(upbeat music) >> Announcer: Live from Boston, Massachusetts, it's theCUBE, covering your Red Hat Summit 2019. Brought to you by Red Hat. >> And welcome back to Boston, we continue our coverage here in theCUBE of the Red Hat Summit and welcoming now to theCUBE stage for the first time, I believe, David Gledhill. Who is the group chief information officer and head of group technology and operations at DBS Bank. David, good morning to you. >> Morning, hi, it's great to be here. >> All the way from Singapore, and he was early for the segment this morning. So you get extra points for that, congratulations. >> Put that up to jet lag. (men laughing) >> Thanks for being with us. David, anymore we talk about companies in general, everybody says everybody's a tech company now, right? Not the way it used to be. How is that playing out in your world in financial services as far as how deeply ingrained you have to be with the technology? >> Yeah, so very much, we are. Tech transformation started about 10 years ago. Been CIO of the company about 10 years. And frankly, the first five years were just fixing the basics. So getting in place what we'd call world-class systems. Doing a bunch of stuff on resilience and security and all of that kind of stuff. And the other thing, and this is the dramatic change, you know 10 years ago when I joined the company, we were 85% outsourced to managed service vendors. So I had technology people that basically were signing contractors and managing service agreements. We didn't have technology DNA. Over those five years and a full 10 years actually. We've been doing a lot about just insourcing and rebuilding our technical muscle if you like. So now we're, we've gone from 85% outsourced to 90% insourced. So we run, build and manage our own. We're now a technology company. >> And five years ago, we had a real big shift and we were closest to what was going on in China and so probably saw this before many, many of the other banks saw this around the world. Of what Alibaba was doing with Ant Financial and Tencent and this whole, just complete disruption of how customers interact with the banking industry. So we got an early lead on this digital transformation and really for the last five, six years have been doubling down on building a pure digital offering and we see ourselves as a technology company providing banking services, not as a bank with some technology department in the backend. >> Yeah, I'd love if you can, a little bit, to help us dig into that because, I think back it was okay, what does digitization mean 10 years ago, it was like oh, okay, I need to make sure I have a good website and maybe a good mobile app. Which is a fine starting piece, and also the piece you talked about is when it was outsourced, I'm managing pieces but I sign up for what I need today and when the business needs something, those outsources aren't necessarily tied to them, so you're playing telephone with them. Most the companies I've talked to that have brought skills back inside, it's because the needs of the business are constantly asking for more and it can't be well, it's not part of our contract with what we have. We'll get to that in a year or two. >> Yeah, yeah, so that's a very interesting shift. It plays out in a number of different dimensions. First of all, let me go into that question of business and tech and that separation. When we were managed service, it was literally writing contracts and the specs and handing to vendors. It was just a horrible process. And how can you be a modern technology firm like that? So insourcing, for us, was one big thing to owe those people, but when you insource, then you end up with a business unit and a technology unit. And you still have got silos, so how do you break that? Because that really is a problem. If you look at the way technology companies work, they don't work like that. Five years ago when we said, okay, how do you make that flip to being the digital company? We went very deep into how some of the great technology companies operate. We wanted to understand, what is it? How does that DNA work? How does that culture work? How do they organize themselves? How do they build technology? How do they become agile, speed to market? And so we looked at, we studied Google, Amazon, Netflix Apple, LinkedIn, Facebook and we call them the Gandalf companies. And we said, how can we be more like them? Well, a Gandalf is missing a D. And fortunately at DBS, we happen to have a D. (laughing) So our goal became how do we become the D in Gandalf? And that was just like a lightening rod through the organization because all of the sudden it said to our people, forget about biz and tech and things. You need to think about how these technology comp operates and be more like them. Which means there's no separation, there are no silos we are together building great technical products for our customers so we need to re-think the organization to make sure that happens. >> There's magic. (laughing) You've got a saying, making banking joyful. >> Yep. >> All right, which is not exactly the emotion that I associate with having to deal with my bank. It's fine, but joyful? A very unusual adjective there. What's that all about? And again, at the end of the day, how does technology enhance that? How does that compliment that and really boosted that? >> Yeah, so it was quite a radical moment for us. That came up, we were at a leadership meeting and talking about what is our purpose and how do we. Lots of people talk about customer journey, thinking and stuff like that but how do you bring that to life and one of the execs there said, well what about making banking joyful? And the rest of us just looked at him like he was from a different planet. Saying, are you kidding, what do you mean by that? But as we thought about it more, it has a great meaning and a great purpose to it, that we're not there just to do transactions but we're there to enrich lives, create new businesses, to make customers feel great in their financial stability, in the way they deal with us. And it applies on so many levels. So if you're a bank teller, you understand how to make banking joyful by just that, going the extra mile, in terms of service. If you're in infrastructure, and you're dealing with data centers and servers, you understand that making banking joyful, you must be there all the time. You must have sub-second responses, you must feel great in the customers hands, so it's something that you can apply to all aspects of banking and everybody plays a part in making banking joyful for our customers. >> All right, so David, bring us inside a little bit your organization, you said transforming to a technology company. What's that mean, what technology are you using? We're here at the Red Hat Show. Open-source, not the first thing that people think about when they they think about banking. So how does that fit into the culture that you're building? >> Yeah, yeah, okay, so, this Gandalf thing that I talked about, that's great as a logo and a mindset shift, but it doesn't get you very far. And so what we came up with is five key elements that have to change. And we had to work on to become more like a technology company. And one was a shift from projects to running technology like a series of platforms. That enables you to do agile at scale, but for that you need to organize very differently, which is the third thing. And then the fourth thing is that you need to build for modern systems. The legacy way of building technology just wasn't going to get us to where we needed to be as a technology company. And then the last bit is alternate everything. So, if you want speed to market and agility, you have to alternate. That modern technology stat, it was very obvious to us that the legacy, corporate technologies that we used to build systems, were just not going to win it for us. And so that's our move to open-source. Red Hat was a fabulous partner in that and we used Red Hat extensively throughout our entire infrastructure. And so we went through this rapid modernization of moving to open-source, moving to open-source database we used Merare DD quite extensively, but also, picking up pieces of the open-source from the Gandalf companies. We've seen the way they use open-source to scale. Plus also, to provide just amazing services. So for example, Netflix. We run a bunch of banking platforms on Netflix, believe it or not. It's kind of cool, banking on Netflix is a kinda crazy concept, but we brought that do life. What is is that Netflix we loved? We loved their engineering discipline around chaos engineering and the use of chaos to really build resilient platforms. So in our whole test and deployment framework, we have a lot of Netflix chaos elements built into that to make sure that when we actually are testing, we're testing for chaos and random failures which we inject into those platforms. We don't do it in production like Netflix do quite yet. This whole concept of site reliability and chaos and excellence of service is again something we learned from the Gandalf companies. So Gandalf was not just a, oh yeah, let's pester in the heart of the business article. It was really, let's use their engineering disciplines and design principles to build our own systems. Our network, Facebook, which is, you think of it as a network company. We think of network and the infrastructure layer. And our infrastructure and our networking is designed on a bunch of concepts that Facebook have about how they build their network within their data centers. >> Can you help connect the dots, you talk about a phenomenal technologies, chaos engineering, networking like these global companies. How does that lead to the outcome that you talked about? You know, joy to your end users? >> So if you want to make banking joyful, you have to be super-reliable. You have to be on the edge of the innovation curve all the time. Which means you need to be test and learn, which means you'll be very agile. You need to be able to scale very well and the open-source technologies enable us to scale superbly. You need to be able to to perform as well, superbly well. When I joined the company, our measure of how well our applications were performing is are they up or down? And then we advanced that to, well, are they up and maybe 80% of the time they responded within four seconds. Those are terrible measures because they're not joyful. That means 20% of the time we're awful. That doesn't bring joy to a customer. So what brings joy is we've now scrapped all those things and we're starting to look at performance, for example at the 99th percentile, so anything below that is just noise and the signal is what is our worst performing 99%, because if you wanna be joyful every single time that a customer opens your application you wanna be there and respond well, et cetera, et cetera. Same thing goes for customer science. Where do you get customer drop offs and how do you fix problems? So customer science and the engineering disciplines around observing and instrumenting a platform all the time become very , very important. So it goes very, very deep. You know it's a simple concept. But totally changes the way we engineer. >> Your line of work, or your industry obviously is very security oriented, right? >> Yeah. >> I think of healthcare being another with health information what have you. But certainly financial services, so in the open-source community, how do you address this, I would say it's not a clash by any means, but it's a concern, I would think, still that you have to be micro-observant of security practices and yet this is an open-community and exchange of ideas and could be an exchange of vulnerabilities or problems, too. >> So, sure, and we absolutely do. There are certain things that we, certain places that we won't go, or we will go but only for experimentation reasons because of that question. But you know arguably, we think that open-source company can more secure over time than non-open source, because you're also getting a bunch of people fixing it the whole time. And we've seen some of the issues with some of the open-source and the heart bleed and those other bits and pieces. But they were shut down pretty quickly and found pretty quickly. So we move with caution, we're very cognoscente. We move with our eyes open and it's really the zero day vulnerabilities that we are exposed to. But equally, if you're in a proprietary state. The whole thing that came up with the X86 platform in terms of the vulnerabilities there that apply open-source or not. So yeah, security issues exist everywhere. >> Right, all right, so, David, bring us in. You talked about some of the open-sourced technologies. Where does Red Hat fit into this? What's it like, how have they advanced that journey that DBS has been on? >> So for the heavy lifting, for the big applications that we want to run, and the majority of our workload going with open-source is fine, but you want to have open-source that also you think isn't going to break or have big security vulnerabilities to your question. And that's really where a partner like Red Hat comes in because it gives us access to all the wonderful benefits of open-source with a trusted partner that's putting industrial strength quality releases out that we can really rely on and bank on. So, in awhile we used open-source, at the periphery and true open-source that we just plug it off the internet. The really very, very high demanding workloads and very secure workloads we will always work with a partner that can wrap that into an enterprise-quality offering for us. So Red Hat has gone from zero to running way over 50%, 60% of our workload. And we'll continue to put it even more on that because it's a platform we can trust. >> That's great, so, when you look at your journey overall, how far are you along that journey? Anything when you look out, what are some of the things that are exciting you looking forward? >> So while we believe we're ahead of most banks. There are some that are in the mix, Goldmans are pretty far advance, Duetsche, a couple of the others, Capital One, a few. But it's a sort of rare breed. We're about 80%, 90% done on our transformation journey to get stuff to what we'd call cloud ready or optimized. But we think we're just scratching the surface because if you think about plugging ourselves into customers lives, making banking joyful, our external brand promise for that is live more, bank less. And nobody wakes up in the morning and says, oh, I can't wait to go to my local bank branch and go and do some banking. >> (laughing) I heard Steve talking about it yesterday. >> Actually there is one place. My wife loves going there because we do some great free cookies at DBS. >> John: Oh, excellent. >> But other that my wife, the rest of the planet doesn't really do that. >> John: Fair enough. >> If you want to live more, bank less, it's how do we get the toil of banking away from customers yet embed ourselves in their journeys. And for that we believe that this whole play on ecosystems is very important. So being a creator of an ecosystem or participating so that we take the banking toil out, and yet we inject ourselves, be that leisure or travel or insurance or whatever. And you don't see the bank, the bank is invisible. Then you're live more, bank less. To do that, you need great ecosystems. And we think three things help us to plug into ecosystems. Number one is you have to be able to scale very easily. And all the work we've done on the Gandalf stack means that were no longer afraid of scale, just bring it on. The second thing you need a lot of connectivity, and DBS two years ago, we launched the world's largest banking API platform. We went live with 150 different APIs and 60 live partners at the time. That's now grown to over 350 APIs and 100s of corporates and SME partners that wanna partner with us to pug us into their offering. So the more we do that, the more we disappear and let people live more, bank less. >> Well, next time, if you wanna bring some cookies with you, by all means, okay. (David laughs) We're always up for that, David, thanks for the time. >> Sure. >> We appreciate that and good luck on the mission and the journey there at DBS. >> Sure, thanks very much and great to be here, thank you. >> David Gledhill joining us this morning here, as we continue our coverage of the Red Hat Summit. We're in Boston. You're watching theCUBE. (upbeat music)

>> Announcer: Live From Las Vegas, it's theCUBE. Covering AWS re:Invent 2017. Presented by AWS, Intel, and our ecosystem of partners. >> Hey, welcome back to theCUBE. We are live on day three of our continuing coverage AWS re:Invent 2017. We've had an amazing three days, lots of great guests, lots of great conversations. I am Lisa Martin with my co-host Keith Townsend, and we're very excited to be joined by two guests new to theCUBE, please help us welcome Adam Burden, the Senior Managing Director of Advanced Technology and Architecture at Accenture, welcome to theCUBE Adam. >> Thank you so much. >> Lisa: And Sandra Stonham, one of the Managing Directors of Technology and Operations at DBS Bank, welcome-- >> Thank you. >> All the way from Singapore. >> Thank you, yes. >> Great to have you guys here. So we hear great things about there is a remarkable story that DBS has, that started last year when you guys attended the AWS re:Invent 2016. Talk to us about what you discovered last year and how this has facilitated your journey to cloud, your transition. >> So if I may, maybe I'll start just a little bit before that, because actually we had been playing with AWS before that. We actually have a huge transformation, transformation strategy that takes us towards cloud. So we've actually been using AWS for infrastructure as a service just scaling. We were putting our trading system grid on to that, so, that was our initial exposure, and then what happened to AWS last year is that I came to re:Invent and I saw all these rich, rich services that AWS were providing and I thought, we actually can't afford not to be building on this. So, from then, I went back, and with my organization I basically said, look, we need to be building natively on AWS, and so that's what we've been doing. We invited Accenture to come and help us because they actually have more experience of building natively on AWS, taking advantages of those services, and we invited them in, they've been working together with us, and we've got now native AWS applications using serverless. >> So Sandra, talk to a little bit more about that process, because, politically it's tough to move something as very consistent, very stable, as a bank, to digital transformation, and then even select not just a partner to help that transition, but AWS. How was that first set of conversations when you got back from re:Invent 2016, excited about the transformation opportunities, what were some of the internal discussions? >> Well as I said, we actually had a whole technology transformation strategy underway at that point, so we'd actually really looked deeply at ourselves and we looked also at the tech giants that are out there, and we'd created this whole technology transformation strategy that basically meant that we needed to go completely cloud native. Cloud native infrastructure, cloud native applications, complete automation on everything, and a very agile, agile and fast moving business environment as well to work with us. So we actually had that whole strategy in place and that was all underway, and we starting to work with AWS, so this was actually just an extension of that strategy. >> Tell me a little bit about this digital transformation strategy, I'm sure a lot of others would love to learn from what you guys are doing. What were the top three business goals that this transformation strategy needs to drive? >> What we did, actually maybe I'll tell you a bit about it. We call it Gandalf, and the reason we call it Gandalf is we actually took a look at our, all the tech giants, and we said, how can we DBS be standing tall among the tech giants. So the tech giants that we looked at were Google, Amazon, Netflix, Apple, LinkedIn, Facebook. And we said, how can we be the D in Gandalf. That kinda became our sort of code name and our galvanizing strategy. To help people understand what that really meant internally, we actually came up with five key themes, and we put them in a wheel, and we've got kinda five cheeses in the wheel if you like. Three of them are really about the organization and the culture, so one of them is organize for success, one of them is to change from project to platform, one of them is high performing agile team. So those are kinda the three organization and culture focus. Then we have two that are very specific to technology. One of them is design for modern systems, and the second one is automate everything. On each of these five themes we then have a whole load of sub-themes and that give people a little bit more idea of what they can do, and that strategy we've found has been very galvanizing for the whole organization so that everybody in the organization, they know if they are aligning to the strategy because they're doing one or more of these themes or sub-themes. >> So Adam, this is kind of the perfect customer. They already gone through a transformation, you don't have to have the conversation, cloud isn't a technology, it's more of a business process. What was it like engaging DBS for the first time in their transformation? >> Well the good news is, I was actually with Sandra at the re:Invent Conference last year, so I saw some of the light bulbs go on during that process. We engaged with DBS, they've been a client of Accenture for many many years, and we're delighted to have an opportunity obviously to work with them. Going in and having these discussions though, and helping them identify the right workloads to move to serverless technology, is something that we've done a lot for other clients. We move workloads all the time to AWS, and there's lots of different techniques to do that. You can just lift and shift, you can move things into containers and move them, but for the right workloads, you can get truly break through results, benefits and value release, by moving them to serverless. That's what we're able to identify for them and we worked through a process to do exactly that with that experience. It was actually very pleasant because we'd had an opportunity to see that process from the very beginning and I think that the inspiration at the end of that, that we've created about the value that can be generated is going to help to really drive even further adoption of cloud and other serverless technologies at DBS as a result. >> One of the things that I love that you were talking about is the cultural transformation. How long has DBS been in business? >> Sandra: Since the 1960's. >> Quite a long time, so the strategy that you laid out, I love also, not just the cultural transformation, those are hard and so challenging, but also the fact that as a bank, you want to be like one of those big tech leaders, and I think that's gonna be incredibly inspirational for people to hear your story that even in terms of adapting the culture, but even attracting talent that you have such big aspirations. How did you establish the strategy? What were some of the cultural elements that you have successfully changed and how quickly were people able to get on board with this? >> It's a very long journey and there are many different facets to that journey. We have a CEO who's very driven to be digital to the core. He's very visionary and has really sorta set targets for us as an organization. Embracing digital, embedding ourselves in customer journeys, driving for joyful customer journeys, making banking joyful is one of our missions. So he really set some of these strategies even challenging us as well to be a data driven company because we feel that's very much the future. We have a CEO who's really set many of these strategies out there, but even so, to make it happen in the organization is difficult. The agile teams is one aspect, where we've really been looking at what does it mean to be agile and sometimes you can be tech agile