyone and welcome back to theCUBE's exclusive, three days of coverage here at IBM Think 2018. I'm John Furrier co-host with Dave Vellante, hosting three days and next is Joel Horowitz, Vice President Strategic Partnerships and Offering, of The Digital Business Group. >> Thanks. >> Welcome back to theCUBE. Good to see you. >> Good to see you guys. Thanks for having me here. >> Thanks for coming on. >> You've been on theCUBE, probably so many times, talking big data, talking analytics, now, in your new role in The Digital Group, the digital transformation. I really want to just ask you right off the bat about your new role, and how it relates to the changing ecosystem. >> Joel: Yeah. >> All of these markets are changing big time, the role of the ecosystem, the leverage that they have with technology and the value propositions, whether it's decentralized applications in Blockchain to storage and infrastructure, and big data. What is your role, take a minute to explain what you're doing, because you have a unique position, because this demand for partnerships, this demand for collaboration at many levels. What's the latest? >> So I would describe my role as being a champion of our partners, for sure. I look at, you know, I take, a very outside in perspective on IBM. Joining just over three years ago now, I came in, really through analytics, as you know, focused on machine learning, data science, and the growth of A.I. at that time. Last year I was part of the corporate development team over there. So looking, really, at a lot of the industry trends and what's going on, as well, in analytics, data, and A.I. This year, you know, we recognize that we're only going to do so many strategic partnerships a year, right, where there's probably a handful that we're going to work with. For example, last year we did a great partnership with Lightbend to bring their reactive platform to IBM, and we launched the iPhone 10, with Verizon on Lightbend's platform. But, these days, my team, can't be everywhere, obviously, and part of the value of digital, and that route to market is really the idea that partner should be able to self service. So, you know, my job this year, is frankly to put myself out of a job, right. Meaning, if I can get, you know, 70% of the work my team does, right, contracting, legal, setting up, provisioning, all of that on our cloud, and partners can just do that themselves. Then we'll capture a much larger swath of the emerging A.I., data, and cloud market. >> I want to talk about the killer app creating value and then the role the market place is playing. You mentioned self service. I want to kind of go down that. Before we get there, I want to get your thoughts on this because I noticed, in your role you're covering, it's cutting across a lot of different things, and you know we've been talking about cloud, as a horizontally disrupting technology, >> Joel: Yeah. >> Certainly in the data space you saw that. And stacks will be horizontally scalable with the cloud. >> Yeah. >> But you could be vertical specialization in the applications. So I noticed you're covering analytics, Watson, Cloud, hybrid cloud, emerging technologies. >> Yeah. >> Blockchain, and many others. >> Yeah. >> So talk about, it's obvious you guys are now cutting across, horizontally, across the different IBM divisions. Is that by design? >> Yeah. >> What's the impact of the ecosystem and partners for that horizontally cut over? >> Yeah, I know, I mean it's a great question, I think. Look, there are some specific design patterns that we see across every technology, across every, you know, business at IBM. One design pattern is pretty obvious, you saw it with the launch of the IBM cloud private data. Following up on last years IBM Cloud Private. And that design pattern is really about people containerizing applications. And so, at the end of the week, we have the business partner, or PartnerWorld Leadership conference. Excuse me. Where a number of our partners really are looking at how do I bring that work load to the cloud. And it's not so much the cloud is the end point. That's really the starting off point to A; Get much wider distribution and B; Be able to take advantage of a lot of these emerging technologies, like Blockchain, like A.I. Like IOT, and numerous others, Quantum, et cetera, they'll just keep coming. So really cloud to me is just a way for us to open the door to a lot of the technology that's flooding the market. >> Dave: Joel, can you talk about partnership, you mentioned before that you guys are kind of selective, John calls them Barney deals, ya know. I love you, you love me. You guys sound like you don't look for those, not volume, it's quality. >> Yeah. >> What are the criteria that you're looking for? How do you get value out of those? How do you measure that value out of the partnerships? If someone is a prospective partner out there, how should I be interacting with you? >> Yeah, I think, there's probably two steps. I think one is really recognizing that, in my own personal view, is that we really want to partner with folks who embrace open standards. Now I'm not going to like go as far to say open source, 'cause I think there is a lot that goes into that. But I will say open standards, meaning, not these like large monolithic applications, but can you actually integrate with us in some meaningful way? And to do that, that's why we actually started on this new platform that we are launching today. Called IBM Partner Self-service. Is the ability to first integrate with IBM. So, if you can demonstrate that you can build with IBM first, whether that's a startup, an ISV, a business partner. Like that's criteria number one. Criteria number two is are you a trusted partner? So, do you actually have the same level of competency that we would expect from, frankly, our own sellers, and our own people. And so, to do that, we've also launched new competency paths for business partners and partners as well. So, those are the two major criterias. And then the third one, which I think is kind of the holy grail, is selling with IBM. So we also launched a sell with path today where you can actually list in our marketplace. And then we will actually help you reach new markets. And then demonstrate there's clients, there's a client need that really wants our joint solution, right? And so, to me, those are the three things, to re-state. Like, you know, building with us, having a level of competency with us, and then demonstrating client success with us. >> Okay, so, integrate, you really don't need you guys to do that. I can just dive in and do that. Bake it out a little bit, and then approach you. What kind of help do you give? Do you have programs once you get by those gates? >> So, you know, I would categorize into two groups, I think we have a ton of online support. So, you know, we even embrace Slack at IBM. If you're not aware of that, we have Slack everywhere. And, so, for a self service, I want to say, look, what does zero touch mean, right, in this day and age, for a partner. And so, they can go to our site today, and actually get, you know, sign up for Slack, and talk directly to our technical specialist as well as to our developer advocates. And so, on the enablement and integration side, my colleague, Angel Diaz and team, have done a great job of launching hundreds of IBM code patterns. So that you can just pick these artifacts up, these assets up, and leverage them to integrate all sorts of capabilities into your product. >> You know, Dave, I want to get your thoughts on this, because you and I have been talking about the API integration, and I want to get back to Joel's point in a second because I think this is critical for startups and ecosystem partners. API's are the (speaking quickly) for developers right now, so if I don't want to take a big chance on being all in on IBM, say I want to kick the tires, API's are critical. So the question is, are you seeing that traction on your side of the house, in terms of the end now, since the level of API integration, is that the touchpoint? Is it like the beginning phases? And what level of commitment that you're seeing with people. >> Well, John, to me it comes down to innovation, and it's interesting because Joel came out of the data world. To me, the innovation in the next 10 years starts with data. The second component of that innovation, I think, over the next decade or so is going to be, really, A.I., whether you call it cognitive or machine intelligence or artificial intelligence. And then third, I think is cloud economics and that's really where the API economy fits in. You got to have API'S to integrate, as Joel was saying. You've got to have marginal... You've got to have scale, marginal costs go to zero eventually. You've got to have network effects and you've got to be able to track startups, which is another question I have. >> Now Joel, back to you, on the start on the integration, whether it's a startup or a big company. It used to be, the old days, you got to go all in. You've got to get the developer kit, >> Joel: Yeah. >> Download it, line it to a swim lane, get deeper, prove your value. >> Yeah. >> Find the value's faster; what's the first hurdle if someone wants, hey I want to give IBM a shot here? Love the sell, holy grail option, is it API'S, can people integration on their own? Talk about that specific first step because some people might open up the door and go whoa! There's more here than I thought. Or, wow, there's some real tech. Or, I don't want to use IBM tech, I want to use some of mine. There's that first indifference point. >> Yeah, I think there are areas where we've seen dramatic customer experience improvements. So to give one example, as we've partnered with Ubisoft, Redstorm last year around a new title game that they released called Star Trek Bridge Crew, and so, you know, to me, we went on our own merit, and I think that publisher chose IBM because Watson Conversation is absolutely the best on the market. And so what that did is it enabled game players, their end customer, their end user, to speak into a VR headset and just give commands, as you would naturally. And so, I think a lot of, as you think about IBM, it's, yeah, we've made it completely easy to access our API'S. I think, there's a great quote from the founder of Flickr that I read years ago, I'll go dig it up for you guys later, but it was along the lines of business development means, today is exposing your API'S, like, that's it! And, on the other side of it, we give a lot away in terms of cloud credits, right, and so, today, if you go and sign up on our self service platform, we'll give you $10,000 a month in free cloud credits to build and build quickly. Because, at the end of the day, if it's not self service, if it requires more heavy lifting, then, frankly, we're not doing our jobs. And so that's my commitment, is to make sure that is available, is accessible, and there's experts there that can help you on your journey. >> So that attracts startups, obviously, 10K a month is a honey pot for those guys. What about existing IBM clients that want to get to the cloud. Migrate to the cloud. How do you help those guys? >> Yeah, so, in the migration front, we have a great team in place with IBM services, who basically have set up a migration factor, if you will, and there are numerous ways to chart your course to the cloud. Whether it's, you know, full cloud or hybrid cloud, or some offloading, some aspects to the cloud. There's a lot of different paths you can take and so to do that, we're offering $50,000 in migration credits for the first couple months. We're also offering 35% off for professional services. So, we have a great offer going on over the next few months to help people make that first step. >> Incentives are key. >> And, look, we're here with you so it's not like, here, throwing it over the fence, and good luck! You know, tweet at me, instant message me, I'm around. And I will be absolutely committed to partner success. >> Yeah, you know, incentives are critical, that's going to get the market going. But, the end of the day, it's the type of value, and I want to get your thoughts, it's something that's come up that I've heard people talk about in the hallways and other conferences. They kind of chirp about "Hey, you know, "I'd like to get this, from suppliers. "I want to see more tools, more programs "to help me get more customers, to get more value. "I'm building apps, but also got a business to run." What are some of the conversations you've had over the past year with customers and partners? Stack rank the top three or four things that they talk about, either their pain points or things that are on their mind, that's worth noting? >> I mean, I would say first and foremost, I mean, me, myself, being in a startup at H2O. Three, four years ago. We used to walk in there and sell into the data scientists, right, so if you don't know H2O, they're a great company, a machine learning company, but we would get the data scientists really excited about working with our product, and then lo and behold, we'd get to the CIO office saying, "Hey, what is this stuff? "Get it out of here." You know, Hadoop was the same way, by the way, 2010 working at AVG, like, we'd bring in Hadoop. Like what is this data like thing? There's no governance, it's a mess. Where they could really, you know, work with IBM, where they see value from IBM is when we go into the CIO office together and say, look, we've demonstrated that there's value here. We've demonstrated that there's actual customer need. We can create a lot of help in terms of getting the rest of the organization bought in. Put in the right governance around it. Because, look, I mean GDPR is real, it's a big deal. Like, data privacy, is huge. So, you know, Rob Thomas likes to say, "You can't have good A.I. without I.A." I think that's a great information architecture. So, I agree, and so I think that's what the number one benefit is. Really get in there, move quickly, demonstrate value, and then when you're ready to make that next step of how you roll that out to the rest of the enterprise, that's when IBM becomes a huge help. >> You know, you mentioned GDPR. With regulatory issues now are becoming criteria for a lot of application developers that are