but not business agile, and so what does it really mean to be business led agile? So that's a long learning journey we're still on it. But we're getting some successes and so now that helps to start get other people on board. We also look at innovation, so we have an innovation officer and he feels that his job, his job of himself and his team is not to produce product, but to actually change the culture of the organization so that we look like a 22,000 person start up. He tries to, on many many different things, whether he's bringing in speakers, or whether he's out working with us to align to start ups and work with start ups so that we can really get exposure to how start ups work. Many many different aspects of what he does to just encourage innovation among everybody, right from the senior leadership down. So many different aspects of the cultural transformation. Another area is one we're grappling with at the moment, is how we do funding. When we want to move from projects to platforms, how do you take away that big cumbersome way of working where you fund these big initiatives and you have to wait for a long time to get any output and how do you move that more to a sort of iterative evolution of a platform that the business really owns and champions. All of these things, it actually crosses all aspects of the organization and I think you have to do all of them. You have to take every facet and work on it, and move it forward. >> So Adam, large company like DBS comes to you with these big aspirational goals, become a platform, from a technologist perspective, architect, that's exciting to hear. However, baby steps and chunks. >> Right. >> What were some of the first steps that you guys took after identifying opportunity and workload, what was some of the first technologies you engage AWS with? >> Well, Accenture, well first of all, I should probably explain that I'm a customer of DBS as well, they're my bank in Singapore, so I care very deeply about making sure that the work we are doing, even more so than Accenture would normally. (laughing) The things that we do to help a client get started on a journey like this, first of all, helping to identify the applications. A lot of times, one of the very first things that we do is we look at different patterns. Almost like a sewing pattern that you would follow and be able to repeat over and over again, different patterns for how workloads should actually move. We use those as ways that developers can kind of follow a recipe book almost, so that in the future as they're moving new workloads or they're building new services, that they do it in a very similar style and technique. Those initial steps, those processes, kinda set the tone for how the migration process will go, and you can really expand from there. If you try to do too much at once, without really getting a nucleus of it right, you'll have a lot of varying standards and it'll be much harder for you to be able to make the kind of progress that you want. So we typically try to start with a really good marquee, couple of projects, get those going really really well, save those patterns and then expand upon them as more and more workloads actually move. That's one of the key elements of success we find early on. >> Well Adam, as you engage with customers, and you're coming to a show like this, it's great that a customer gets really excited about the business opportunity, but working internal IT for long time, exposing just a little bit of the capability of AWS is both good and bad, because now you've exposed AWS and developers want the whole thing. They'll look at something like Sage, SAS Master I think it is, is the AI solution from yesterday. >> Or Recognition. >> Yeah. >> Yeah, yeah, yeah. >> I want that today. But you have to be able to roll it out in a controlled fashion. How do you guys handle governance once you've embraced a opportunity and the relationship with a company like AWS? >> Well I can speak about, why don't Sandra, why don't you talk about that from DBS's perspective and then I'm gonna give you Accenture's as well. >> So no doubt about it, it's challenging. But governance is changing, regardless of whether you're looking at cloud internally or cloud externally, governance is changing. Now the whole focus is to give developers self-service access to everything they wanted. Everything they want to be able to do, so they can deploy, they can run tests, they can do all of things themselves. So that applies whether you're looking at private cloud or whether you're looking at public cloud. Now obviously in public cloud, all of those controls that you have internally, not only they need to change for the new world, but they also now need to translate, if you like, into public cloud. So things don't just necessarily, you can't just necessarily move them and apply the same things to public cloud as you do to private cloud. You have to go and reinvent them in public cloud. AWS is good in that they give you all the tools to do it, but the tools are not already set up, so you do have to learn about it and you do have to build slowly over time. That's why we started with simple things like infrastructure as a service which we can just scale up and down and now are moving to the more complex which is using the native services, which obviously need more governance around them and contain more data. So it's a learning process, but basically if you've got a great organization internally that really understands what it is you're trying to control, then you need to be able to translate that and see how that applies to AWS. >> One of the things that interests me Adam, is what you talked about with the recipes. Recipes, the consistency, how important was that for DBS Sandra, in terms of, alright they've got some prescriptions here on how we can be successful, talked about governance, the steps to take, so that like Keith was saying, you get exposed to all these things, you gotta kind of control everybody. But talk to us about the recipes and this kinda playbook for success, and what that means to DBS to be able to do things in a streamlined fashion and be successful. >> That was the real reason that we brought Accenture on board is because they've actually looked at applications before, in house applications that we've, that the people have built, and then they've looked at what would that look like if you were to rebuild that from scratch on AWS using native services. So they were able to work with us and work through difficulties with us to actually transpose those applications onto an AWS native format. That was actually very helpful, and that's been our learning. So the team that's been working together with Accenture has now learned, we've taken other applications from there and we're now looking at just starting directly building natively on AWS based on what we've learned. It's very valuable and I would say expedited our journey. >> Excellent. >> So let's talk about some of those newer services. Infrastructure as a Service, we can do what we do in our data center today in AWS much faster, there's instant value there, but as we start to expand out and look at something like serverless, how is DBS and Accenture in general looked at something like serverless and taken advantage of lamda? >> I'll tackle that one first maybe. Serverless technology for Accenture has been something that has really allowed our clients to move from looking at cloud as a data center to looking at cloud as a platform. It's an epiphany actually for many of our customers where they look at, well, absolutely, we can move our workloads into cloud, well maybe we'll get a lower operating cost, maybe we'll get some other benefits of being there, but now I can begin to actually, in serverless and other techniques, I can take advantage of the native services there to actually operate at a far lower cost and enrich it with new capabilities. Think about adding text to speech capabilities from Polly, think about adding image recognition facilities. Think about the other capabilities that you can now have because you're on a cloud platform that you wouldn't have if all you were looking at it was as simply another data center. That is the light bulb that goes on, and why I think serverless from a breakthrough standpoint, about the cost structure, the granularity of how things are metered and actually priced. But then the richness of features that are available, you're inventing your future there. It's available at your fingertips. You do have to control the governance, you do have to make sure that you're, you've got some guardrails around that, but the developers will be incredibly creative with those services and you will have new features that'll delight your business users and your clients much faster than you'd ever been able to in the past. >> I love that, ignite your future. I wish we had more time, because I wanted to ask you both about what you're excited about that was released and Adam got this great grin on his face, but unfortunately we are out of time. We wanna thank you both Adam and Sandra for joining us and sharing what you guys are doing. Sounds like the light bulbs are going off, continuously burning, and we look forward to hearing more of your great successes. >> Great. >> My pleasure. Thank you so much. >> Thank you very much. >> Thank you. >> Thank you. >> And for my co-host Keith Townsend, I'm Lisa Martin, you're watching theCUBE live from AWS re:Invent 2017. Stick around, day three of coverage, we've got more great stories coming back.

>>Well, hello everybody at John Wallace here on the Cube, and glad to have you along here for day two of our coverage here at AWS Reinvent 22. We're up in the global startup program, which is part of AWS's Startup Showcase, and I've got Kevin Farley with me. He is the director of Strategic Alliances with Maria Day db. And Kevin, good to see you this morning. Good to see you, John. Thanks for joining us. Thank >>You. >>Appreciate it. Yeah. First off, tell us about Maria db. Sure. Obviously data's your thing. Yep. But to share that with some folks at home who might not be familiar with your offering. >>Yeah. So Maria DB's been around as a corporate entity for 10 plus years, and we have a massive customer base. You know, there's a billion downloads from Docker Hub, 75% of the Fortune 500. We have an enormous sea of really happy users. But what we realize is that all of these users are really thinking about what do we, what does it mean to transform it? What does cloud modernization mean? And how do we build a strategy on something we really love to drive it into the cloud and take it to the future. So what we launched about two years ago, two and a half years ago, is Skye. It's our database as a service. It leverages all the best elements, what we provide on the enterprise platform. It marries to the AWS cloud, and it really provides the best of both worlds for our >>Customers. So in your thought then, what, what problem is that solving? >>I think what you see in the overall database market is that many people have been using what we would call legacy technology. There's been lots of sort of stratification and mixes of different database solutions. All of them come with some promise, and all of 'em come with a lot of compromise. So I think what the market is really looking for is something that can take what they know and love, can bring it to the cloud and can survive the port drive the performance and scale. That completely changes the landscape, especially as you think about what modern data needs look like, right? What people did 10 years ago with the exponential scale of data no longer works. And what they need is something that not only can really deliver against their core business values and their core business deliverables, but gets 'em to the future. How do we drive something new? How do we innovate? How do we change the game? And I think what we built with AWS really delivers what we call cloud scale. It's taking something that is the best technology, and I as a V can build, marrying it to, you know, Kubernetes layer, marrying it to global availability, thinking about having true global high availability across all of your environments and really delivering that to customers through an integrated partnership. >>Could we see this coming? I mean, because you know data, right? I mean, yeah, we, we, everybody talked about the tsunami of growth, you know, >>Back 10 >>Sure. 11 years ago. But, but maybe the headlights didn't go far enough or, or, but, but you could see that there was going to be crunch time. >>There's no doubt. And I think that this has been a, there's, there's been these sort of pocket solutions, right? So if you think at the entire no sequel world, right? People said, oh, I need scale, I can get it, but what do I have to give up asset compliance? So I have to change the way I think about what data is and how I, I can govern it. So there's been these things that deliver on half the promise, but there's never been something that comes together and really drives what we deliver through CIQ is something called expand. So distributed SQL really tied to the SQL Query language, having that asset data. So having everything you need without the compromise built on the cloud allows you to scale out and allows you to think about, I can actually do exponential layers of, of data, data modeling, data querying, complete read, write, driving that forward. And I think it gives us a whole nother dynamic that we can deliver on in a way that hasn't been before. And I think that's kind of the holy grail of what people are looking for is how am I building modern applications and how do I have a database in the cloud that's really gonna support >>It? You know, you talk about distributed, you know, sequel and, and I mean, there's a little mystery behind it, isn't there? Or at least maybe not mystery. There's a little, I guess, confusion or, or just misunderstanding. I mean, I, how, nail that down a little bit. I >>Would say the best way to say it, honestly, this is the great thing, is it people believe it's too good to be true. And I think what we see over and over >>Again, you know, what they say about that. >>But this is the great part is, you know, you know, we've just had two taste studies recently with aws, with HIT labs and Certified power, both on expand, both proof in the pudding. They did the POCs, they're like, oh my God, this works. If you watch the keynote yesterday, you know, Adam had a slide that was, you know, as big as the entire room and it highlighted Samsung and they said, you know, we're doing 80,000 requests per second. So the, you know, the story there is that AWS is able as, as an entity with their scale and their breadth to handle that kind of workload. But guess what that is? That's MariaDB expand underneath there driving all of that utilization. So it's already there, it's already married, it's already in the cloud, and now we're taking it to a completely different level with a fully managed database solution. Right? >>How impressive is that? Right? I mean, you would think that somebody out there who, I mean that that volume, that kind of capacity is, is mind blowing. >>I mean, to your kind of previous point, it's like one of those things, do I see what's coming and it's here, right? You know, it's, is it actually ever gonna be possible? And now we're showing that it really is on a daily basis for some of the biggest brands in the world. We're also seeing companies moving off not only transitioning from, you know, MariaDB or myse, but all of the big licensed, you know, conversions as well. So you think about Oracle DBS Bank is one of our biggest customers, one of the largest Oracle conversions in the world onto MariaDB. And now thinking about what is the promise of connecting that to the cloud? How do you take things that you're currently doing, OnPrem delivering a hybrid model that also then starts to say, Hey, here's my path to cloud modernization. Skye gives me that bridge. And then you take it one layer farther and you think about multi-cloud, right? That's one of the things that's critical that ISVs can really only deliver in a meaningful way, is how can we have a solution for a customer that we can take to any availability zone. We can have performance, proximity, cost, proximity. We're always able to have that total data dexterity across any environment we need and we can build on that for the future. >>So if, if we're talking about cloud database and there's so many good things going forward here. You're talking about easy use and scalability and all that. But as with ever have you talked about this, there's some push and there's some pull. Yeah. So, so what's the, what's the other side that's still, you know, you that you think has to be >>Addressed? And I think that's a great question. So there's, we see that there's poll, right? We've seen these deals, this pipeline growth, this, there's great adoption. But what I think we're still not at the point of massive hockey stick adoption is that customers still don't fully understand the capabilities distributed SQL and the power they can actually deliver. So the more we drive case studies, the more we drive POCs, the more we prove the model, I think you're gonna see just a massive adoption scale. And I also think customers are tired of doing lots of different things in lots of different pockets. So neither one of the key elements of Sky SQL is we can do both transactional and analytical data out of the same database driven by the same proxy. So what, instead of having DBAs and developers try to figure out, okay, I'm gonna pull from this database here. >>Yeah. That there, it's, it's this big spaghetti wire concept that is super expensive and super time intensive. So the ability to write modern applications and pull data from both pockets and really be able to have that as a seamless entity and deliver that to customers is massive. I mean, another part of the keynote yesterday was a new deliverable, like kind of no etl. Adam talked about Aurora and Redshift and the massive complexity of what used to exist for getting data back and forth. You also have to pay for two different databases. It's super expensive. So I think the idea that you can take the real focus of AWS and US is customer value. How do you deliver that next thing that changes the game? Always utilizes AWS delivers on that promise, but then takes a net new technology that really starts to think about how do we bring things together? How do we make it more simple? How do we make it more powerful? And how do we deliver more customer value as we go forward? >>But you know, if, if I'm, I'm still an on-prim guy, just pretend I'm not saying I am. Just pretend I just for the sake of the discussion here, it's like I just can't let it go. Yeah. Right. I, I still, you know, there's control, there's the known versus the unknown. The uncertain. Yeah. So twist my arm just a little bit more and get me over the hum. >>Well, first of all, you don't have to, right? And there's gonna be some industries and some verticals that will always have elements of their business that will be OnPrem. Guess what? We make the best based in the world. It can be MariaDB, but there's those that then say, these, these elements of our business are gonna be far more effective moving to the cloud. So we give you Skye, there's a natural symbiotic bridge between everything we do and how we deliver it. Where you can be hybrid and it's great. You can adopt the cloud as your business needs grow. And you can have multi-cloud. This is that, that idea that you can, can have your cake and eat it too, right? You can literally have all these elements of your business met without these big pressure to say, you gotta throw that away. You gotta move to this. It's really, how do you kind of gracefully adopt the cloud in a way that makes sense for your business? Where are you trying to drive your business? Is it time to value, right? Is it governance? Is it is there's different elements of what matters the most to individual businesses. You know, we wanna address those and we can address >>Those. So you're saying you don't have to dive >>In, you don't have to dive >>In. You, you can, you can go ankle deep, knee deep, whatever you wanna >>Do. Absolutely. And you know, some of the largest MariaDB users still have massive, massive on-prem implementations. And that's okay. But there's elements that are starting to fall behind. There's cost savings, there's things that they need to do in the cloud that they can't do. OnPrem. And that's where expand Skye really says, okay, here is your platform. Grow as you want to, migrate as you want to. And we're there every step along the way. We, we also provide a whole Sky DBA team. Some guys just say, I wanna get outta the database world at all. This is, this is expensive, it's costly and it's difficult to be an expert. So you can bring in our DBA team and they'll man and run, they'll, they'll run your entire environment. They'll optimize it, you know, they'll troubleshoot it, they'll bug fix, they'll do everything for you. So you can just say, I just wanna focus on building phenomenal applications for my customers. And the database game as we knew it is not something that I know I want to invest in anymore. Right. I wanna make that transition >>That makes that really, yeah. You know, I mean really attractive to a lot of people because you are, you talk about a lot of headache there. Yeah. So let's talk about AWS before Sure. I let you go just about that relationship. Okay. You've talked about the platform that it provides you and, and obviously the benefits, but just talk about how you've worked with AWS over the years Yep. And, and how you see that relationship allowing you to expand your services, no pun intended. >>For sure. So, I mean, I would start with the way we even contemplated architecture. You know, we worked with the satisfactory team. We made sure that the things that we built were optimized in their environment. You know, I think it was a lot of collaboration on how does this combined entity really make the most value for our customers? How does it make the most sense for our developers as we build it out? Then we work in the, in the global startup team. So the strategic element of who we are, not all startups are created equal, right? We have, right, we have 75% of the Fortune 100, we've got over a billion downloads. So, you know, we come in with promise. And the reason this partnership is so valuable and the reason there's so much investment going forward is cuz what really, what do the cloud guys care about? >>The very, very most, they want all of these mission critical, big workloads that are on prem to land in their cloud. What do we have a massive, massive TAM sitting out there, these customers that could go to aws. So we both see, like if we can deliver incredible value to that customer base, these big workloads will end up in aws. They'll use other AWS services. And as we scale and grow, you know, we have that platform that's already built for it. So I think that when you go back to like the tenants, the core principles of aws, the one that always stands out, the one that we always kind of lean back on is, are we delivering customer value? Is this the best thing for the customer? Because we do have some competition just like many other, other partners do, right? So there is Aurora and there is rds and there is times when that's a great service for a customer. But when people are really thinking about where do I need my database to go? Where do I really need to be set for the future growth? Where am I gonna get the kind of ROI I need going forward? That's where you can go, Hey, sky sql, expand distributed sql. This is the best game in town. It's built on aws and collectively, you know, we're gonna present that to a customer. I'm >>Sold. Done. >>I love it. Right? >>Maria db, check 'em out, they're on the show floor. Great traffic. I know at at the, at the booth. They're here at AWS Reinvent. So check 'em out. Maria db. Thanks >>Kevin. Hey, thanks John. Appreciate your >>Time. Appreciate Great. That was great. Right back with more, you're watching the cube, the leader in high tech coverage.