small that may not have the resources to handle the right to get your name out of a database or other tools, and other regulations, certainly. Decentralize applications with Blockchain, another regulatory challenge-- >> Yep. >> Opportunity as well. Are you guys having those kinds of conversations, like putting specific things in place beyond GDPR, and if so what regulatory and legal things do you see out there that could be blockers for customers, that you guys hope to go after? >> I mean, I don't think there's a one word answer here. I do think that you take it on a case by case basis. I think you're seeing different countries adopt GDPR differently. Germany, obviously, being a very strict kind of country in doing that. So, you know, IBM services, as well as our analytics team, are really focused on that. I think, like I said, what you saw with ICP data coming out this week, I think that's a really important way to look at it. My own personal view, I think, for sure there's a lot of compliance, They have to look at, and understand the workflow, workflows of how people are using that data, as well as application architecture is big. And those are all the considerations, I think, that you are going to see as people move. I read a statistic that 40% of all CSP'S, MSP'S, are moving, are growing, like it's 40% growth from IBC, 50% of all developers are now embedding A.I. So, this market is growing and growing fast. But, you're right. If folks out there aren't really taking GDPR seriously, you can get yourself into some hot water. >> Well, we've observed that scale matters, certainly, whether it's a partner or cloud, that gets, that helps people. >> Yeah. >> Joel, well, thanks for coming onto theCUBE, we really appreciate it. >> Yeah, my pleasure. >> Before we end, I want to get your thoughts, just share with the folks that are watching. What kind of deals do you want to do? What's on your radar? What's the priorities for you? From a strategic business development standpoint. To develop across that horizontally scalable, IBM division space, as well as technology space? >> You know, it's not what deals I want to do, it's really what deals our partners want to do. >> Come on, your in charge, come on. >> It's really what deals our partners want to do, ya know. I mean, look, I get excited about transforming industries, I really do, so I look at, not what's the transactional partnership, like go, we'll do something, and there's some revenue, or something. I look at how do we transform an industry? >> Let me rephrase the question. What's on the priority list for you guys, from a transformational area, that's important for your partners. >> Yeah, I would say for sure, obviously, A.I. is huge. Obviously data is huge, obviously cloud is huge. But, looking really specific, I think you just add tech after each industry. So Addtech, Fintech, Healthtech obviously. Game tech and, I think, probably the last one, to me personally, is the most exciting. We signed an amazing deal with Unity at the end of last year, the start of this year. In fact GDC game developer conference is going on as we speak in San Francisco. So half my team right now is over there, demonstrating Watson as like VR, AR, and it's not just for games, right. It's like with BMW and VW doing some cool stuff there as well. So, I'm really excited about the, AR, VR, industry growing, especially with our partner Unity. >> There's a new creative out there-- >> Can I jump in before you exit? I want to ask you a follow up on that, because if transformation is sort of the target for your partnerships. Healthcare is an area that should be transformed. But, needs to be transformed, but it's hard to transform healthcare. >> Joel: It is, yeah. >> Do you feel like you could start moving the needle from a partnership perspective? Or is that going to take some more time? >> You know, I think there's a lot of great work being done there. I do believe... Look, in general, I think we can move a lot faster with partners, in fact, I like to call it like the Nordstrom model. Right? Like IBM in the past has been Barney's of New York, forever, right? From a branding and from how we partner with folks, like I think we need to move more to a Nordstrom, like, yeah, we'll sell our own offerings off the rack, but then we need to help partners come in and create the right styles for the right need and the right industry. >> Yeah and then there's a Nordstrom Rack you're going to need to put that on. (laughing) Over technology goes the Nordstrom Rack. Joel Horowitz, thanks for coming out. Vice President Strategic Partnerships and Offerings, here on theCUBE. I'm John Furrier with Dave Vellante, with three days of IBM Think live streaming, all of the videos will be up on thecube.net sports live now. Youtube.com/siliconangle for all the ondemands when the show's over. We'll be right back with more after this short break. (light techno music)

>> Announcer: Live from Las Vegas, it's the Cube, covering VMworld 2017. Brought to you by VMware and its ecosystem partners. (bright music) >> Welcome back to the Cube's continuing coverage of VMworld 2017. I'm Lisa Martin with my cohost Dave Vellante. Dave, it's been an exciting almost two full days, and we're very excited to be joined by Joel Reich, the EVP of products and operations at NetApp. And none of us can believe you haven't been on the Cube before! >> I haven't been. I'm sure I've sure I've watched a lot of the episodes, but I've never been a guest. >> I apologize for that. I'm shocked, given our past history and relationship, but welcome. >> Thank you. >> And we like to hear that- >> We'll see how I do before you invite me back again. (laughing) >> As a former NetApp-ian, I have high hopes. So Joel, NetApp is positioning itself as a storage software company for multi-cloud world. What does that really mean? >> So what it means is that, you know, when I think about it, when I talk to customers, the problem space about how do I manage my data has gotten so wide and so broad in the last three years because, you know, it used to include to walls, just the space between the four walls of the data centers that you owned. And now the problem space is that plus other data sources you might need to take to grow your business. It might be infrastructure as a service that you built in the cloud. It might be a software as a service application that you're running that's still your data, but it's in some other person's data center. So what it means is that we've really focused on the fact that the world is going to be hybrid cloud and that it's going to be multi-cloud. And the problems that, a high-class problem to solve is how to allow people to manage their data in that world. >> And we've been talking all week on the Cube, and you see it now at VMware's results, there's a lot of tailwinds, but part of that is the customer reality that they can't bring their business into the cloud and reformulate it to force fit it into the cloud. Rather, they have to bring the cloud to the data. >> Joel: Yep, exactly. >> That's sort of your wheelhouse. When you think about multi-cloud, are you talking about being the data store that multiple clouds can access? And do you see it going beyond that, where people are, you know, cross-clouding, if you will, inter-clouding? >> Sure, yeah, I mean, and the mechanisms vary by which people will do that. You know, we see people wanting to still own their data, in other words still have it behind their firewall. So one of the ways they can cross connect to multiple clouds is they can put a storage system, you know, NetApp storage system, in a cold location facility, let's say an Equinix facility, and there they have access to all of the fastest, broadest pipes that you could find anywhere on the network. And you can do a direct connect to Azure. You can do an express connect, or direct connect to Amazon or express connect to Azure, and make a decision about, you know, who has the best pricing plan or who has the best contractual terms or legal terms for you to go to the cloud. So you know, there's that way to do it when you're trying to manage, really just physically own your data when you're concerned about the privacy or sovereignty of it. There's other ways of doing it also, where our storage management software can sit in the cloud. We have a product called Cloud ONTAP, and you can buy it as a service in Amazon, or you can buy it as a service in Azure. And you could store your data in their infrastructure, and because it has a built-in capability of moving from one, you know, from one vendor to another, you don't have to worry about format differences between the cloud vendors. So you can migrate, you know, across, you could SnapMirror, which is a replication capability that we've had for 20 years, and you could be multi-cloud in that sense also. >> You know what's interesting about that, Joel, is when I first started looking at NetApp, and there was no such thing as cloud, but it struck me that you guys were early on with the concept of storage as a service. You had many, many services within your solutions, whether it was copy services or even data reduction services, well ahead of its time, that were bundled in to the platform. And you would invoke those as necessary. Very sort of cloud-like or, you know, we all talk about serverless today, and that's sort of the model, is invoking services as I need them. >> Yes. >> Kind of composable, if you will. So very compatible, in concept anyway, with cloud. So take us through kind of where you are today, with the architecture, you know, used to be so simple. EMC block, NetApp file, right? And that's changed dramatically. And of course I'm oversimplifying. Where are we today with the portfolio and the company strategy? Maybe you could talk to things like all-flash arrays, hyper-converged, bring us up to date. >> So I mean, I think there's, we look at, you're right, we do take a services perspective of what we're building. And what I do, we look at that there's a consumption continuum that people actually want to buy those services in different shapes or forms. So when we think about it, it's not, we don't have a separate clustered data ONTAP roadmap for, you know, our next high-end FAS system that has different features than the roadmap for the version of ONTAP that's going to run in the cloud. It's actually one product, right, that we build to be able to be consumed in different ways. And that, you know, when you think about it, that's kind of like a microcosm of our strategy, which is that what we're trying to do is make those data services available no matter where the data happens to be. And so to give you an example of a new service that we've implemented as part of cluster data ONTAP, it was in a new release that we did this past spring called 9.2. So it has a feature in it called Fabric Pools. And what Fabric Pools allow you to do is, you know, we have this idea that over time, storage becomes a service-level based thing and you have a capacity tier and you have a performance tier. And over time whatever that performance tier is going to be built of is going to be, you know, as flash progresses to Optane and things like that, that capacity tier is going to get faster and faster and faster, and it's always going to be a little bit more expensive than the, I mean, the performance tier is going to get, you know, it's going to get bigger, it's going to get faster, it's always going to be more expensive than the capacity tier. So Fabric Pools is essentially designed, if you forward think a bunch of years, when what you've got in the data center is all flash, you know, where's your capacity tier going to be? Well so in essence what we're doing is we're doing tiering between all flash and any S3 target. So that S3 target could be Glacier. The S3 target could be S3, I mean the S3 target could be, you know, it could be any S3 target. It could anybody's object store. And essentially what happens is, the system will manage secondary data and snapshot data into that capacity tier, but it'll manage it all together so that you're getting the most efficient use you can of flash. So you look at that and say, okay, that's the consumption model. That's a traditional consumption model, where you're buying controller-based functionality. Well it turns out that cloud ONTAP can use Fabric Pools also. So what that means is I can deploy, I could go to Amazon's Marketplace, I can buy Cloud ONTAP for I think it's $1.45 an hour, and I can run an instance, I can set up a cluster in the cloud, and I could use EC2 storage. And I could use Amazon storage and I could run ONTAP on it, and then if I want to I could implement Fabric Pools and I could tier to S3 or Glacier, right, within the cloud. >> The point is, the value of that is single point of control. >> Joel: Yeah. >> And customers will pay for that value. >> My point is actually, the point is was trying to make is that it's a consumption model choice. I'm not going to force someone, I view the cloud as our friend, right, what we're trying to do is find ways for people to leverage either the infrastructure of the public cloud or their on prem infrastructure to be able to manage their data in ways that they can't. >> And discretely selling that software as a service. >> Joel: Yes. >> Yeah. >> Exactly, or as a feature of our standard appliance product. >> But a much different model than taking a Seagate disk drive, packaging it into a controller, and selling it for 10X what you paid for it. >> Right, exactly. So you know, that's really the exciting thing is trying to find ways of making what we have portable into the different ways that people want to consume data management services these days. >> A couple questions for you, Joel. You mentioned the word "friends." You've been, NetApp, long-time friends with VMware. >> Yes. >> Since we're one year post combination of Dell EMC, how has the NetApp VMware relationship evolved? One of the things we heard Michael Dell say this morning was it's very important to maintain the independence of the VMware ecosystem. Talk to us about how in the last year that relationship has progressed and how that's helping NetApp continue its history of being very innovative. >> Yah, so there's always been coopetition, there's always been places