>>Good morning from New York city, Lisa Martin and John furrier with the cube. We are at AWS summit NYC. This is a series of summits this year, about 15 summit globally. And we're excited to be here, John, with about 10,000 folks. >>It's crowded. New York is packed big showing here at 80 of us summit. So it's super exciting, >>Super exciting. Just a little bit before the keynote. And we have our first guest, Kevin Farley joins us the director of strategic alliances at Maria DB. Kevin, welcome to >>The program. Thank you very much. Appreciate you guys having us. >>So all of us out from California to NYC. Yeah, lots of eyes. We got keynote with Warner Vogels coming up. We should be some good news, hopefully. Yep. But talk to us about Maria DB Skys cloud native version released a couple years ago. What's going on? >>Yeah, well, it's, you know, Skys SQL for us is really a be on the future. I think when we think about like the company's real mission is it's just creating a database for everyone. It's it's any cloud, any scale, um, any size of performance and really making sure that we're able to deliver on something that really kind of takes advantage of everything we've done in the market to date. If you think about it, there's not very many startups that have a billion downloads and 75% of the fortune 500 already using our service. So what we're really thinking about is how do we bridge that gap? How do we create a natural path for all of these customers? And if you think about not just Maria DB, but anyone else using the sequel query language, all the, my people, what I think most Andy jazzy TK, anyone says, you know, it's about 10% of the market currently is in the clouds. That's 90% of a total addressable market that hasn't done it yet. So creating cloud modernization for us, I think is just a huge opportunity. Do >>You guys have a great history with AWS? I want to just step back, you mentioned some stats on, on success. Can you scope the size and track record of Maria DB for us real quick and set the table? Because I think there's a bigger picture going on that we've been tracking for the past 13 years we address is the role of the database has always been one of those things where they didn't believe a one database fits all things, right. You guys have been part of that track record scope, the size and scale of Maria DB, the usage, the use cases and some of the successes. >>Yeah. I mean, like I said, some of the stats are already threw out there. So, you know, it is pervasive, I think is the best way to put it. I think what you look at what the database market really became is very siloed. Right? I think there was a lot of unique solutions that were built and delivered that had promise, but they also had compromise. And I think once you look at the landscape of a lot of fortune 500 companies, they have probably 10 to 15 different database solutions, right? And they're all doing unique things. They're difficult to manage. They're very costly. So what Marie DB is always kind of focused on is how do we continue to build more and more functionality into the database itself and allow that to be a single source of truth where application developers can seamlessly integrate applications. >>So then the theme of this event in New York city, which is scale dot, dot, dot, anything must align quite well with Maria and your >>Objectives. I mean, honestly, I think when I think of the problems that most database, um, companies, um, face customers, I should say it, it really comes down to performance and scale. Most of them like Maria DB, like you said, they it's like the car, you know, and love you've been driving it for years. You're an expert at it. It works great, but it doesn't have enough range. It doesn't go fast enough. It's hitting walls. That modern data requirements are just breaking. So scale for me is the favorite thing to talk about because what we launched as MariaDB expand, which is a plugable storage engine that is integrated into Skye, and it really gives you dynamic scale. So you can scale in, you can scale out, it's not costly compute to try to get for seasonality. So you can make your black Friday numbers. It's really about the dexterity to be able to come in and out as you need in a share, nothing architecture with full failover sale healing, high availability, married to the cloud for full cloud scale. And that's really the beauty of the AWS partnership. >>Can you elaborate a bit more on the partnership? How long have you guys been partners? Where is it now anything exciting coming out? >>Yeah, it it's, it's actually been a wonderful ride. They've really invested from the very beginning we went for the satisfactory. So they really brought a lot of resources to bear. And I think if you're looking at why it works, um, it's probably two things. I think the number one thing is that we share one of the core tenants and it's customer obsession in a, in a, in an environment where there is co-opetition right. You have to find paths for how do you get the best thing for the customer? And the second is pretty obvious, but if you look at any major cloud, their number one priority is getting large mission critical workloads into their cloud because the revenue is exponential on the backside. So what do we own? Large mission critical workloads. So if you marry that objective with AWS, the partnership is absolutely perfect for driving true revenue, growth scale, and, and revenue across, across both entities in the partner ecosystem. >>So Kevin talk about the, um, the hybrid strategy, cuz you're seeing cloud operations. Yep. Go hybrid. Amazon announced AWS announced outpost like four years ago. Right now edge is super hot. Yeah. So you're seeing like most of the enterprise is saying mm-hmm <affirmative> okay. Love cloud love the cloud database, but I got the on-prem hybrid cloud operations. Right. So it's not just proprietary operations. It's cloud ops. Yeah. How do you guys fit into that? What's the story. >>We, we actually it's. I mean, there's, there's all these new deliverables outposts, you know, come out with a promise. What we have is a reality right now, um, one of the largest, um, networking companies, which I can't mention yet publicly, um, we want a really big sky SQL deal, but what they had manufacturing plants, they needed to have on-prem deployments. So Maria DB naturally syncs with sky SQL. It's the same technology. It works in perfect harmony. So we really already deliver on the promise of hybrid, but of course there's a lot more we can grow in that area. And certainly thinking about app posts and other solutions, um, is definitely on the, the longer term roadmap of what could make sense for in our customer. What, >>What are some of the latest things that, that you guys are doing now that you weren't doing a few years ago that customers should know about the audience should know about? >>I mean, I think the game changer, we're always innovating. I mean, when you're the company that writes the code owns the code, you know, we can do hot fixes, we can do security patches, we can always do the things that give you real time access to what you need. But I think the game changer is what I mentioned a little bit earlier. And I think it's really the, the holy grail of the cloud. It's like, how can we take the, the SQL query language, which is well over 50% of the open source market. Right. And how do we convert that seamlessly into the cloud? How do we help you modernize on that journey? And expand gives you the ability to say, I can be the small, I can be a small startup. I got my C round. I don't wanna manage databases. I can use the exact same service as the largest fortune 100 company that has massive global scale and needs to be able to drive that across globe. Yeah. So I think that's the beauty is that it's really a democratization of the database, >>At least that, you know, we've been covering the big data space for 10 years. Remember all those different conversations had do those days and oh, they have big data and right. But then it's like too hard to set up. Then you had that kind of period where you saw a spark and data lakes emerge. Yeah. Then you, now it almost seems, seems like now more than ever, there's a data revolutions back. Right. It was almost like a lull in the, in, in the, in the market a little bit. Yeah. I'm gonna democratize data science right now. You got data. So now it just seems to be an explosion at that level. What's your analysis on that? Because you you've been in, in, in the weeds and in the, in the, in this market for 10 years. Yeah. And nothing really changed. It's just now it's more ready. Yeah. I think what's your observation. Why >>Is that? I think that's a really good question. And I love it cuz I mean, what the promise of things like could do and net new technologies sort of, it was always out there, but it required this whole net new lift and how do I do it? How do I manage it? How do I optimize it? The beauty of what we can do with Maria DB is that sky SQLs, which you already know and love. Right? And now we can Del you can deliver a data lake on S3, right? You can pull that data. And we also have the ability to do both analytical data and transactional data from the same database. So you can write applications that can pull column, store data up into, um, your application, but you can also have all of your asset transactions, which are absolutely required for all of your mission critical business. So I think that we're seeing more and more adoption. You've seen other companies start to talk about bringing the different elements in, but we're the only ones that really >>Do it and SQL standardizing that front end. Yeah. Even better than ever before. All the stuff under the covers is all being connected. >>That's the awesome part is right. Is you're literally doing what you already know how to do, but you blow it out on the back end, married to the cloud. And that I think is the real revolution of what makes usability real in the data space. And I think that's what was always the problem before >>When you're in partner conversations, you mentioned co-opetition. Yeah. <laugh> so I think when you're in partner conversations and customer conversations, there is a lot of the, the there's a lot of competition out there. Absolutely. Everyone's got their own key messages. What are the key differentiators that you're saying AWS Marie to be together better? And here's why, >>Yeah. I, I think that certainly you, you start with the global footprint of AWS, right? So what we rely on the most is having the ability to truly deal with global customers in availability zones, they're gonna optimize performance from them. But then when we look at what we do that really changes the game, it comes down to scale and performance. We actually just ran, um, a suspense test against cockroach that also does distributed sequel. Absolutely. You know, the results were off the chart. So we went public and said, we have an open challenge. Anyone that wants to try to beat, um, expand and Skye will we'll if you can, we'll put $25,000 towards charity. So we really are putting our money where our mouth is on that challenge. So we believe the performance cuz we've seen it and we know it's real, but then it's really always about data scale. Modern data requirements are breaking the mold of charting. They're breaking the mold of all these bandaids that people have put in these traditional services. And we give them future. We, we feature proof their investments, so they can say, Hey, I can start here. But if I end up being a startup that becomes Airbnb, I'm already built to blow it out on the back end. I can already use what I have. >>Speaking of startups, being the next Airbnb. If you look at behind us here, you can see, this is a really packed event in New York city events are back, but the ecosystem here is even flourishing. So Dave and I and Lisa were observing that we're still kind of in a growth mode, big time. So yeah, there's some market forces headwinds for the big unicorns, overfunded, you know, public companies, maybe the valuations are a little bit off, but there's still a surge of new innovations, new companies coming out of this. Um, and it's all around data and scale. It's all around new names. We've never heard of. Absolutely. What's your take on >>Reaction? Well, actually another awesome segues cuz in addition to the public clouds, I manage the ecosystem. And one of the things that we've really been focused on with Skys SQL is making it accessible API accessible. So if you're a company that has a huge Marine DB footprint change data capture might be the most important thing for you to say, we wanna do this, but we want you to stay in sync with our environments. Um, things like monitoring, things like BI, all of these are ecosystem plays and current partners that we have, um, that we really think about how do you holistically look at not only the database and what it can do, but how does it deliver value to different segments of your customer base or just your employee base that are using that stuff? So I think that's huge for us. >>Well, you know, one of the things that we talk often about is that every company, these days, regardless of industry, has to be a data company. Yep. You've gotta be able to access the data glean insights from an act on it quickly, whether it's manufacturing, retail, healthcare, are there any verticals in where Maria DB really excels? >>Um, so certainly we Excel in areas like financial services is huge DBS bank. Um, in APAC, one of our biggest customers, also one of the largest Oracle migrations, probably the, that we've ever done. A lot of people trying to get off Oracle, we make it seamless to get into Maria DB. Um, you can think about Samsung cloud and another, their entire consumer cloud is built on Maria DB, why it's integrated with expand right seasonality. So there's customers like that that really bring it home for us as far as ServiceNow tech sector. Right? So these are all different ones, but I think we're really strong in those >>Areas. So this brings up a good point. Dave and I a coined a term called super cloud at reinvent and Lisa and Dave were at multiple events we're together at events. And so a lot of people are getting behind this cuz it's multi-cloud sounds like something's broken. Yes. But so we call it super cloud because customers are building on top of ecosystems like Maria DB and others. Yeah. Not just AWS SOS does all the CapEx absolutely provide the value. So now people are having this new super cloud moment. We' saying we can get all the benefits of cloud scale mm-hmm <affirmative> without actually being a cloud. Right. So this is where the next gen layer comes. What's your reaction to, to super cloud. Do you think it's a thing? >>Well, I think it's a thing in the sense, from our perspective as an ISV, we're, we're laser focused on making sure that we support any cloud and we have a truly multicloud cloud platform. But the beauty of that as well is from a single UI, you're able to deploy databases in different clouds underneath that you're not looking at so you can have performance proximity, but you're still driving it through the same Skys UI. So for us it's, it's unequivocally true. Got it. And I think it's only ISVs like Maria DB that can deliver on that value because >>You're enabling, >>We're enabling it. Right. We partner, we build on top of everything. Right. So we can access everything underneath >>And they can then build on top of you. >>Sure, exactly. And that's exactly where it goes. Right? Yeah. So that, I think in that sense, the super cloud is actually already somewhat real. >>It's interesting. You look at the old, it spend, you take a big company. I won't say a name, but a leader in a, a vertical, they have such a big spend. Now they can leverage that spend in with the super cloud model. They then could become a service provider in the vertical. Absolutely capital one S doing it. Yeah. You're seeing, um, Goldman Sachs doing it. They have the power on the spend that they're leveraging in for their business and servicing their vertical and the smaller players. Do you see that trend? >>Well, I think that's the reality is that everyone is getting this place where if you're talking about sort of this broader super concept, you're talking about global scale, right? That's if in order to deliver a backbone that can service that model, you have to have the right data structure and the right database footprint to be able to scale. And I think that's what they all need to be able to do. And that's what we're really well positioned with Skys >>To enable companies, as we talked about a minute ago to truly become data companies. Yeah. And to be competitive and to scale on their own, where are your customer conversations? Are they at the C-suite level? Has that changed in the last couple of years? >>Uh, that's actually a really great way to state that question because I think you would've traditionally probably talked more to, um, the DBAs, right? They're the people that are having headaches. They're having problems. They're, they're trying to solve. We see a lot of developers now tons, right? They're thinking about, I have this, I have this new thing that I need to do to deliver this new application. And here's the requirements and the current model's broken. It doesn't optimize that it's a lot of work and it's hard to manage. So I think that we're in a great position to be able to take that to that next phase and deliver. And then of course, as you get deeper in with AWS, you're talking about, you know, CIO level, CISO level, they're they need to understand how do you fit into our larger paradigm. And many of these guys have, you know, hundreds of million dollar commits with AWS. So they think of their investment in the sense of the cloud stack. And we're part of that cloud stack, just like AWS services. So those conversations continue to happen certainly with our larger customers, cuz it truly is married. >>It is. And they continue to evolve. Kevin, thank you so much >>For joining. You're welcome. Great, >>John and me talking about what's going on with Maria >>D. Thank you, John. Thank you, Lisa. On behalf of Maria B, it was wonderful. Really >>Appreciate it. Fantastic as well for John furrier. I'm Lisa Martin. You're watching the cube live from New York city at AWS summit NYC, John and I we're back with our next guest in a minute.

>>Okay, welcome back everyone. Cube's coverage here. Live on the floor at AWS summit, 2022, an in person event in San Francisco. Of course, AWS summit, 2022 in New York city is coming up this summer. The cube will be there as well. Make sure you check us out then too, but we day two of coverage had a great guest here. I Han VP of developer relations, Mongo DB, formally of AWS. We've been known each other for a long time doing, uh, developer relations at Mongo DB. Welcome to the queue. Good to see >>You. Thank to be here. Thanks for inviting me, John. It's great >>To, so Mongo DB is, um, first of all, stocks' doing really well right now. Businesswise is good, but I still think it's undervalue. A lot of people think is, is a lot more going huge success with Atlas. So congratulations to the team over there. Um, what's the update? What's the relationship withs, you know, guys have been great partners for years. What's the new thing. Yeah. >>So MongoDB Atlas obviously runs on several different major cloud providers, but AWS is the largest partner that we work with in the public cloud. So the majority of our Atlas workloads for our customers are running on the AWS platform. And just earlier this year, we announced a new strategic collaboration agreement with AWS. That's gonna further strengthen and deepen that partnership that we have with them. >>What's the main product value right now on the scale on, on Atlas, what's the drive in the revenue momentum. >>So, I mean, you know, there's a huge trend in the industry towards cloud managed databases, right? You look back 10, 15 years ago when we first met, most customers were only and operating their own data infrastructure, either running it in their own data centers, or maybe if they were really early using the primitives that cloud providers like AWS offered to run their databases in the cloud when Amazon launched RDS back in 2009, I think it was, we started to see this trend towards cloud managed databases. We followed that with our own Atlas offering back in 2016. And as Andy jazzy from AWS would say very often it's offloading that UND differentiated, heavy lifting, allowing developers to focus on building applications. They don't have to win and operate the data infrastructure. We do it for them, and that has proven incredibly popular amongst our customers. You know, Atlas route right now is growing at 50, sorry, 85% car year on year growth. >>You know, um, I've been following MongoDB for a long, long time. I mean, going back to the lamp stack days, you know, and you think about what Mongo has done as a product because of the developer traction, you know, Mongo can't do this, just keeps getting better every year. And, and the, I think the stickiness with developers is a real big part of that. Can you your view there cuz you're in VE relations. I mean, developers all love Mongo. They're teaching in school. People are picking up a side hustles, they're coding on it, using it all everywhere. I mean it's well known. >>There's a few different reasons for that. I think the main one is the, the document orientated model that we use, the document data models that are used by Mongo DB, just a net way for developers to work with data. And then, uh, we've invested in creating 16 first party drivers that allow developers using various different programming languages, whether that's JavaScript or Python or rust to integrate MongoDB, natively and idiomatic with their software. So it's very, very easy for a developer to pick up MongoDB, grab one of these drivers from their package manager of their choice and then build applications that natively manipulate data inside MongoDB, whether that's MongoDB Atlas or our enterprise edition on their own premises. They get a very consistent and very easy to, I easy to use developer experience with our, with our platform. >>Talk about the go to market with AWS. You guys also have a tightly coupled relationships. There's been announcements there recently. Uh, what's changing most right now that people should pay attention to. Well, >>The first thing is there's a huge amount of technical integration between MongoDB and AWS services. And that's the basis for many of our customers choosing to run Mon Mongo DB on AWS. We're active in 23 AWS regions around the world. And there's many other integration points as well, like cryptographic protection of Mongo MongoDB, stored data using Amazon cryptographic services, for example, or building serverless applications with AWS Lambda and MongoDB servers. So there's a ton of technical integration. Yeah, but what we started to work on now is go to market integration with AWS as well. So you can buy Mongo DB Atlas through AWS's marketplace. You can use the payer, you go offering to pay for it with your AWS bill. And then we're collaborating with AWS on migrations and other joint go to market activities as well. That >>Means get incentives, the sales people at AWS. >>Of course our moreover I mean, it's just really easy for customers, really easy for developers to consume. Yeah, they don't need to contract with MongoDB. They can use their existing AWS contracting, their existing discounting relationships and pre purchasing arrangements with AWS to consume Atlas. >>It's the classic meet the customers where they >>Are exactly right. Meet the developer where they are and meet the customers where they are now with this new model as well. >>Yeah. I love marketplace. I think it's been great. You know, even with its kind of catalog and vibe, I think it's gonna get better and better, uh, over there teams doing good work. Um, and it's easy to consume. That's key. >>Yeah. Super easy. Reduce that friction and make it real easy for developers to adopt this. Right. >>Talk about some of the top customers that you guys share with AWS. What are some of the customers you guys have together and what the benefits of the >>Relationship joint references that we talk about? A lot, one of them is Shutterfly. So in the photographic products area, they built a eCommerce offering with MongoDB and AWS. The second is seven 11 with seven 11. We're doing a lot in the mobile space. So edge applications, we've got a feature in MongoDB Atlas that allows you to synchronize data with databases on mobile devices. Those can be phones point of sale devices or handheld devices that might be used in the parcel industry, for example. So seven 11 using us in that way. And then lastly with Pitney Bowes, we've got a big digital transformation project with Pitney Bowes where they've reimagined their, uh, postage and packaging services, delivering those to their customers, using MongoDB as a data store as well. >>I wanna get in some of the trends, you've got a great per you know, you know, Mongo from Amazon side and now you're there. Um, Mongo's, as you pointed out has, has been around for a long time. What are some of the stats? I mean, how many customers, how many countries? Well, it's pretty massive >>Mind. We've got almost quarter of a billion downloads today, 240 million MongoDB downloads since we launched the first product <laugh>, we've got 33,000 active customers that are using MongoDB Atlas today and we're running well over a million free tier clusters on MongoDB Atlas across all of the different providers where we operate the service as well. So these numbers are, you know, mind blowing in terms of scale. Uh, but of course at the core of that is operational excellence. Customers love Mongo DBS because they don't have to operate it themselves. They don't have to deal with fairly conditions. They don't have to deal with scaling. They don't have to deal with deployment. We all, we do all of those things as part of the service offering and customers get an endpoint that they can use with their applications to store and retrieve data reliably. And with consistently high perform, >>You know, it's, you know, in the media, something has to be dead. Someone's the death of the iPhone, the death of this, nothing that really dies. Mongo DB has always been kind of like talked about, well, it doesn't scale on the high end. Of course, Oracle was saying that, I mean, all the, all the big database vendors were kind of throwing darts at, at Mongo, uh, DB, uh, but it kept scaling. Atlas is a whole nother. Could you just unpack that a little bit more? Why is it so important? Because scale is just, I mean, it's, it's horizontal, but it's also performant. >>Exactly. Right. So with, uh, Mongo DB's document access model that I've described already, you break some of the limitations that exist inside traditional relational databases. So, you know, they don't scale well, if you've got high concurrent and see of data access, and they're typically difficult and expensive to scale because you need to share data. Once you grow beyond individual cluster nodes, and you'll know that all relational databases suffer from these same kinds of issues with non relational systems, no SQL systems like MongoDB, you have to think a little bit more about design at the beginning. So designing database to cater for the different access patterns that you have, but in return for that upfront preparation, that design work, you get near limitless, scalability and performance will scale nearly linearly with that scalability as well. So very much more high performance, very much more simplicity for the developer as their database gets larger and their cluster gets larger to support it. >>Yeah. You know, Amazon web service has always had an a and D jazz. We talk to us all the time, every interview I've done with Swami and Matt wood or whoever on the team and executive levels always said the same thing. There's not one database to rule the world, right? Obvious you're talking about Oracle, but even within AWS customers, they're mixing and matching databases based on use cases. So in distributed environment, they're all working together. So, um, you guys fit nicely into that. So how does that, >>I think strategy slightly counterbalances that so, you know, they would say use the specific tool for the specific task that you have in hand. Yeah. What we try to focus on is creating the simple and most effective developer experience that we can, and then supporting different facets to the product in order to allow developers to different use cases. A really good example with something like MongoDB Atlas search. So we integrated Apache Luine into MongoDB Atlas. Customers can very simply apply Apache Luine search indexes to the data that they've got in MongoDB. And then they can interact with that search data using the same drivers as an API. Yeah, yeah. That they use for regular queries. So if you want to run search on your application data, you don't need a separate open search or elastic search cluster, just turn on MongoDB Atlas search and use that, that search facet. So it's interest and we have other capabilities that it's >>Vertically integrating inside within Mongo, >>Correct? Yes. That's better. Yeah. With the guy, all of creating a really simple and effective developer experience, boosting developer productivity and helping developers get more done in less time. >>You mentioned serverless earlier, what's the serverless angle with AWS when Mongo, >>Is there one? Yeah. So we have MongoDB serverless currently in preview, uh, has the same kind of characteristics that you would, or the characteristics that you would expect from a serverless data base. So consumption based model, you provision an endpoint and that will scale elastically in accordance with your usage and you get billed by consumption units so much like the serverless paradigm that we've seen delivered by AWS, the same kind of model for Mongo, DB, Atlas serverless. >>What, what attracted you to Mongo DBS? So you knew them before, or you moved over there. Um, what's going on there? What's the culture like right now? Oh, >>The culture's great. I mean, it's a much smaller company than AWS where I was before, you know, it's a very large organization. And one of the things that I really like about MongoDB is, as I've said earlier, we can serve the different use cases that a developer might have with a single product, with different aspects, to it, different facets to it. Uh, and it's a really great conversation to have with a, with a developer, with a developer customer, to be able to offer one thing that helps them solve five or six different problems that have traditionally been quite hard for them to wrestle with quite difficult for them to, to deal with. And then we've got this focus on developer experience through these driver packages that we have as well. So it's really great to have as a developer relations pro have that kind of tooling in my kit bag that can help developers become more effective. >>Talk about tooling, cuz you know, I always have, uh, kind of moments where I waffle between more. I love platforms, tools are being over overused, too many tools tool with the tool, you know, the expressions, but we're seeing from developers, the ones that don't want to go into the hood, we serverless plays beautifully. Yep. They want tools. They do. And, and the, the new engineering developers that are coming outta college and universities, they love tools. >>Yeah. And we actually have quite a few of those built into Mongo, DB Atlas. So inside Mongo, DB Atlas, we've got things like an index optimizer, which will suggest the best way that you might index your data for better perform months inside MongoDB, running on Atlas, we've got a data Explorer, which is much like another product that we've got called MongoDB compass that allows you to see and manipulate the data that you have stored within your database natively within the Atlas interface. Uh, and then we also have, uh, whole slew of different metrics, monitoring capabilities built into the platform as well. So these are aspects of Atlas that developers can take advantage of. And then over on the client side, visual studio code plugins. Yeah. So you can manipulate and operate with data directly inside visual studio code, which is obviously the most common and popular IDE out there today, as well as integration with things like infrastructure is code tools. So we support cloud formation for provisioning. We have CDK constructs inside. Yeah. The CDK construct library. We also have a lot of customers using Terraform to provision MongoDB across both AWS and other providers. So having that developer tooling of course is super important. Yeah. Aspect of the developer experience, trying to >>Build out deploying observability is a big one. How does that fit in? Cuz you knew need to talk and not only measure everything here, but talk to other systems. >>Yeah. So we recently announced a provider for Prometheus and Grafana. So we can emit metrics into those providers. Obviously CNCF projects, very common and popular inside customers that are running on Kubernetes. We've got a Kubernetes operator for MongoDB Atlas as well. Good. So you can provision MongoDB Atlas from within Kubernetes as well as having our own native metrics directly within Atlas as well. >>Ian you're crushing it. You got all the, the data, the fingertips. Are you gonna be at Cuban this year? Uh, >>I will be, but some of our team members will definitely be there. >>Yeah, we'll be at, uh, EU. The cube will be there. Great. Thanks for coming on. Appreciate the insight final world. I'll give you the last word. Tell the audience what's going on. What's at Mongo DB. What should they pay attention to? If they've used Mongo and are aware of it? What's the update. What's >>The so you should come to MongoDB world actually in New York at the beginning of June, June 7th, the ninth in the Javit center in New York. Gonna have our own show there. And of course we'd love to see you there. >>Okay. Cube comes here day two of eight, us summit, 2020, this Cub I'm John for your host. Stay with us more. Our coverage as day two winds down. Great coverage.

>> Welcome to theCUBE, I am Dave Vellante. And today we're going to explore the ebb and flow of data as it travels into the cloud and the data lake. The concept of data lakes was alluring when it was first coined last decade by CTO James Dixon. Rather than be limited to highly structured and curated data that lives in a relational database in the form of an expensive and rigid data warehouse or a data mart. A data lake is formed by flowing data from a variety of sources into a scalable repository, like, say an S3 bucket that anyone can access, dive into, they can extract water, A.K.A data, from that lake and analyze data that's much more fine-grained and less expensive to store at scale. The problem became that organizations started to dump everything into their data lakes with no schema on our right, no metadata, no context, just shoving it into the data lake and figure out what's valuable at some point down the road. Kind of reminds you of your attic, right? Except this is an attic in the cloud. So it's too big to clean out over a weekend. Well look, it's 2021 and we should be solving this problem by now. A lot of folks are working on this, but often the solutions add other complexities for technology pros. So to understand this better, we're going to enlist the help of ChaosSearch CEO Ed Walsh, and Thomas Hazel, the CTO and Founder of ChaosSearch. We're also going to speak with Kevin Miller who's the Vice President and General Manager of S3 at Amazon web services. And of course they manage the largest and deepest data lakes on the planet. And we'll hear from a customer to get their perspective on this problem and how to go about solving it, but let's get started. Ed, Thomas, great to see you. Thanks for coming on theCUBE. >> Likewise. >> Face to face, it's really good to be here. >> It is nice face to face. >> It's great. >> So, Ed, let me start with you. We've been talking about data lakes in the cloud forever. Why is it still so difficult to extract value from those data lakes? >> Good question. I mean, data analytics at scale has always been a challenge, right? So, we're making some incremental changes. As you mentioned that we need to see some step function changes. But in fact, it's the reason ChaosSearch was really founded. But if you look at it, the same challenge around data warehouse or a data lake. Really it's not just to flowing the data in, it's how to get insights out. So it kind of falls into a couple of areas, but the business side will always complain and it's kind of uniform across everything in data lakes, everything in data warehousing. They'll say, "Hey, listen, I typically have to deal with a centralized team to do that data prep because it's data scientists and DBAs". Most of the time, they're a centralized group. Sometimes they're are business units, but most of the time, because they're scarce resources together. And then it takes a lot of time. It's arduous, it's complicated, it's a rigid process of the deal of the team, hard to add new data, but also it's hard to, it's very hard to share data and there's no way to governance without locking it down. And of course they would be more self-serve. So there's, you hear from the business side constantly now underneath is like, there's some real technology issues that we haven't really changed the way we're doing data prep since the two thousands, right? So if you look at it, it's, it falls two big areas. It's one, how to do data prep. How do you take, a request comes in from a business unit. I want to do X, Y, Z with this data. I want to use this type of tool sets to do the following. Someone has to be smart, how to put that data in the right schema, you mentioned. You have to put it in the right format, that the tool sets can analyze that data before you do anything. And then second thing, I'll come back to that 'cause that's the biggest challenge. But the second challenge is how these different data lakes and data warehouses are now persisting data and the complexity of managing that data and also the cost of computing it. And I'll go through that. But basically the biggest thing is actually getting it from raw data so the rigidness and complexity that the business sides are using it is literally someone has to do this ETL process, extract, transform, load. They're actually taking data, a request comes in, I need so much data in this type of way to put together. They're literally physically duplicating data and putting it together on a schema. They're stitching together almost a data puddle for all these different requests. And what happens is anytime they have to do that, someone has to do it. And it's, very skilled resources are scanned in the enterprise, right? So it's a DBS and data scientists. And then when they want new data, you give them a set of data set. They're always saying, what can I add to this data? Now that I've seen the reports. I want to add this data more fresh. And the same process has to happen. This takes about 60% to 80% of the data scientists in DPA's to do this work. It's kind of well-documented. And this is what actually stops the process. That's what is rigid. They have to be rigid because there's a process around that. That's the biggest challenge of doing this. And it takes an enterprise, weeks or months. I always say three weeks or three months. And no one challenges beyond that. It also takes the same skill set of people that you want to drive digital transformation, data warehousing initiatives, motorization, being data driven or all these data scientists and DBS they don't have enough of. So this is not only hurting you getting insights out of your day like in the warehouses. It's also, this resource constraint is hurting you actually getting. >> So that smallest atomic unit is that team, that's super specialized team, right? >> Right. >> Yeah. Okay. So you guys talk about activating the data lake. >> Yep. >> For analytics. What's unique about that? What problems are you all solving? You know, when you guys crew created this magic sauce. >> No, and basically, there's a lot of things. I highlighted the biggest one is how to do the data prep, but also you're persisting and using the data. But in the end, it's like, there's a lot of challenges at how to get analytics at scale. And this is really where Thomas and I founded the team to go after this, but I'll try to say it simply. What we're doing, I'll try to compare and contrast what we do compared to what you do with maybe an elastic cluster or a BI cluster. And if you look at it, what we do is we simply put your data in S3, don't move it, don't transform it. In fact, we're against data movement. What we do is we literally point and set that data and we index that data and make it available in a data representation that you can give virtual views to end-users. And those virtual views are available immediately over petabytes of data. And it actually gets presented to the end-user as an open API. So if you're elastic search user, you can use all your elastic search tools on this view. If you're a SQL user, Tableau, Looker, all the different tools, same thing with machine learning next year. So what we do is we take it, make it very simple. Simply put it there. It's already there already. Point us at it. We do the hard of indexing and making available. And then you publish in the open API as your users can use exactly what they do today. So that's, dramatically I'll give you a before and after. So let's say you're doing elastic search. You're doing logging analytics at scale, they're lending their data in S3. And then they're ETL physically duplicating and moving data. And typically deleting a lot of data to get in a format that elastic search can use. They're persisting it up in a data layer called leucine. It's physically sitting in memories, CPU, SSDs, and it's not one of them, it's a bunch of those. They in the cloud, you have to set them up because they're persisting ECC. They stand up same by 24, not a very cost-effective way to the cloud computing. What we do in comparison to that is literally pointing it at the same S3. In fact, you can run a complete parallel, the data necessary it's being ETL out. When just one more use case read only, or allow you to get that data and make this virtual views. So we run a complete parallel, but what happens is we just give a virtual view to the end users. We don't need this persistence layer, this extra cost layer, this extra time, cost and complexity of doing that. So what happens is when you look at what happens in elastic, they have a constraint, a trade-off of how much you can keep and how much you can afford to keep. And also it becomes unstable at time because you have to build out a schema. It's on a server, the more the schema scales out, guess what? you have to add more servers, very expensive. They're up seven by 24. And also they become brutalized. You lose one node, the whole thing has to be put together. We have none of that cost and complexity. We literally go from to keep whatever you