where, you know, our products overlapped and where you could do similar things with VMware at the server level that we could do at the storage level. But from the beginning, you know, the integrations that we did with them I think were, you know, really helped move the market and really helped move both of our businesses. I think there's like three things that we're doing right now that are new, you know, in the last year with them. One of them is we built another version of ONTAP, which we call vNAS, which can run on top of VSAN. And you know, in an ESX environment and provide, you know, NFS and CIFS file services on top of VSAN, right? So that's a really interesting combination of both of our software-defined products that solve a customer problem. Another thing we've done is, you know, we've announced our HCI product, NetApp HCI, and we have a really close partnership with VMware. We decided that that was the way to go, that we didn't want to build our own hypervisor, and that they did a really good job on the management side, and you know, that our integration of those three things would build something, you know, with our strength being at the storage management layer and their strength being at the hypervisor and management layer, that that would help us build a really effective, competitive product. So you know, I think those are two really good examples of that the partnership is moving forward. Lots of interesting integrations, we're working on figuring out how to bring value to what VMware is now doing with AWS. Cause we have a very large install base. I came to the show and I asked, well how many combined customers do we have with VMware? And it's 50,000 combined customers over the years we've been doing this. So you know, our customers want to know how to get access to that capability, how to move into AWS in a way that provides them an interim step. So there's some really good cooperative work going on between our developers in that area. >> And a couple of strong GTM routes, right, through, you mentioned a new version of ONTAP for VSAN. Yesterday Pat Gellsinger mentioned there's now 10,000 customers on VSAN, talked about obviously with AWS as another GTM opportunity for you. >> Yeah, I mean, our teams work together great. There's no question about it. >> Yeah, I mean you guys have always been right there, in the inner circle of integration with VMware. I mean, in the days where, you know, EMC was trying to control the chessboard, NetApp was always able to have products like, same day, you know, as integrated as anybody. And that was important for VMware to show its independence. >> Well you know, Mountain View is much closer to Sunnyvale than Hopkinton. (laughing) >> It's true. >> So you talked about, you chose not to develop your own hypervisor. Others have. So maybe talk about that a little more, how you differentiate from some of the other hyper converged players. >> I mean, I don't, you know, there are other ways of dealing with, you know, when people don't want to spend money on a license, there are other ways of dealing with that problem than building your own hypervisor. You know, for example, so in our HCI product, we can scale server and, we can scale compute and storage independently. So you don't actually get locked into buying, you don't have to buy another VMware license if all you're doing is selling combined storage, you know, combined HCI nodes. By breaking them apart and having separate HCI nodes, we don't drive people into consuming VMware licenses that they might not need, right, in order to meet the demands of, you know, what they're trying to build. So I think we've taken a much different approach to HCI. We talk about it as second generation. The core, there's a lot of value to it. The core value in differentiation is really ease of setup and use that people have grown to expect from HCI combined with an amazing amount of quality of service and workload guarantee, you know, guaranteed workload per workload performance and scaling to, you know, 100 nodes, which, you know, we think really makes HCI a data center class technology. You know, not an edge technology, not a single application technology, but by adding data management features and having that real ability to scale to very large systems, we think we really, you know, come into the market at a time when HCI is ready to move to that next step of not just being single workload, single application. So we think we're there at a good time, with the right product. >> How about all-flash arrays? Bring us up to speed on that. You guys made an acquisition of SolidFire, great acquisition, picked it up at a good time in the marketplace, got it for I think a relatively good price, really good company, true software defined, built for sort of cloud-oriented applications. So how have you integrated that asset, where do it fit in your portfolio? And maybe you can share some proof points. >> Yeah, so we've seen a lot of success with that. You know, what we were doing, when we bought SolidFire, there were a whole bunch of motivations for it. One of the motivations was we knew we needed access to the new buyers. We knew we needed access to people who were making decisions about deploying applications independent of the infrastructure that they happened to have in their data center. Right, they were trying to find new ways of doing things. So when we bought SolidFire, you know, a lot of it was, we loved the technology. A lot of it was getting access to the new buyers and bringing them to the table. And it's funny cause I was noticing today in a bunch of customer meetings that I had here, that you know, in the past I'd have meetings and it would be like sort of the same IT stack, here's the system admin, here's the server admin, here's the network admin, here's the storage person sitting around the table. And when I talk about, you know, we talk about the data fabric, which is the way we tie together, you know, our hybrid clouds. When I talk about that, you know, either people would start to yawn or they'd start to feel threatened because you know, we're talking about something that was a new world for them that they didn't quite know how they would fit in. One of the things I'm seeing now, especially this year, is that customers are coming to the table with both with a cloud architect, and you know, the person who's trying to figure out how they get to the next place, plus the person who owns the existing infrastructure, and they're trying to figure out how to modernize it. So it's something, you know, when we bought SolidFire, we had this theory, okay, we got to go, we have this new technology, it's aimed at a new buyer. And one of the things I'm seeing now is that the portfolio sale of the things that we're offering is starting to be relevant to actually, we don't have to go find the different people. We're actually starting to see them come to the table and talk to us together. So you know, all-flash for us, that's been what's driving the company. We went, we made a big investment about two and a half years ago. It started to pay off last year. We're still growing much faster than the market, much faster than companies who are a lot smaller than us, and the last, you know, market research data that I saw had us as number two in the world, after really not even, you know, being in the top single digits about three years ago. So that's been a really good thing for us, both for our install base but also winning new footprint and winning new business that we didn't have before. We're displacing legacy competitors, one a day. I think George talked about it in our last earnings call. We're replacing EMC once a day, right, at least, and accelerating past that. And it's replacing the old stuff. And a lot of it is because of what we've done with flash. A lot of it is also because it's a future proof. Okay, well, how am I going to, so let's say I decide I want to move this part of this workload that's on here, one workload that's on here to the cloud next year. Alright, NetApp, how could you help me do that? And we'll go through and talk about, this is what we do with Azure, this is what we do with Amazon, right, this is what we do with IBM Cloud. None of our competitors can do that. >> Excellent, and so, sorry to cut you off, we've got to wrap. But you've got a NetApp Insight 2017 Change the World with Data coming up in, you're going to have to come back to Vegas in October and Berlin, and I'm sure, >> Right here, I might even be sitting in the same place. (laughing) >> I hope you get some fresh air. >> Let's make that happen. Let's get the Cube to Insight. >> There we go! Thank you so much for joining. You're now a Cube alumni, which is fantastic. >> Thank you, do I get a t-shirt? >> Congratulations, a pin I think. >> Yeah, a pin. Alright, well for my cohost Dave Vellante, I'm Lisa Martin, we want to thank you for watching. Come right back. We've got more exciting coverage from day two of VMworld 2017 right now. (bright music)

>> Narrator: Live from Boston, Massachusetts this is the Cube, covering Spark Summit East 2017 brought to you by Databricks. Now, here are your hosts, Dave Vellante and George Gilbert. >> Welcome back to Boston, everybody, where it's a blizzard outside and a blizzard of content coming to you from Spark Summit East, #SparkSummit. This is the Cube, the worldwide leader in live tech coverage. Joel Cumming is here. He's the head of data at Kik. Kicking butt at Kik. Welcome to the Cube. >> Thank you, thanks for having me. >> So tell us about Kik, this cool mobile chat app. Checked it out a little bit. >> Yeah, so Kik has been around since about 2010. We're, as you mentioned, a mobile chat app, start-up based in Waterloo, Ontario. Kik really took off, really 2010 when it got 2 million users in the first 22 days of its existence. So was insanely popular, specifically with U.S. youth, and the reason for that really is Kik started off in a time where chatting through text cost money. Text messages cost money back in 2010, and really not every kid has a phone like they do today. So if you had an iPod or an iPad all you needed to do was sign up, and you had a user name and now you could text with your friends, so kids could do that just like their parents could with Kik, and that's really where we got our entrenchment with U.S. youth. >> And you're the head of data. So talk a little bit about your background. What does that mean to be a head of data? >> Yes, so prior to working at Kik I worked at Blackberry, and I like to say I worked at Blackberry probably around the time just before you bought your first Blackberry and I left just after you bought your first iPhone. So kind of in that range, but was there for nine years. >> Vellante: Can you do that with real estate? >> Yeah, I'd love to be able to do that with real estate. But it was a great time at Blackberry. It was very exciting to be part of that growth. When I was there, we grew from three million to 80 million customers, from three thousand employees to 17 thousand employees, and of course, things went sideways for Blackberry, but conveniently at the end Blackberry was working in BBM, and leading a team of data scientists and data engineers there. And BBM if you're not familiar with it is a chat app as well, and across town is where Kik is headquartered. The appeal to me of moving to Kik was a company that was very small and fast moving, but they actually weren't leveraging data at all. So when I got there, they had a pile of logs sitting in S3, waiting for someone to take advantage of them. They were good at measuring events, and looking at those events and how they tracked over time, but not really combining them to understand or personalize any experience for their end customers. >> So they knew enough to keep the data. >> They knew enough to keep the data. >> They just weren't sure what to do with it. Okay so, you come in, and where did you start? >> So the first day that I started that was the first day I used any AWS product, so I had worked on the big data tools at the old place, with Hadoop and Pig and Hive and Oracle and those kinds of things, but had never used an AWS product until I got there and it was very much sink or swim and on my first day our CEO in the meeting said, "Okay, you're data guy here now. "I want you to tell me in a week why people leave Kik." And I'm like, man we don't even have a database yet. The first thing I did was I fired up a Redshift cluster. First time I had done that, looked at the tools that were available in AWS to transform the data using EMR and Pig and those kinds of things, and was lucky enough, fortunate enough that they could figure that out in a week and I didn't give him the full answer of why people left, but I was able to give him some ideas of places we could go based on some preliminary exploration. So I went from leading this team of about 40 people to being a team of one and writing all the code myself. Super exciting, not the experience that everybody wants, but for me it was a lot of fun. Over the last three years have built up the team. Now we have three data engineers and three data scientists and indeed it's a lot more important to people every day at Kik. >> What sort of impact has your team had on the product itself and the customer experience? >> So the beginning it was really just trying to understand the behaviors of people across Kik, and that took a while to really wrap our heads around, and any good data analysis combines behaviors that you have to ask people their opinion on and also behaviors that we see them do. So I had an old boss that used to work at Rogers, which is a telecomm provider in Canada, and he said if you ask people the things that they watch they tell you documentaries and the news and very important stuff, but if you see what they actually watch it's reality TV and trashy shows, and so the truth is really somewhere in the middle. There's an aspirational element. So for us really understanding the data we already had, instrumenting new events, and then in the last year and a half, building out an A/B testing framework is something that's been instrumental in how we leverage data at Kik. So we were making decisions by gut feel in the very beginning, then we moved into this era where we were doing A/B testing