want, whatever you want to keep an S3 is single persistence, very cost effective. And what we are able to do is, costs, we save 50 to 80%. Why? We don't go with the old paradigm of sit it up on servers, spin them up for persistence and keep them up 7 by 24. We're literally asking their cluster, what do you want to cut? We bring up the right compute resources. And then we release those sources after the query done. So we can do some queries that they can't imagine at scale, but we're able to do the exact same query at 50 to 80% savings. And they don't have to do any tutorial of moving that data or managing that layer of persistence, which is not only expensive, it becomes brittle. And then it becomes, I'll be quick. Once you go to BI, it's the same challenge, but the BI systems, the requests are constant coming at from a business unit down to the centralized data team. Give me this flavor of data. I want to use this piece of, you know, this analytic tool in that desk set. So they have to do all this pipeline. They're constantly saying, okay, I'll give you this data, this data, I'm duplicating that data, I'm moving it and stitching it together. And then the minute you want more data, they do the same process all over. We completely eliminate that. >> And those requests are queue up. Thomas, it had me, you don't have to move the data. That's kind of the exciting piece here, isn't it? >> Absolutely no. I think, you know, the data lake philosophy has always been solid, right? The problem is we had that Hadoop hang over, right? Where let's say we were using that platform, little too many variety of ways. And so, I always believed in data lake philosophy when James came and coined that I'm like, that's it. However, HTFS, that wasn't really a service. Cloud object storage is a service that the elasticity, the security, the durability, all that benefits are really why we founded on-cloud storage as a first move. >> So it was talking Thomas about, you know, being able to shut off essentially the compute so you don't have to keep paying for it, but there's other vendors out there and stuff like that. Something similar as separating, compute from storage that they're famous for that. And you have Databricks out there doing their lake house thing. Do you compete with those? How do you participate and how do you differentiate? >> Well, you know you've heard this term data lakes, warehouse, now lake house. And so what everybody wants is simple in, easy in, however, the problem with data lakes was complexity of out. Driving value. And I said, what if, what if you have the easy in and the value out? So if you look at, say snowflake as a warehousing solution, you have to all that prep and data movement to get into that system. And that it's rigid static. Now, Databricks, now that lake house has exact same thing. Now, should they have a data lake philosophy, but their data ingestion is not data lake philosophy. So I said, what if we had that simple in with a unique architecture and indexed technology, make it virtually accessible, publishable dynamically at petabyte scale. And so our service connects to the customer's cloud storage. Data stream the data in, set up what we call a live indexing stream, and then go to our data refinery and publish views that can be consumed the elastic API, use cabana Grafana, or say SQL tables look or say Tableau. And so we're getting the benefits of both sides, use scheme on read-write performance with scheme write-read performance. And if you can do that, that's the true promise of a data lake, you know, again, nothing against Hadoop, but scheme on read with all that complexity of software was a little data swamping. >> Well, you've got to start it, okay. So we got to give them a good prompt, but everybody I talked to has got this big bunch of spark clusters, now saying, all right, this doesn't scale, we're stuck. And so, you know, I'm a big fan of Jamag Dagani and our concept of the data lake and it's early days. But if you fast forward to the end of the decade, you know, what do you see as being the sort of critical components of this notion of, people call it data mesh, but to get the analytics stack, you're a visionary Thomas, how do you see this thing playing out over the next decade? >> I love her thought leadership, to be honest, our core principles were her core principles now, 5, 6, 7 years ago. And so this idea of, decentralize that data as a product, self-serve and, and federated computer governance, I mean, all that was our core principle. The trick is how do you enable that mesh philosophy? I can say we're a mesh ready, meaning that, we can participate in a way that very few products can participate. If there's gates data into your system, the CTL, the schema management, my argument with the data meshes like producers and consumers have the same rights. I want the consumer, people that choose how they want to consume that data. As well as the producer, publishing it. I can say our data refinery is that answer. You know, shoot, I'd love to open up a standard, right? Where we can really talk about the producers and consumers and the rights each others have. But I think she's right on the philosophy. I think as products mature in this cloud, in this data lake capabilities, the trick is those gates. If you have to structure up front, if you set those pipelines, the chance of you getting your data into a mesh is the weeks and months that Ed was mentioning. >> Well, I think you're right. I think the problem with data mesh today is the lack of standards you've got. You know, when you draw the conceptual diagrams, you've got a lot of lollipops, which are APIs, but they're all unique primitives. So there aren't standards, by which to your point, the consumer can take the data the way he or she wants it and build their own data products without having to tap people on the shoulder to say, how can I use this?, where does the data live? And being able to add their own data. >> You're exactly right. So I'm an organization, I'm generating data, when the courageously stream it into a lake. And then the service, a ChaosSearch service, is the data is discoverable and configurable by the consumer. Let's say you want to go to the corner store. I want to make a certain meal tonight. I want to pick and choose what I want, how I want it. Imagine if the data mesh truly can have that producer of information, you know, all the things you can buy a grocery store and what you want to make for dinner. And if you'd static, if you call up your producer to do the change, was it really a data mesh enabled service? I would argue not. >> Ed, bring us home. >> Well, maybe one more thing with this. >> Please, yeah. 'Cause some of this is we're talking 2031, but largely these principles are what we have in production today, right? So even the self service where you can actually have a business context on top of a data lake, we do that today, we talked about, we get rid of the physical ETL, which is 80% of the work, but the last 20% it's done by this refinery where you can do virtual views, the right or back and do all the transformation need and make it available. But also that's available to, you can actually give that as a role-based access service to your end-users, actually analysts. And you don't want to be a data scientist or DBA. In the hands of a data scientist the DBA is powerful, but the fact of matter, you don't have to affect all of our employees, regardless of seniority, if they're in finance or in sales, they actually go through and learn how to do this. So you don't have to be it. So part of that, and they can come up with their own view, which that's one of the things about data lakes. The business unit wants to do themselves, but more importantly, because they have that context of what they're trying to do instead of queuing up the very specific request that takes weeks, they're able to do it themselves. >> And if I have to put it on different data stores and ETL that I can do things in real time or near real time. And that's game changing and something we haven't been able to do ever. >> And then maybe just to wrap it up, listen, you know 8 years ago, Thomas and his group of founders, came up with the concept. How do you actually get after analytics at scale and solve the real problems? And it's not one thing, it's not just getting S3. It's all these different things. And what we have in market today is the ability to literally just simply stream it to S3, by the way, simply do, what we do is automate the process of getting the data in a representation that you can now share an augment. And then we publish open API. So can actually use a tool as you want, first use case log analytics, hey, it's easy to just stream your logs in. And we give you elastic search type of services. Same thing that with CQL, you'll see mainstream machine learning next year. So listen, I think we have the data lake, you know, 3.0 now, and we're just stretching our legs right now to have fun. >> Well, and you have to say it log analytics. But if I really do believe in this concept of building data products and data services, because I want to sell them, I want to monetize them and being able to do that quickly and easily, so I can consume them as the future. So guys, thanks so much for coming on the program. Really appreciate it.

>>Hi, Brian, this is Dave Volante. Welcome to this cube conversation with Thomas Hazel was the founder and CTO of chaos surgeon. I'm also joined by ed Walsh. Who's the CEO Thomas. Good to see you. >>Great to be here. >>Explain Jason. First of all, what >>Jason, Jason has a powerful data representation, a data source. Uh, but let's just say that we try to drive value out of it. It gets complicated. Uh, I can search. We activate customers, data lakes. So, you know, customers stream their Jason data to this, uh, cloud stores that we activate. Now, the trick is the complexity of a Jason data structure. You can do all these complexity of representation. Now here's the problem putting that representation into a elastic search database or relational databases, very problematic. So what people choose to do is they pick and choose what they want and or they just stored as a blob. And so I said, what if, what if we create a new index technology that could store it as a full representation, but dynamically in a, we call our data refinery published access to all the permutations that you may want, where if you do a full on flatten, your flattening of its Jason, one row theoretically could be put into a million rows and relational data sort of explode, >>But then it gets really expensive. But so, but everybody says they have Jason support, every database vendor that I talked to, it's a big announcement. We now support Jason. What's the deal. >>Exactly. So you take your relational database with all those relational constructs and you have a proprietary Jason API to pick and choose. So instead of picking, choosing upfront, now you're picking, choosing in the backend where you really want us the power of the relational analysis of that Jaison data. And that's where chaos comes in, where we expand those data streams we do in a relational way. So all that tooling you've been built to know and love. Now you can access to it. So if you're doing proprietary APIs or Jason data, you're not using Looker, you're not using Tableau. You're doing some type of proprietary, probably emailing now on the backend. >>Okay. So you say all the tools that you've trained, everybody on you can't really use them. You got to build some custom stuff and okay, so, so, so maybe bring that home then in terms of what what's the money, why do the suits care about this stuff? >>The reason this is so important is think about anything, cloud native Kubernetes, your different applications. What you're doing in Mongo is all Jason is it's very powerful but painful, but if you're not keeping the data, what people are doing a data scientist is, or they're just doing leveling, they're saying I'm going to only keep the first four things. So think about it's Kubernetes, it's your app logs. They're trying to figure out for black Friday, what happens? It's Lilly saying, Hey, every minute they'll cut a new log. You're able to say, listen, these are the users that were in that system for an hour. And here's a different things. They do. The fact of the matter is if you cut it off, you lose all that fidelity, all that data. So it's really important that to have. So if you're trying to figure out either what happened for security, what happened for on a performance, or if you're trying to figure out, Hey, I'm VP of product or growth, how do I cross sell things? >>You need to know what everyone's doing. If you're not handling Jason natively, like we're doing either your, it keeps on expanding on black Friday. All of a sudden the logs get huge. And the next day it's not, but it's really powerful data that you need to harness for business values. It's, what's going to drive growth. It's what's going to do the digital transformation. So without the technology, you're kind of blind. And to be honest, you don't know. Cause a data scientist is kind of deleted the data on you. So this is big for the business and digital transformation, but also it was such a pain. The data scientists in DBS were forced to just basically make it simple. So it didn't blow up their system. We allow them to keep it simple, but yes, >>Both power. It reminds me if you like, go on vacation, you got your video camera. Somebody breaks into your house. You go back to Lucas and see who and that the data's gone. The video's gone because it didn't, you didn't, you weren't able to save it cause it's too >>Expensive. Well, it's funny. This is the first day source. That's driving the design of the database because of all the value we should be designed the database around the information. It stores not the structure and how it's been organized. And so our viewpoint is you get to choose your structure yet contain all that content. So if a vendor >>It says to kind of, I'm a customer then says, Hey, we got Jason support. What questions should I ask to really peel the onion? >>Well, particularly relational. Is it a relational access to that data? Now you could say, oh, I've ETL does Jason into it. But chances are the explosion of Jason permutations of one row to a million. They're probably not doing the full representation. So from our viewpoint is either you're doing a blob type access to proprietary Jason APIs or you're picking and choosing those, the choices say that is the market thought. However, what if you could take all the vegetation and design your schema based on how you want to consume it versus how you could store it. And that's a big difference with, >>So I should be asking how, how do I consume this data? Are you ETL? Bring it in how much data explosion is going to occur. Once I do this, and you're saying for chaos, search the answer to those questions. >>The answer is, again, our philosophy simply stream your data into your cloud object, storage, your data lake and with our index technology and our data refinery. You get to create views, dynamic the incident, whether it's a terabyte or petabyte, and describe how you want your data because consumed in a relational way or an elastic search way, both are consumable through our data refinery, which is >>For us. The refinery gives you the view. So what happens if someone wants a different view, I want to actually unpack different columns or different matrices. You able to do that in a virtual view, it's available immediately over petabytes of data. You don't have that episode where you come back, look at the video camera. There's no data there left. So that's, >>We do appreciate the time and the explanation on really understanding Jason. Thank you. All right. And thank you for watching this cube conversation. This is Dave Volante. We'll see you next time.

>>Welcome to the cube. I'm Dave Volante. Today, we're going to explore the ebb and flow of data as it travels into the cloud. In the data lake, the concept of data lakes was a Loring when it was first coined last decade by CTO James Dickson, rather than be limited to highly structured and curated data that lives in a relational database in the form of an expensive and rigid data warehouse or a data Mart, a data lake is formed by flowing data from a variety of sources into a scalable repository, like say an S3 bucket that anyone can access, dive into. They can extract water. It can a data from that lake and analyze data. That's much more fine-grained and less expensive to store at scale. The problem became that organizations started to dump everything into their data lakes with no schema on it, right? No metadata, no context to shove it into the data lake and figure out what's valuable. >>At some point down the road kind of reminds you of your attic, right? Except this is an attic in the cloud. So it's too big to clean out over a weekend. We'll look it's 2021 and we should be solving this problem by now, a lot of folks are working on this, but often the solutions at other complexities for technology pros. So to understand this better, we're going to enlist the help of chaos search CEO and Walsh and Thomas Hazel, the CTO and founder of chaos search. We're also going to speak with Kevin Miller. Who's the vice president and general manager of S3 at Amazon web services. And of course they manage the largest and deepest data lakes on the planet. And we'll hear from a customer to get their perspective on this problem and how to go about solving it, but let's get started. Ed Thomas. Great to see you. Thanks for coming on the cube. Likewise face. It's really good to be in this nice face. Great. So let me start with you. We've been talking about data lakes in the cloud forever. Why is it still so difficult to extract value from those data? >>Good question. I mean, a data analytics at scale is always been a challenge, right? So, and it's, uh, we're making some incremental changes. As you mentioned that we need to seem some step function changes, but, uh, in fact, it's the reason, uh, search was really founded. But if you look at it the same challenge around data warehouse or a data lake, really, it's not just a flowing the data in is how to get insights out. So it kind of falls into a couple of areas, but the business side will always complain and it's kind of uniform across everything in data lakes, everything that we're offering, they'll say, Hey, listen, I typically have to deal with a centralized team to do that data prep because it's data scientist and DBS. Most of the time they're a centralized group, sometimes are business units, but most of the time, because they're scarce resources together. >>And then it takes a lot of time. It's arduous, it's complicated. It's a rigid process of the deal of the team, hard to add new data. But also it's hard to, you know, it's very hard to share data and there's no way to governance without locking it down. And of course they would be more self-service. So there's you hear from the business side constantly now underneath is like, there's some real technology issues that we haven't really changed the way we're doing data prep since the two thousands. Right? So if you look at it, it's, it falls, uh, two big areas. It's one. How do data prep, how do you take a request comes in from a business unit. I want to do X, Y, Z with this data. I want to use this type of tool sets to do the following. Someone has to be smart, how to put that data in the right schema. >>You mentioned you have to put it in the right format, that the tool sets can analyze that data before you do anything. And then secondly, I'll come back to that because that's a biggest challenge. But the second challenge is how these different data lakes and data we're also going to persisting data and the complexity of managing that data and also the cost of computing. And I'll go through that. But basically the biggest thing is actually getting it from raw data so that the rigidness and complexity that the business sides are using it is literally someone has to do this ETL process extract, transform load. They're actually taking data request comes in. I need so much data in this type of way to put together their Lilly, physically duplicating data and putting it together and schema they're stitching together almost a data puddle for all these different requests. >>And what happens is anytime they have to do that, someone has to do it. And it's very skilled. Resources are scant in the enterprise, right? So it's a DBS and data scientists. And then when they want new data, you give them a set of data set. They're always saying, what can I add this data? Now that I've seen the reports, I want to add this data more fresh. And the same process has to happen. This takes about 60 to 80% of the data scientists in DPA's to do this work. It's kind of well-documented. Uh, and this is what actually stops the process. That's what is rigid. They have to be rigid because there's a process around that. Uh, that's the biggest challenge to doing this. And it takes in the enterprise, uh, weeks or months. I always say three weeks to three months. And no one challenges beyond that. It also takes the same skill set of people that you want to drive. Digital transformation, data, warehousing initiatives, uh, monitorization being, data driven, or all these data scientists and DBS. They don't have enough of, so this is not only hurting you getting insights out of your dead like that, or else it's also this resource constraints hurting you actually getting smaller. >>The Tomic unit is that team that's super specialized team. Right. Right. Yeah. Okay. So you guys talk about activating the data lake. Yep, sure. Analytics, what what's unique about that? What problems are you all solving? You know, when you guys crew created this, this, this magic sauce. >>No, and it basically, there's a lot of things I highlighted the biggest one is how to do the data prep, but also you're persisting and using the data. But in the end, it's like, there's a lot of challenges that how to get analytics at scale. And this is really where Thomas founded the team to go after this. But, um, I'll try to say it simply, what are we doing? I'll try to compare and stress what we do compared to what you do with maybe an elastic cluster or a BI cluster. Um, and if you look at it, what we do is we simply put your data in S3, don't move it, don't transform it. In fact, we're not we're against data movement. What we do is we literally pointed at that data and we index that data and make it available in a data representation that you can give virtual views to end users. >>And those virtual views are available immediately over petabytes of data. And it re it actually gets presented to the end user as an open API. So if you're elastic search user, you can use all your lesser search tools on this view. If you're a SQL user, Tableau, Looker, all the different tools, same thing with machine learning next year. So what we do is we take it, make it very simple. Simply put it there. It's already there already. Point is at it. We do the hard of indexing and making available. And then you publish in the open API as your users can use exactly what they do today. So that's dramatically. I'll give you a before and after. So let's say you're doing elastic search. You're doing logging analytics at scale, they're lending their data in S3. And then they're,, they're physically duplicating a moving data and typically deleting a lot of data to get in a format that elastic search can use. >>They're persisting it up in a data layer called leucine. It's physically sitting in memories, CPU, uh, uh, SSDs. And it's not one of them. It's a bunch of those. They in the cloud, you have to set them up because they're persisting ECC. They stand up semi by 24, not a very cost-effective way to the cloud, uh, cloud computing. What we do in comparison to that is literally pointing it at the same S3. In fact, you can run a complete parallel, the data necessary. It's being ETL. That we're just one more use case read only, or allow you to get that data and make this virtual views. So we run a complete parallel, but what happens is we just give a virtual view to the end users. We don't need this persistence layer, this extra cost layer, this extra, um, uh, time cost and complexity of doing that. >>So what happens is when you look at what happens in elastic, they have a constraint, a trade-off of how much you can keep and how much you can afford to keep. And also it becomes unstable at time because you have to build out a schema. It's on a server, the more the schema scales out, guess what you have to add more servers, very expensive. They're up seven by 24. And also they become brittle. As you lose one node. The whole thing has to be put together. We have none of that cost and complexity. We literally go from to keep whatever you want, whatever you want to keep an S3, a single persistence, very cost effective. And what we do is, um, costs. We save 50 to 80% why we don't go with the old paradigm of sit it up on servers, spin them up for persistence and keep them up. >>Somebody 24, we're literally asking her cluster, what do you want to cut? We bring up the right compute resources. And then we release those sources after the query done. So we can do some queries that they can't imagine at scale, but we're able to do the exact same query at 50 to 80% savings. And they don't have to do any of the toil of moving that data or managing that layer of persistence, which is not only expensive. It becomes brittle. And then it becomes an I'll be quick. Once you go to BI, it's the same challenge, but the BI systems, the requests are constant coming at from a business unit down to the centralized data team. Give me this flavor of debt. I want to use this piece of, you know, this analytic tool in that desk set. So they have to do all this pipeline. They're constantly saying, okay, I'll give you this data, this data I'm duplicating that data. I'm moving in stitching together. And then the minute you want more data, they do the same process all over. We completely eliminate that. >>The questions queue up, Thomas, it had me, you don't have to move the data. That's, that's kind of the >>Writing piece here. Isn't it? I absolutely, no. I think, you know, the daylight philosophy has always been solid, right? The problem is we had that who do hang over, right? Where let's say we were using that platform, little, too many variety of ways. And so I always believed in daily philosophy when James came and coined that I'm like, that's it. However, HTFS that wasn't really a service cloud. Oddish storage is a service that the, the last society, the security and the durability, all that benefits are really why we founded, uh, Oncotype storage as a first move. >>So it was talking Thomas about, you know, being able to shut off essentially the compute and you have to keep paying for it, but there's other vendors out there and stuff like that. Something similar as separating, compute from storage that they're famous for that. And, and, and yet Databricks out there doing their lake house thing. Do you compete with those? How do you participate and how do you differentiate? >>I know you've heard this term data lakes, warehouse now, lake house. And so what everybody wants is simple in easy N however, the problem with data lakes was complexity of out driving value. And I said, what if, what if you have the easy end and the value out? So if you look at, uh, say snowflake as a, as a warehousing solution, you have to all that prep and data movement to get into that system. And that it's rigid static. Now, Databricks, now that lake house has exact same thing. Now, should they have a data lake philosophy, but their data ingestion is not daily philosophy. So I said, what if we had that simple in with a unique architecture, indexed technology, make it virtually accessible publishable dynamically at petabyte scale. And so our service connects to the customer's cloud storage data, stream the data in set up what we call a live indexing stream, and then go to our data refinery and publish views that can be consumed the lasted API, use cabana Grafana, or say SQL tables look or say Tableau. And so we're getting the benefits of both sides, you know, schema on read, write performance with scheme on, right. Reperformance. And if you can do that, that's the true promise of a data lake, you know, again, nothing against Hadoop, but a schema on read with all that complexity of, uh, software was, uh, what was a little data, swamp >>Got to start it. Okay. So we got to give a good prompt, but everybody I talked to has got this big bunch of spark clusters now saying, all right, this, this doesn't scale we're stuck. And so, you know, I'm a big fan of and our concept of the data lake and it's it's early days. But if you fast forward to the end of the decade, you know, what do you see as being the sort of critical components of this notion of, you know, people call it data mesh, but you've got the analytics stack. Uh, you, you, you're a visionary Thomas, how do you see this thing playing out over the next? >>I love for thought leadership, to be honest, our core principles were her core principles now, you know, 5, 6, 7 years ago. And so this idea of, you know, de centralize that data as a product, you know, self-serve and, and federated, computer, uh, governance, I mean, all that, it was our core principle. The trick is how do you enable that mesh philosophy? We, I could say we're a mesh ready, meaning that, you know, we can participate in a way that very few products can participate. If there's gates data into your system, the CTLA, the schema management, my argument with the data meshes like producers and consumers have the same rights. I want the consumer people that choose how they want to consume that data, as well as the producer publishing it. I can say our data refinery is that answer. You know, shoot, I love to open up a standard, right, where we can really talk about the producers and consumers and the rights each others have. But I think she's right on the philosophy. I think as products mature in this cloud, in this data lake capabilities, the trick is those gates. If you have the structure up front, it gets at those pipelines. You know, the chance of you getting your data into a mesh is the weeks and months that it was mentioning. >>Well, I think you're right. I think the problem with, with data mesh today is the lack of standards you've got. You know, when you draw the conceptual diagrams, you've got a lot of lollipops, which are API APIs, but they're all, you know, unique primitives. So there aren't standards by which to your point, the consumer can take the data the way he or she wants it and build their own data products without having to tap people on the shoulder to say, how can I use this? Where's the data live and, and, and, and, and being able to add their own >>You're exactly right. So I'm an organization I'm generally data will be courageous, a stream it to a lake. And then the service, uh, Ks search service is the data's con uh, discoverable and configurable by the consumer. Let's say you want to go to the corner store? You know, I want to make a certain meal tonight. I want to pick and choose what I want, how I want it. Imagine if the data mesh truly can have that producer of information, you, all the things you can buy a grocery store and what you want to make for dinner. And if you'd static, if you call up your producer to do the change, was it really a data mesh enabled service? I would argue not that >>Bring us home >>Well. Uh, and, um, maybe one more thing with this, cause some of this is we talking 20, 31, but largely these principles are what we have in production today, right? So even the self service where you can actually have business context on top of a debt, like we do that today, we talked about, we get rid of the physical ETL, which is 80% of the work, but the last 20% it's done by this refinery where you can do virtual views, the right our back and do all the transformation need and make it available. But also that's available to, you can actually give that as a role-based access service to your end users actually analysts, and you don't want to be a data scientist or DBA in the hands of a data science. The DBA is powerful, but the fact of matter, you don't have to affect all of our employees, regardless of seniority. If they're in finance or in sales, they actually go through and learn how to do this. So you don't have to be it. So part of that, and they can come up with their own view, which that's one of the things about debt lakes, the business unit wants to do themselves, but more importantly, because they have that context of what they're trying to do instead of queuing up the very specific request that takes weeks, they're able to do it themselves and to find out that >>Different data stores and ETL that I can do things in real time or near real time. And that's that's game changing and something we haven't been able to do, um, ever. Hmm. >>And then maybe just to wrap it up, listen, um, you know, eight years ago is a group of founders came up with the concept. How do you actually get after analytics at scale and solve the real problems? And it's not one thing it's not just getting S3, it's all these different things. And what we have in market today is the ability to literally just simply stream it to S3 by the way, simply do what we do is automate the process of getting the data in a representation that you can now share an augment. And then we publish open API. So can actually use a tool as you want first use case log analytics, Hey, it's easy to just stream your logs in and we give you elastic search puppet services, same thing that with CQL, you'll see mainstream machine learning next year. So listen, I think we have the data lake, you know, 3.0 now, and we're just stretching our legs run off >>Well, and you have to say it log analytics. But if I really do believe in this concept of building data products and data services, because I want to sell them, I want to monetize them and being able to do that quickly and easily, so that can consume them as the future. So guys, thanks so much for coming on the program. Really appreciate it. All right. In a moment, Kevin Miller of Amazon web services joins me. You're watching the cube, your leader in high tech coverage.

>>From around the globe, it's the Cube with digital coverage of postgres Vision 2021 brought to you by >>enterprise DB. Hello everyone. This is Dave Volonte for the cube we're covering Postgres Vision 2021. The virtual cube edition. Welcome to our conversation with the Ceo Ed Boyajian is here is the Ceo of enterprise DB and we're gonna talk about what's happening in open source and database in the future of tech. Ed welcome. >>Hi Dave, Good to be here. >>Hey, several years ago, at a, at a Postgres Vision event, you put forth the premise that the industry was approaching a threshold moment, a digital transformation was the linchpin of that shift now. Ed Well you were correct and I have no doubt the audience agreed. Most people went back to their offices after that event and they returned to their hyper focus of their day to day jobs. Maybe a few accelerated their digital initiatives, but generally pre Covid, we moved in a pretty incremental pace and then the big bang hit. And if you were digital business, you are out of business. So that single event created the most rapid change that we've ever seen in the tech industry by far, nothing really compares. So the question is why is Postgres specifically and e d B generally the right fit for this new world? >>Yeah, I think, look, a couple of things are happening gave right along the bigger picture of digital transformation. We are seeing the database market in transformation and and I think the things that are driving that shift are the things that are resulting the success of Postgres and the success of B D B I think first and foremost we're seeing a dramatic re platform ng. And just like we saw in the world of Lennox where I was at red hat during that shift where people are moving from UNIX based systems to x 86 systems. We're seeing that similar re platform in happening. Whether that's from traditional infrastructures to cloud based infrastructures or container based infrastructures, it's a great opportunity for databases to be changed out. Postgres wins in that context because it's so easily deployed anywhere. I think the second thing that's changing is we're seeing a broad expansion of developers across the enterprise so they don't just live in I. T. Anymore. And I think as developers take on more power and control their defining the agenda and it's another place where Postgres shines, it's been a priority of the dBS to make postgres easier. Uh and that's coming to life. And I think the last Stack Overflow Developer Survey suggested that I think they survey 65 developers, the second most loved and the second most used database by developers, Postgres. And so I think there again Postgres shines in a moment of change. Uh and then I think the third is kind of obvious. It's always an elephant in the room, no pun intended. But it's this relentless nagging burden of the expenses of the incumbent proprietary databases and the need. And we especially saw this in Covid to start to change that more dramatically, change that economic equation here Again. PostGres shines. >>You know, I want to ask you, I'm gonna jump ahead to the future for a second because you're talking about the re platform NG and with your red hat chops, I kind of want to pick your brain on this because you're right, you saw it with red hat and you're kind of seeing it again when you think about open shift and where it's going my my question is related to replant forming around new types of workloads, new processing models at the edge. I mean you're seeing an explosion of processing power, GPU SNP us accelerators, dSPs and it appears that this is happening at a very low cost. I'm referring that you're saying Postgres can take advantage of that trend as well that that broader re platform ng trend to the edge, is that correct? >>It is. And I think you know this is, this has been one of the, I think the most interesting things with posters now I've been here almost 13 years. So if you put that in some perspective, I've watched Uh and participated in leading transformation in the category, you know, we've been squarely focused on postgres. So we've got 300 engineers who worry about making postgres better. And as you look across that landscape of time, not only as Postgres gotten more performant and more scalable, it's also proven to be the right database choice in the world of not just legacy migrations, but new application development. And I think that stack overflow developer survey is a good indicator of how developers feel about postgres. But you know, over that time frame I think if you went back to 2008 when I joined E D. B, post chris was considered a really good general purpose database. And today I think post chris is a great general purpose database. General purpose isn't sexy in the market broadly speaking, but Postgres capabilities across workloads in every area is really robust. Let me just spend a second on it. We look at our customer base is deploying in what we think of as systems of record, which are the traditional er, P type apps, uh you know where there's a single source of truth you might think of the RP apps there. We look at our customers deploying in systems of engagement. And those are apps that you might think of in the context of social media style apps or websites that are backed by a database in the third area Systems of analytics where you would typically think of data warehouse style applications interestingly. Postgres performs well and our customers report using us across that whole landscape of application areas. And I think that is one of postgres hidden superpowers. Is that ability to reach into each area of requirement on the workload side. >>And as always alluding to before that that itself is evolving as you now inject ai into the equation ai influencing and it's just a very exciting times ahead. There's no there's no database, You know, 20 years ago it was kind of boring. Now it's just exploding. I want to come back to that the notion of of post grass and maybe talk about other database models. Uh, I mean you mentioned that you've evolved from this, you know, system of record. You can take a system engagement on structured data etcetera. Jason. It's so how should we think about post grass in relation to other databases and specifically other business models of companies that provide database services? Why is Postgres attractive? Where is it winning? >>Yeah, I think a couple of places. So I mean first and foremost Postgres, you know, at his core, post chris is a sequel, relational databases in acid compliance, equal relational database. And that is inherently a strength of Postgres. But it's also a multi model database, which means we handle a lot of other, um, you know, database requirements, whether that's geospatial or or Jason, uh, for documents or time series, things like that. And so Postgres extensive bility is one of its inherent strengths and that's kind of been built in from the beginning of Postgres. So not surprisingly, people use postgres across the number of workloads because at the end of the day there's still value in having a database is able to do more. There are a lot of important specialty databases and I think they will remain important specialty databases, but Postgres thrives in its ability to cross cross over in that way. Um and I think that is, you know, one of the different key differentiators in how we've seen the market in the business development and that's the breadth of of workloads that Postgres succeeds in. But but our growth, if you kind of ventured it across vectors, we see growth happening, you know, in a few dimensions. First we see growth happening in new applications. About half of our customers that come to us today for new uh new postgres users are deploying us on new applications. The others are our second area migrating away from some existing legacy in companies often oracle. Not always. Um The third area of growth we see is in cloud, where Postgres is deployed very prolifically, both in the traditional cloud platforms, Uh like EC two, but then then again also uh in the database as a service environment. And then the fourth area growth we're seeing now is around uh container deployment, kubernetes deployment. >>Well, you may Oracle's prominent because it's just it's a big installed base and it's expensive and people, >>you >>know, they got a look at them. It's funny, I do a lot of TCO work and mostly, you know, usually TCO is about labor costs. When it comes to Oracle, it's about license costs and maintenance costs. And so to the extent that you can reduce that, at least for a portion of your state, you're gonna you're gonna drop right to the bottom line. But but but but I want to ask you about that kind of that spectrum that you think about the prevailing models for database you've got. On the one hand, You've got the right tool for the right job approach. It might be 10 or 12 data stores in the cloud. On the other hand, you've got, you know, kind of a converged approach. Oracle's going that direction clearly. Postgres with its open source innovation is going that direction. And it seems to me that at scale that's a more the latter is a more cost effective model. How do you think about that? >>Well, you know, I think at the end of the day, you kind of have to look at it. I mean, the business side of my brain looks at that as an addressable market question. Right? And you've heard me talk about three broad categories of workloads and you know, people define workloads in different bucket, but that's how we do it. But if you look at just a system of record in the system of engagement market, I think that's what would be traditionally viewed as the database market. Uh and there that's you know, let's just say for the sake of arguments of $45-$50 billion $18 billion dollar market. And you know, as we talk about that. So all in it's still between 60 and $70 billion market. And I think what happens there's so much heat and light poured on the valuation multiples of some of the specialty players. That the market gets confused, but the reality is our customers don't get confused. I mean if you look at those specialty players take that $48 billion market. I mean add up Mongo red is cockroach neo, all of those. I mean hugely valued companies. All unicorn companies. But combined to add up to a billion bucks don't get me wrong that's important revenue and meaningful in the workloads they support. But it's not. It doesn't define the full transformation of this category. Look at the systems of analysis again, another great great market example. I mean if you add up the consolidation of the Hadoop vendors add in there. Um Snowflake, you're still talking you know a billion five in revenue and an $18 billion market. So while those are all important technologies, the question is in this transformation move to the database market fully transform you. And my view is no it didn't were in the first maybe second inning of a $65 billion transformation. And I think this is where Postgres will ultimately shine. I think this is how Postgres wins because at the end of the day the nature of the workloads fits with postgres and the future tech that we're building in post schools will serve that broader set of needs I think more effectively >>well. And I love these tam expansion discussions because I think you're right on and I think it comes back to the data and we all talk about the data growth, the data explosion, we see the I. D. C. Numbers and you ain't seen nothing yet. And so data by its very nature is distributed. That's why I get so excited about these new platform models and and I want to tie it back to developers and open source because to me that is the linchpin of innovation um in the next decade it has been, I would even say for the last decade we've seen it, but it's gaining momentum, so, so in thinking about innovation and and specifically Postgres and an open source, you know, what can you share with us in terms of how we should think about your advantage, and again, what, where people are glomming leaning in to that advantage? >>Yeah, so, I mean, I think I think you bring up a really important topic for us as a company. Postgres we think is an incredibly powerful community, uh and when you step away from it again, I remember I told you I was at red hat before, now here at E D B, and there's a common thread that runs through those two experiences in both experiences. The companies are attached and prominent alongside a strong independent, open source community, and I think the notion of an independent community is really important to understand around postgres. There are hundreds and thousands of people contributing to Postgres now. E D B plays a big role in that. About approaching a third of the contributions. In the last release released, 13 of Postgres came from E D B. You might look at that and say gee, that sounds like a lot, but if you step away from it, you know, about 30% of those contributions, Most of the contributions come from a universe around D D. B. And that's inherently healthy for the community's ability to innovate and accelerate. And I think that while we play a strong role there, you can imagine that having and there are other great companies that are contributing to Postgres, I think having those companies participating and contributing gets the best, the best ideas to the front in innovation. So I think the inherent nature Postgres community makes it strong and healthy. And then contrast that to some of the other prominent high value open source companies, the companies and the communities are intimately intertwined. They're one and the same. They're actually not independent open source communities. And I think that therein lies one of the, one of the inherent weaknesses in those but postgres to rise because you know, we bring all those ideas from the DB, we bring a commercial contingent with us all the things we hope we emphasize and focus on in growth and postgres, whether that's in the areas of scalability, manageability, all hot topics, of course security, all of those areas. And then, you know, performance as always, all of those areas are informed to us by enterprise customers deploying post chris at scale. And I think that's the heart of what makes a successful independent project. >>Yeah. The combinatorial powers of of that ecosystem. Uh they their their multiplication, I've as opposed to the resources of one. I want to talk about postgres Vision 2021 sort of set up that a little bit. The theme this year is the future. Is you, what do you mean by that? >>So if you think about what we just said post the category is in transit database categories and transformation. And we know that many of our people are interested in. Postgres are early in their journey, their early in their experience. And so we want to focus this year's postcards vision on them that we understand as a company has been committed to postgres as long as we have and with the understanding we have the technology and best practices, we want to share that view those insights uh, with those who are coming to postgres, Some for the first time, some who are experienced >>Postgres. Vision 21 is june 22nd and 23rd. Go to enterprise db dot com and register the cube is going to be there. We hope you will be too. Ed, thanks for coming to the Cuban previewing the event. >>Thanks Dave. >>Thank you. We'll see you at Vision 21 >>mm mm.