and very focused on statistical significance, and rigor around all of our experiments, but then stepping back and realizing maybe the bets that we have aren't big enough. So we need to maybe bet a little bit more on some bigger features that have the opportunity to move the needle. So we've been doing that recently with a few features that we've released, but data is super important now, both to stimulate creativity of our product managers as well as to measure the success of those features. >> And how do you map to the product managers who are defining the new features? Are you a central group? Are you sort of point guards within the different product groups? How does that, your evidence-based decisions or recommendations but they make ultimately, presumably, the decisions. What's the dynamic? >> So it's a great question. In my experience, it's very difficult to build a structure that's perfect. So in the purely centralized model you've got this problem of people are coming to you to ask for something, and they may get turned away because you're too busy, and then in the decentralized model you tend to have lots of duplication and overlap and maybe not sharing all the things that you need to share. So we tried to build a hybrid of both. And so we had our data engineers centralized and we tried doing what we called tours of duty, so our data scientists would be embedded with various teams within the company so it could be, it could be the core messenger team. It could be our bot platform team. It could be our anti-spam team. And they would sit with them and it's very easy for product managers and developers to ask them questions and for them to give out answers, and then we would rotate those folks through a different tour of duty after a few months and they would sit with another team. So we did that for a while, and it worked pretty well, but one of the major things we found was a problem was there's no good checkpoint to confirm that what they're doing is right. So in software development you're releasing a version of software. There's QA, there's code review and there's structure in place to ensure that yes, this number I'm providing is right. It's difficult when you've got a data scientist who's out with a team for him to come back to the team and get that peer review. So now we're kind of reevaluating that. We use an agile approach, but we have primes for each of these groups but now we all sit together. >> So the accountability is after the data scientist made a recommendation that the product manager agrees with, how do you ensure that it measured up to the expectation? Like sort of after the fact. >> Yeah, so in those cases our A/B tests are it's nice to have that unbiased data resource on the team that's embedded with them that can step back and say yes, this idea worked, or it didn't work. So that's the approach that we're taking. It's not a dedicated resource, but a prime resource for each of these teams that's a subject matter expert and then is evaluating the results in an unbiased kind of way. >> So you've got this relatively small, even though it's quadruple the size when you started, data team and then application development team as sort of colleagues or how do you interact with them? >> Yeah, we're actually part of the engineering organization at Kik, part of R and D, and in different times in my life I've been part of different organizations whether it's marketing or whether it's I.T. or whether it's R and D, and R and D really fits nicely. And the reason why I think it's the best is because if there's data that you need to understand users more there's much more direct control over getting that element instrumented within a product that you have when you're part of R and D. If you're in marketing, you're like hey, I'd love to know how many times people tap on that red button, but no event fires when that red button is tapped. Good luck trying to get the software developers to put that in. But when there's an inherent component of R and D that's dependent on data, and data has that direct path to those developers, getting that kind of thing done is much easier. >> So from a tooling standpoint, thinking about data scientists and data engineers, a lot of the tools that we've seen in this so-called big data world have been quite spoke. Different interfaces, different experience. How are you addressing that? Does Spark help with that? Maybe talk about that a bit more. >> Yeah, so I was fortunate enough to do a session today that sort of talked about data V1 at Kik versus data V2 at Kik, and we drew this kind of a line in the sand. So when I started it was just me. I'm trying to answer these questions very quickly on these three or five day timelines that we get from our CEO. >> Vallente: You've been here a week, come on! >> Yeah exactly, so you sacrifice data engineering and architecture when you're living like that. So you can answer questions very quickly. It worked well for a while, but then all of a sudden we come up and we have 300 data pipelines. They're a mess. They're hard to manage and control. We've got code sometimes in Sequel or sometimes in Python scripts, or sometimes on people's laptops. We have no real plan for Getup integration. And then you know real scalability out of Redshift. We were doing a lot of our workloads in Redshift to do transformations just because, get the data into Redshift, write some Sequel and then have your results. We're running into contention problems with that. So what we decided to do is sort of stop, step back and say, okay so how are we going to house all of this atomic data that we have in a way that's efficient. So we started with Redshift, our database was 10 terabytes. Now it's 100, except for we get five terabytes of data per day that's new coming in, so putting that all in Redshift, it doesn't make sense. It's not all that useful. So if we cull that data under supervision, we don't want to get rid of the atomic data, how do we control that data under supervision. So we decided to go the data lake route, even though we hate the term data lake, but basically a folder structure within S3 that's stored in a query optimized format like Parquet, and now we can access that data very quickly at an atomic level, at a cleansed level and also an at aggregate level. So for us, this data V2 was the evolution of stopping doing a lot of things the way we used to do, which was lots of data pipelines, kind of code that was all over the place, and then aggregations in Redshift, and starting to use Spark, specifically Databricks. Databricks we think of in two ways. One is kind of managed Spark, so that we don't have to do all the configuration that we used to have to do with EMR, and then the second is notebooks that we can align with all the work that we're doing and have revision control and Getup integration as well. >> A question to clarify, when you've put the data lake, which is the file system and then the data in Parquet format, or Parquet files, so this is where you want to have some sort of interactive experience for business intelligence. Do you need some sort of MPP server on top of that to provide interactive