>>From around the globe. It's the cube with digital coverage of IBM. Think 20, 21 brought to you by IBM, >>Welcome back to IBM. Think at 2021, the virtual edition, my name is Dave Volante and you're watching the cubes continuous coverage of think 21. And right now we're going to talk about banking and the post isolation economy. I'm very pleased to welcome our next guests. Look at Wigley's the general manager global banking financial markets at IBM and John diagonal is the global CTO and vice president and distinguished engineer for banking and financial services. Gentlemen, welcome to the cube. That's my pleasure. Look at this current economic upheaval, it's quite a bit different from the last one. Isn't it? I mean, liquidity doesn't seem to be a problem for most banks these days. I mean, if anything, they're releasing loan loss reserves that they didn't need. What's from your perspective, what's the state of banking today and hopefully as we exit this pandemic soon. Okay. >>So, so Dave, I think, like you say, it's a, it's a, it's a state in a picture that, uh, in a significantly different from what people were expecting. And I, and I think some way, in some ways you're seeing the benefits of a number of the regulations that were put into, into place after the, you know, the financial crisis last time round, right? And therefore this time, you know, a health crisis did not become a financial crisis because I think the banks were in better shape. And also, you know, governments clearly have put worldwide a lot of liquidity into the, into the system. Um, I think if you look at it though, um, maybe two or three things ready to call out, firstly, there's a, there's a massive regional variation. So if you look at the U S banking industry, uh, it's extremely buoyant and I'll come back to that in a managing the way in which it's performing. >>Uh, you know, the banks that are starting to report that first quarter results are going to show a profitability that's significantly ahead of where they were last year. And probably some of those, some of that best performance for quite a long time, if you go into Europe, it's a completely different picture. I think the banks are extremely challenged at that. And I think you're going to see a much Bleaker outlook in terms of what those banks report, as far as Asia Pacific is concerned again, you know, because they did, they have come out of the pandemic much faster that consumer businesses back into growth. Again, I think they're showing some pretty buoyant up performance as far as, as far as banking performance is concerned. I think the beast that's particularly interesting. And I think Kim is a bit of a surprise to most, uh, is, is what we've seen in the U S right? >>And in the U S what's actually happened is, uh, the investment banking side of banking businesses has been doing better than they've ever done before. There's been the most unbelievable amount of acquisition activity. You've seen a lot of what's going on with the specs that's driving the res you know, deal based fee income for the banks, the volatility in the marketplace, meaning that trading income is much, much higher than it's ever been. And therefore the banks are very much seeing a profitability on that investment banking side. That was way ahead of what I think they were, they were expecting. Consumer business is definitely down. If you look at the credit card business, it's down, if you look at, uh, you know, lending activity, that's going down, going out, it's substantially less than where it was before. There's hardly any lending growth because the economy is flat at this moment in time. >>But again, the good news that, and I think this is a worldwide, but you're not just in the us. The good news here is that because of the liquidity and some of them are special mentions that government put out that there has not been, uh, the, the level of bankruptcies that people were expecting. Right. And that for most of the provisioning that the banks did, um, in expectation of non-performing loans has been, I think, a much more, much greater than what they're going to need, which is why you're starting to supervision is being released as well, which I kind of flattering, flattering the income flattering. I think going forward though, you're going to see a different picture. >>It's the, thank you for the clarification on the regional divergence is that you're right on, I mean, European central banks are, are not the same, the same position, uh, to, to affect liquidity, but is that nuance, is that variation across the globe? Is that, uh, is that a blind spot? Is that a, is that a, a concern, uh, or the other, other greater concerns, you know, inflation and, and, and the, the, the pace of the, the return to the economy. What are your thoughts on that? >>So I think, I think the, um, the, the, the concern, um, you know, as far as the European marketplace is concerned is, um, you know, whether the, the performance that in particularly, I don't think the level of Verition in there was quite as generous as we saw in other parts of the world. And therefore, um, you know, ease the issue around non-performing loans in, in Europe going to hold the European, uh, European banks back. And are they going to, you know, therefore constrained them under lending that they put into the economy. And that then, um, you know, reduces the level of economic growth that we see in Europe. Right. I think, I think that is certainly that is certainly a concern. Um, I would be surprised and I've been looking at, you know, forecasts that have been brought forward by various people around the world around infection. >>I would be surprised if inflation starts to become a genuine problem in the, in the kind of short to medium term. I think in the industry that are going to be two or three other things that are probably going to be more, you know, going to be more issues. Right. I think the first one, which is becoming top of mind for chief executives is this whole area around operational resiliency. So, you know, regulators universally are making very, very sure that banks do not have a technical debt or a complexity of legacy systems issue. They are. And, you know, the UK has taken the lead on this and they are going so far as even requiring non-executive directors to be liable. If banks are found to not have the right policies in place, this is not being followed by other regulators around the world. Right. So, so that is very much top of mind at this moment in time. >>So I think discretionary investment is going to be, uh, you know, to watch, um, uh, solving that particular problem. I think that that's one issue. I think the other issue is what the pandemic has shown is that, and, and, and this was very evident to me. I mean, I spent the last three years out in Singapore where, you know, banks have become very digital businesses. Right. When I came into the U S in my current role, it was somewhat surprising to me as to where the U S marketplace was in terms of digitization of banking. But if you look in the last 12 months, uh, you know, I think more has been achieved in terms of banks becoming digital businesses. And they've probably done in the last two or three years. Right. And then the real acceleration of that, uh, digitalization, which is going to continue to happen. But the downside of that has been that the threat to the banking industry from essentially fintechs and big decks has exactly, you know, it's really accelerated. Right, right. I mean, just to give you an example, pay Pat is the second largest financial services institution in the us, right. So that's become a real problem of my English. The banking industry is going to have to deal with, >>I want to come back to that, but now let's bring John into the conversation. Let's talk about the tech stack. Look, it was talking about whether it was resiliency going digital. We certainly saw with the pandemic remote work, huge, huge volumes of things like PPP and, and, and, and, and mortgages and with dropping rates, et cetera. So, John, how has the tech stack been altered in the past 14 months? >>Great question, Dave and it's top of mind for almost every single financial services firm, regardless of the sector within the overall industry, every single business has been taking stock of how they handled the pandemic and the economic conditions thereafter, and all of the business needs that were driven by the pandemic. In so many situations, firms were unable to service their clients or were not competitive in serving their clients. And as a result, they've had to do very deep, uh, uh, architectural, uh, transformation and digital transformation around their core platforms, their systems of analytics and their systems, their front end systems of engagement in terms of, uh, the core processing systems that many of these institutions, some in many cases, they're 50 years old. And with any 50 year old application platform, there are inherent limitations as an inflexibility and flexibility as an inability to innovate for the future as a speed of delivery issue. In, in other words, it can be very hard to accelerate delivery of new capabilities onto an aging platform. And so in every single case, um, institutions are looking to hybrid cloud and public cloud technology, and pre-packaged AI and pre-packaged solutions from an ISV ecosystem of software vendor ecosystem to say, as long as we can crack open many of these old monolithic cores and surround them with new digitization, new user experience that spans every channel and automation from the front to back of every interaction, that's where most institutions are prioritizing. Yep. >>Banks, aren't gonna migrate. Uh, they're gonna, they're going to build a abstraction layer. I want to come back to the disruption is so interesting. You had the Coinbase IPO last month, see Tesla and micro strategy. They're putting Bitcoin on their balance sheets. Jamie diamond says traditional banks are playing a smaller role in the financial system because of the new fintechs. Look at, you mentioned PayPal, the Stripe does Robin hood. You get the Silicon Valley giants have this dual disruptive disruption agenda, Apple, Amazon, even Walmart, Facebook. The question is, are traditional banks going to lose control of the payment systems? >>Yeah, I mean, I think to a large extent that is, that is already happened, right? Because I think if you look at, if you look at the experience in Asia, right, and you look at particularly organizations like iron financial, uh, you know, in India, you look at organizations like ATM the, you know, very substantial trends, particularly on the consumer payment side has actually moved, uh, away from the banks. And I think you're starting to see that in the West as well, right. With organizations like, you know, cloud. Now that's coming out with this, um, you know, pay, you know, buying out the later type of schemes. You've got and then, so you've got PayPal. And as you said, Stripe, uh, and, and others as well, but it's not just, um, you know, in the payment side. Right. I think, I think what's starting to happen is that, that are very core part of the banking business, you know, especially things like lending, for instance, where again, you are getting a number of these, um, fintechs and big, big tech companies entering the marketplace. >>And I, and I think the threat for the banks is, and this is not going to be small chunks of market share that you're going to actually lose. Right. It's, it's, it's actually, uh, it could actually be a Kodak moment. Let me give you an example. Uh, you know, you will have just seen that grab is going to be acquired by one of these facts for about $40 billion. I mean, this organization started like the Uber in Singapore. It very rapidly got into both the payment side, right? So it actually went to all of these mom and pop shops and it offered QR based, um, go out code based payment capabilities to these very small retailers. They were charging about half or a third of what MasterCard or visa were charging to run those payment routes. They took market share overnight. You look at the remittance business, right? >>They, they went into the remittance business, they set up these wallets in 28 countries around the ICR and region. They took huge chunks of business completely away from DBS, which is the local bank out there from Western union and all of these, all of these others. So, so I, I think it's a real threat. I think Jamie Dimon is saying what the banking industry has said always, right? Which is the reason we are losing is because the playing field is not even, this is not about playing fields and even right. All of these businesses have been subject to exactly the same regulation that the bank shop subject to regulations in Singapore and India, more onerous than maybe in other parts of the world. This is around the banking business, recognizing that this is a threat. And exactly, as John was saying, you got to get to delivering the customer experience. >>That juniors are wanting at the level of pasta they're prepared to pay. And you're not going to do that by purely shorting out the channels and having a cool app on somebody's smartphone. Right? If that smartphone is 48 by arcade processes and legacy systems, where can I apply? You know, like, like today, you know, you make a payment, your payment does not clear for five days, right? Whereas in Singapore I make a payment, the payment is instantaneously cleared, right? That's where the banking system is going to have to get to in order to get to that. You need to order the whole stack. And the really good news is there are many examples where this has been done very successfully by incumbent banks. You don't have to set up a digital bank on the side to do it. An incumbent bank could do it, and it can do it in a sense of a period of time, or does sense for level of investment. A lot of IBM's business across our consulting, as well as our, our technology stack is very much trying to do that with our clients. So I am personally very bullish about what the industry >>Yeah. I mean, taking friction out of the system sometimes with the case of crypto taking the middle person out of the system. But I think you guys are savvy. You understand that, you know, like, yeah, Jamie diamonds a couple of years ago said, he'd fire anybody doing crypto Janet Yellen and says, ah, I don't really get it. You know, Warren buffet. But I think as technology people, we look at it and say, okay, wait a minute. This is an interesting Petri dish. There's, there's fundamental technology here that has massive funding that is going to inform, you know, the future. I think, you know, big bags are gonna lean in some of them and others, others. Won't, uh, John, give you the last word here, >>But for sure they're leaning in. Uh, so to just, to, to, to think about, uh, uh, something that Likud said a moment ago, the reason these startups were able to innovate fast was because they didn't have the legacy. They didn't have the spaghetti lying around. They were able to be relentlessly laser focused on building new, using the API ecosystem, going straight to public and hybrid cloud and not worrying about everything that had been built for the last 50 years or so. The benefit for existing institutions, the incumbents is that they can use all of the same techniques and tools and hybrid cloud accelerators in terms. And we're not just thinking about, um, uh, retail banking here, your question around the industry, that disruption from Bitcoin, blockchain technologies, new ways of processing securities. It is playing out in every single securities processing and capital markets organization. Right now I'm working with several organizations right now, exactly on how to build custody systems, to take advantage of these non fungible digital assets. It's a hot, hot topic around which there's, uh, incredible, uh, appetite to invest an incredible appetite to innovate. And we know that the center of all these technologies are going to be cloud forward cloud ready, AI infused data infuse technologies. >>So I want to have you back. I wish you had more time. I want to talk about specs. I want to talk about NFTs. I want to talk about technology behind all this really great conversation and really appreciate your time. I'm sorry. We got to go. >>Thank you. Thanks so much indeed, for having us. >>Oh, really? Pleasure. Was mine. Thank you for watching everybody. This is Dave Volante for IBM. Think 2021. You're watching the cube.