performance, or, because I know a lot customers are struggling at that point where they got all the data there, and it's kind of organized, but then if they really want to munge through that huge volume they find it slows to lower than a crawl. >> Yeah, it's a great point. And we're at the stage right now where our data lake at the top layer of our data lake where we aggregate and normalize, we also push that data into Redshift. So Redshift what we're trying to do with that is make it a read-only environment, so that our analysts and developers, so they know they have consistent read performance on Redshift, where before when it's a mix of batch jobs as well as read workload, they didn't have that guarantee. So you're right, and we think what will probably happen over the next year or so is the advancements in Spark will make it much more capable as a data warehousing product, and then you'd have to start a question do I need both Redshift and Spark for that kind of thing? But today I think some of the cost-based optimizations that are coming, at least the promise of them coming I would hope that those would help Spark becoming more of a data warehouse, but we'll have to see. >> So carry that thread a little further through. I mean in terms of things that you'd like to see in the Spark roadmap, things that could be improved. What's your feedback to Databricks? >> We're fortunate, we work with them pretty closely. We've been a customer for about half a year, and they've been outstanding working with us. So structured streaming is a great example of something we worked pretty closely with on. We're really excited about. We don't have, you know we have certain pockets within our company that require very real-time data, so obviously your operational components. Are your servers up or down, as well as our anti-spam team. They require very low latency access to data. We haven't typically, if we batch every hour that's fine in most cases, but structured streaming when our data streams are coming in now through Kinesis Firehose, and we can process those without have to worry about checking to see if it's time we should start this or is all the data there so we can run this batch. Structured streaming solves a lot of those, it simplifies a lot of that workload for us. So that's something we've been working with them on. The other things that we're really interested in. We've got a bit of list, but the other major ones are how do you start to leverage this data to use it for personalization back in the app? So today we think of data in two ways at Kik. It's data as KPIs, so it's like the things you need to run your business, maybe it's A/B testing results, maybe it's how many active users you had yesterday, that kind of thing. And then the second is data as a product, and how do you provide personalization at an individual level based on your data sciences models back out to the app. So we do that, I should point out at Kik we don't see anybody's messages. We don't read your messages. We don't have access to those. But we have the metadata around the transactions that you have, like most companies do. So that helps us improve our products and services under our privacy policy to say okay, who's building good relationships and who's leaving the platform and why are they doing it. But we can also service components that are useful for personalization, so if you've chatted with three different bots on our platform that's important for us to know if we want to recommend another bot to you. Or you know the classic people people you may know recommendations. We don't do that right now, but behind the scenes we have the kind of information that we could help personalize that experience for you. So those two things are very different. In a lot of companies there's an R and D element, like at Blackberry, the app world recommendation engine was something that there was a team that ran in production but our team was helping those guys tweak and tune their models. So it's the same kind of thing at Kik where we can build, our data scientist are building models for personalization, and then we need to service them back up to the rest of the company. And the process right now of taking the results of our models and then putting them into a real time serving system isn't that clean, and so we do batches every day on things that don't need to be near real-time, so things like predicted gender. If we know your first name, we've downloaded the list of baby names from the U.S. Social Security website and we can say the frequency of the name Pat 80 percent of the time it's a male, and 20 percent it's a female, but Joel is 99 percent of the time it's male and one percent of the time it's a female, so based on your tolerance for whatever you want to use this personalization for we can give you our degrees of confidence on that. That's one example of what we surface rate now in our API back to our own first party components of our app. But in the future with more real-time data coming in from Spark streaming with more real-time model scoring, and then the ability to push that over into some sort of capability that can be surfaced up through an API, it gives our data team the capability of being much more flexible and fast at surfacing things that can provide personalization to the end user, as opposed to what we have now which is all this batch processing and then loading once a day and then knowing that we can't react on the fly. >> So if I were to try and turn that into a sort of a roadmap, a Spark roadmap, it sounds like the process of taking the analysis and doing perhaps even online training to update the models, or just rescoring if you're doing a little slightly less fresh, but then serving it up from a high speed serving layer, that's when you can take data that's coming in from the game and send it back to improve the game in real time. >> Exactly. Yep. >> That's what you're looking for. >> Yeah. >> You and a lot of other people. >> Yeah I think so. >> So how's the event been for you? >> It's been great. There's some really smart people here. It's humbling when you go to some of these sessions and you know, we're fortunate where we try and not have to think about a lot of the details that people are explaining here, but it's really good to understand them and know that there are some smart people that are fixing these problems. As like all events, been some really good sessions, but the networking is amazing, so meeting lots of great people here, and hearing their stories too. >> And you're hoping to go to the hockey game tonight. >> Yeah, I'd love to go to the hockey game. See if we can get through the snow. >> Who are the Bruins playing tonight. >> San Jose. >> Oh, good. >> It could be a good game. >> Yeah, the rivalry. You guys into the hockey game? Alright, good. Alright, Joel, listen, thanks very much for coming on the Cube. Great segment. I really appreciate your insights and sharing. >> Okay, thanks for having me. >> You're welcome. Alright, keep it right there, everybody. George and I will be back right after this short break. This is the Cube. We're live from Spark Summit in Boston.