>>from >>around the globe. It's the cube with digital >>Coverage of IBM think 2021 brought to you by IBM. >>Welcome back to IBM Think 2021 The virtual edition. My name is Dave Volonte and you're watching the cubes continuous coverage of think 21. And right now we're gonna talk about banking in the post isolation economy. I'm very pleased to welcome our next guest. Look at wag lee is the general manager, Global banking financial markets at IBM and john Degnan is the global ceo and vice president and distinguished engineer for banking and financial services. Gentlemen, welcome to the cube. >>Thank you. Yeah >>that's my pleasure. Look at this current economic upheaval. It's quite a bit different from the last one, isn't it? I mean liquidity doesn't seem to be a problem for most pecs these days. I mean if anything they're releasing loan loss reserves that they didn't need. What's from your perspective, what's the state of banking today and hopefully as we exit this pandemic soon. >>So so dave, I think, like you say, it's, you know, it's a it's a state and a picture that in a significantly different from what people were expecting. And I think some way, in some ways you're seeing the benefits of a number of the regulations that were put into into place after the, you know, the financial crisis last time around, right? And therefore this time, you know, a health crisis did not become a financial crisis, because I think the banks were in better shape. And also, you know, governments clearly have put worldwide a lot of liquidity into the, into the system. I think if you look at it though, maybe two or three things ready to call out firstly, there's a there's a massive regional variation. So if you look at the U. S. Banking industry, it's extremely buoyant and I'll come back to that in a minute in the way in which is performing, you know, the banks that are starting to report their first quarter results are going to show profitability. That's you know significantly ahead of where they were last year and probably some of the some of their best performance for quite a long time. If you go into europe, it's a completely different picture. I think the banks are extremely challenged out there and I think you're going to see a much bleaker outlook in terms of what those banks report and as far as Asia pacific is concerned again, you know because they they have come out of the pandemic much faster than consumer businesses back into growth. Again, I think they're showing some pretty buoyant performance as far as as far as banking performance is concerned. I think the piece that's particularly interesting and I think him as a bit of a surprise to most is what we've seen in the U. S. Right. And in the US what's actually happened is uh the investment banking side of banking businesses has been doing better than they've ever done before. There's been the most unbelievable amount of acquisition activity. You've seen a lot of what's going on with this facts that's driving deal raised, you know, deal based fee income for the banks. The volatility in the marketplace is meaning that trading income is much much higher than it's ever been. And therefore the banks are very much seeing a profitability on that investment banking side. That was way ahead of what I think they were. They were expecting consumer businesses definitely down. If you look at the credit card business, it's down. If you look at, you know, lending activity that's going down going out is substantially less than where it was before. There's hardly any lending growth because the economy clearly is flat at this moment in time. But again, the good news that, and I think this is a worldwide which are not just in us, the good news here is that because of the liquidity and and some of the special measures the government put out there, there has not been the level of bankruptcies that people were expecting, right. And therefore most of the provisioning that the banks did um in expectation of non performing loans has been, I think, a much more, much greater than what they're going to need, which is why you're starting to see provisions being released as well, which are kind of flattering, flattering the income, flattering the engine. I think going forward that you're going to see a different picture >>is the re thank you for the clarification on the regional divergence, is that and you're right on, I mean, european central banks are not the same, the same position uh to to affect liquidity. But is that nuances that variation across the globe? Is that a is that a blind spot? Is that a is that a concern or the other other greater concerns? You know, inflation and and and the the pace of the return to the economy? What are your thoughts on that? >>So, I think, I think the concern, um, you know, as far as the european marketplace is concerned is um you know, whether whether the performance that and particularly, I don't think the level of provisions in there was quite a generous, as we saw in other parts of the world, and therefore, you know, is the issue around non performing loans in in europe, going to hold the european uh european banks back? And are they going to, you know, therefore, constrain the amount of lending that they put into the economy and that then, um, you know, reduces the level of economic growth that we see in europe. Right? I think, I think that is certainly that is certainly a concern. Um I would be surprised and I've been looking at, you know, forecasts that have been put forward by various people around the world around inflation. I would be surprised if inflation starts to become a genuine problem in the, in the kind of short to medium term, I think in the industry that are going to be two or three other things that are probably going to be more, you know, going to be more issues. Right. I think the first one which is becoming top of mind for chief executives, is this whole area around operational resiliency. So, you know, regulators universally are making very very sure that banks do not have a technical debt or a complexity of legacy systems issue. They are and you know, the U. K. Has taken the lead on this and they are going so far as even requiring non executive directors to be liable if banks are found to not have the right policies in place. This is now being followed by other regulators around the world. Right. So so that is very much drop in mind at this moment in time. So I think discretionary investment is going to be put you know, towards solving that particular problem. I think that's that's one issue. I think the other issue is what the pandemic has shown is that and and and this was very evident to me and I mean I spent the last three years out in Singapore where you know, banks have become very digital businesses. Right? When I came into the U. S. In my current role, it was somewhat surprising to me as to where the U. S. Market place was in terms of digitization of banking. But if you look in the last 12 months, you know, I think more has been achieved in terms of banks becoming digital businesses and they've probably done in the last two or three years. Right. And that the real acceleration of that digitization which is going to continue to happen. But the downside of that has been that the threat to the banking industry from essentially fintech and big tex has exactly, it's really accelerated. Right, Right. Just to give you an example, Babel is the second largest financial services institutions in the US. Right. So that's become a real problem I think with the banking industry is going to have to deal with >>and I want to come back to that. But now let's bring john into the conversation. Let's talk about the tech stack. Look, it was talking about whether it was resiliency going digital, We certainly saw over the pandemic, remote work, huge, huge volumes of things like TPP and and and and and mortgages and with dropping rates, etcetera. So john, how is the tech stack Been altered in the past 14 months? >>Great question. Dave. And it's top of mind for almost every single financial services firm, regardless of the sector within the overall industry, every single business has been taking stock of how they handled the pandemic and the economic conditions thereafter and all of the business needs that were driven by the pandemic. In so many situations, firms were unable to service their clients or we're not competitive in serving their clients. And as a result they've had to do very deep uh architectural transformation and digital transformation around their core platforms. Their systems of analytics and their systems different end systems of engagement In terms of the core processing systems that many of these institutions, some in many cases there are 50 years old And with any 50 year old application platform there are inherent limitations. There's an in flex itty inflexibility. There's an inability to innovate for the future. There's a speed of delivery issue. In other words, it can be very hard to accelerate the delivery of new capabilities onto an aging platform. And so in every single case um institutions are looking to hybrid cloud and public cloud technology and pre packaged a ai and prepackaged solutions from an I. S. V. Ecosystem of software vendor ecosystem to say. As long as we can crack open many of these old monolithic cause and surround them with new digitalization, new user experience that spans every channel and automation from the front to back of every interaction. That's where most institutions are prioritizing. >>Banks aren't going to migrate, they're gonna they're gonna build an abstraction layer. I want to come back to the disruption is so interesting. The coin base I. P. O. Last month see Tesla and microstrategy. They're putting Bitcoin on their balance sheets. Jamie diamonds. Traditional banks are playing a smaller role in the financial system because of the new fin text. Look at, you mentioned Paypal, the striped as Robin Hood, you get the Silicon Valley giants have this dual disrupt disruption agenda. Apple amazon even walmart facebook. The question is, are traditional banks going to lose control of the payment systems? >>Yeah. I mean I think to a large extent that is that has already happened, right? Because I think if you look at, you know, if you look at the experience in ASia, right? And you look at particularly organizations like and financial, you know, in India, you look at organizations like A T. M. You know, very substantial chance, particularly on the consumer payments side has actually moved away from the banks. And I think you're starting to see that in the west as well, right? With organizations like, you know, cloud, No, that's coming out with this, you know, you know, buying out a later type of schemes. You've got great. Um, and then so you've got paper and as you said, strike, uh and and others as well, but it's not just, you know, in the payment side. Right. I think, I think what's starting to happen is that there are very core part of the banking business. You know, especially things like lending for instance, where again, you are getting a number of these Frontex and big, big tech companies entering the marketplace. And and I think the threat for the banks is this is not going to be small chunks of market share that you're going to actually lose. Right? It's it's actually, it could actually be a Kodak moment. Let me give you an example. Uh, you know, you will have just seen that grab is going to be acquired by one of these facts for about $40 billion. I mean, this organization started like the Uber in Singapore. It very rapidly got into both the payment site. Right? So it actually went to all of these moment pop shops and then offered q are based um, 12 code based payment capabilities to these very small retailers, they were charging about half or a third or world Mastercard or Visa were charging to run those payment rails. They took market share overnight. You look at the Remittance business, right? They went into the Remittance business. They set up these wallets in 28 countries around the Asean region. They took huge chunks of business completely away from DBS, which is the local bank out there from Western Union and all of these, all of these others. So, so I think it's a real threat. I think Jamie Dimon is saying what the banking industry has said always right, which is the reason we're losing is because the playing field is not even, this is not about playing fields. Been even write, all of these businesses have been subject to exactly the same regulation that the banks are subject to. Regulations in Singapore and India are more onerous than maybe in other parts of the world. This is about the banking business, recognizing that this is a threat and exactly as john was saying, you've got to get to delivering the customer experience that consumers are wanting at the level of cost that they're prepared to pay. And you're not going to do that by purely sorting out the channels and having a cool app on somebody's smartphone, Right? If that's not funny reported by arcade processes and legacy systems when I, you know, like, like today, you know, you make a payment, your payment does not clear for five days, right? Whereas in Singapore, I make a payment. The payment is instantaneously clear, right? That's where the banking system is going to have to get to. In order to get to that. You need to water the whole stack. And the really good news is that many examples where this has been done very successfully by incumbent banks. You don't have to set up a digital bank on the site to do it. And incumbent bank can do it and it can do it in a sensible period of time at a sensible level of investment. A lot of IBM s business across our consulting as well as our technology stack is very much trying to do that with our clients. So I am personally very bullish about what the industry >>yeah, taking friction out of the system, sometimes with a case of crypto taking the middle person out of the system. But I think you guys are savvy, you understand that, you know, you yeah, Jamie Diamond a couple years ago said he'd fire anybody doing crypto Janet Yellen and says, I don't really get a Warren Buffett, but I think it's technology people we look at and say, okay, wait a minute. This is an interesting Petri dish. There's, there's a fundamental technology here that has massive funding that is going to inform, you know, the future. And I think, you know, big bags are gonna lean in some of them and others, others won't john give you the last word here >>for sure, they're leaning in. Uh so to just to to think about uh something that lick it said a moment ago, the reason these startups were able to innovate fast was because they didn't have the legacy, They didn't have the spaghetti lying around. They were able to be relentlessly laser focused on building new, using the app ecosystem going straight to public and hybrid cloud and not worrying about everything that had been built for the last 50 years or so. The benefit for existing institutions, the incumbents is that they can use all of the same techniques and tools and hybrid cloud accelerators in terms And we're not just thinking about uh retail banking here. Your question around the industry that disruption from Bitcoin Blockchain technologies, new ways of processing securities. It is playing out in every single securities processing and capital markets organization right now. I'm working with several organizations right now exactly on how to build custody systems to take advantage of these non fungible digital assets. It's a hard, hard topic around which there's an incredible appetite to invest. An incredible appetite to innovate. And we know that the center of all these technologies are going to be cloud forward cloud ready. Ai infused data infused technologies >>Guys, I want to have you back. I wish I had more time. I want to talk about SPAC. So I want to talk about N. F. T. S. I want to talk about technology behind all this. You really great conversation. I really appreciate your time. I'm sorry. We got to go. >>Thank you. Thanks very much indeed for having us. It was a real pleasure. >>Really. Pleasure was mine. Thank you for watching everybody's day. Volonte for IBM think 2021. You're watching the Cube. Mhm.

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 >>sponsored by >>Intel and AWS. Yeah, Welcome back to the cubes. Ongoing coverage of aws reinvent virtual the Cuba has gone virtual to. We're gonna talk about machine intelligence, cloud and transformation in healthcare. An industry that is rapidly evolving and reinventing itself to provide better quality care faster and more accurate diagnoses. And this has to be done at lower cost. And with me to talk about This is Dr Taha. Awesome. Who? Who is the director of machine learning at Amazon Web services? Doctor, good to see you again. Thanks for coming on. >>Thank you so much. Good to see Dave. >>Yeah, last time we talked, I think it was a couple of years ago. We remember we were talking about Amazon. Comprehend medical. And, of course, you've been so called so called raising the bar, so to speak, Over the past 24 months, you made some announcements today, including Amazon Health Lake, which we're gonna talk about. Tell us about it. >>Well, we're really excited about eso our customers. Amazon Half Lake, a new hip eligible service for health care providers health insurance companies and pharmaceutical companies to securely store, transform Aquarian, analyze health data in the cloud at petabytes scale, a Amazon health lake uses machine learning models trained to automatically understand context and extract meaningful data from medical data from raw, disparate information such as medications, procedures, Um, and diagnosis. Um Therefore, revolutionizing a process that was traditionally manual Arab prone and highly costly requires a lot of expertise on teams within these organizations. What healthcare Catholic does is it tags and indexes every piece of information on then structure in an open standard. The fire standard, or that's the fast healthcare interoperability resource, is in order to provide a complete view 360 degree view of each patient in a consistent way so you'll be able to curry and share that data securely. It also integrates with other machine learning services and a lot of services that AWS offers, such as Amazon Quicksight or Amazon sage maker. In order to visualize and understand the relationships in the data identify trends, Andi also make predictions. The other great benefit is since the Amazon health lake automatically structures all the health care organizations data into open standard. The fire industry format. The information now can be easily and securely shared between systems. Health systems onda with third party applications. So eso providers, health care providers will will enjoy the ability to collaborate more effectively with each other but also allowing patients and federal access to their medical information. >>I think now, so one of things that people are gonna ask is Okay, wait a minute. Hip eligible Is that like cable ready or HD ready? And but people need to understand that it's a shared responsibility. But you can't come out of the box and be HIPPA compliant there a number of things and processes, uh, that that your customer has to do. Maybe you could explain that a little >>bit. Absolutely. I mean, in practice a little bit. This is a very, very important thing, and and it's something that we really fully baked into the service and how we built Also the service, especially dealing with health care information. First off, AWS, as you know, is vigilant about customers, privacy and security. It is job zero for us. Your data and Health Lake is secure, compliant, and auditable data version is enabled to protect um, the data against any accident collision, for example, and per fire sophistication. If you are to delete one piece of data, it will be version it will be on Lee. Hidden from analysis is a result not believed from the service. So your dad is always encrypted on by using your own customer. Manage key in a keys in a single tenant. Architectures is another added benefit to provide the additional level of protection when the data is access and search for example, every time inquiry a value, for example, someone's glucose level if the data is encrypted and decrypted and and and and so on and so forth. So, additionally, this system in a single tenant architectures so that that way the data, uh, the key. The same key is not shared across multiple customers. So you're saying full ownership and control of your data along with the ability to encrypt, protect move, deleted in alignment with organization, security and policies. Now a little bit about the hip eligibility. It's a term that AWS uses eso for customers storing protected health information or P h. I A. DBS by its business associate agreement on also Business Associate amendment require customers to encrypt data addressed in transit when they're using area services. There are over 100 services today. They're hip eligible, including the Amazon. Health like this is very important, especially for, uh enabling discovered entities and their business associates subject to HIPAA regulations, and is be able to kind of and this shared model between what a the best protection and services and how it can process and store and managed ph I. But there's additional level of compliance is required on the on the customer side, um, about you know, anywhere from physical security thio how each application can be built, which is no different than how you manage it. For example, today in your own that data center, what not? But this is why many cats, growing number of health care providers, um, players as well as I, because professionals are using AWS utility based cloud services today to process, store and transmit pH. I. >>So tell us more about who was gonna benefit from this new capability, what types of organizations and would be some of the outcomes for for for patients, >>absolutely every healthcare provider today, or a payer like a health insurance company or a life. Science companies such as Pharma Company is just trying to solve the problem of organizing instruction their data. Because if you do, you make better sense of this information from better patient support decisions. Design better clinical trials, operate more efficiently, understand population health trends on be able them to share that that security. It's really all starts with making sense of that of that data. And those are the ultimate customers that we're trying to empower with the Amazon Amazon Health Lake. Um, >>well, And of course, there's downstream benefits for the patient. Absolutely. That's ultimately what we're trying to get to. I mean, absolutely. I mean, I set up front. I mean, it's it's everybody you know, feels the pain of high health care costs. A lot of times you're trying to get to see a doctor, and it it takes a long time now, especially with with covitz so and much of this, oftentimes it's even hard to get access to your own data s. So I think you're really trying to attack that problem. Aren't >>you absolutely give you a couple of examples like I mean, today, the most widely used clinical models, uh, in practice to predict. Let's say someone's disease risk lack personalization. Um, it's you and I can be lumped in the same in the same bucket, for example, based on a few attributes that common, UM, data elements or data points, which is problematic also because the resulting models produce are imprecise. However, if you look at an individual's medical records, for example, you know a diabetic type two diabetic patients there, if you look at the entire history and from all this information coming to them, whether it's doctor knows medication dosages, which line of treatment the second line treatment, uh, continuous monitoring of glucose and that sort of thing is over hundreds. You know, there are hundreds of thousands of data points in their entire medical history, but none of this is used today. At the point of care on. You want all this information to be organized, aggregated, structured in a way that you will be able to build even better models easily queried this information, aan den observed the health of the individual in comparison with the rest of the population because at that point you'll be able to make those personalized decisions and then also for patient engagement with the health lake ability to now emit data back on dshea air securely the a p i s that conform to the fire standard. So third party applications can be built also, um, Thio provide the access whether that's for analytics or digital health application, for example, a patient accident, that information all that is very, very, very important. Because ultimately you wanna, um, get at better care of these these populations better. In Roma, clinical trials reduce duplicative tests and waste and health care systems. All that comes when you have your entire information available in a way that structured and normalize on be able to Korean and analyze andan the seamless integration between the health lake and the arrest of the services like Amazon sage maker. You can really start to understand relationships and meaning of the information, build better, better decision support models and predictive models at the individual on a population level. >>Yeah, right. You talked about all this data that's not not really used on. It's because it's not accessible. I presume it's not in in one place that somebody can analyze its not standardized. It's not normalized. Uh, is that >>right, that is the biggest. That is the biggest challenge for every healthcare provider, pair or life science organization today. If you look at this data, it's difficult to work with. Medical health. Data is really different that I siloed spread out across multiple systems, and it's sort of not incompatible formats. If you look at the last decade, I mean, one of the greatest things is we witnessed a great transformation healthcare towards digitization of the record. But your data is scattered across many of these systems anywhere from found your family history, the clinical observation, diagnosis and treatment. When you see the vast majority of that data is contained in unstructured medical records like Dr Notes P. D efs of insurance, um, of laboratory reports or insurance claims and forms with the With With Covad, we've seen in quite a bit of uptake of digital sort of, um uh, delivery of care such as telemedicine and recorded audios and videos, X rays and images, uh, the large propagation of digital health, APS and and digital assistances and on and wearables and as well as these sort of monitors like glucose, monitor or not, data come in all shapes and form and form and start across all these things. It's a huge heavy lift for any health care organization to be able to aggregate normalized stored securely on. Then also be able to kind of analyze this information and structure in a way that zizi to scale. Um uh, with regards, Thio, the kind of problems that you're going after. >>Well, Dr Cox, who We have to leave it there. Thank you so much. I have been saying for years in the Cube. When is it? That machine's gonna be able to make it make better diagnoses than doctors. Maybe that's the wrong question. Maybe it's machines helping doctors make faster and more accurate diagnoses and lowering our costs. Thanks so much for coming. >>Thank you very much. Appreciate it. Thank you. >>Thank you for watching everybody keep it right there. This is Dave Volonte. We'll be back with more coverage of aws reinvent 2020. You virtual right after this short break

>>from around the >>globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel and AWS. Yeah, >>Hi. We are the Cube live covering AWS reinvent 2020. I'm Lisa Martin, and I've got one of our cube alumni back with me. Mike Miller is here. General manager of A W s AI Devices at AWS. Mike, welcome back to the Cube. >>Hi, Lisa. Thank you so much for having me. It's really great to join you all again at this virtual reinvent. >>Yes, I think last year you were on set. We have always had to. That's at reinvent. And you you had the deep race, your car, and so we're obviously socially distance here. But talk to me about deepracer. What's going on? Some of the things that have gone on the last year that you're excited >>about. Yeah, I'd love to tell. Tell you a little bit about what's been happening. We've had a tremendous year. Obviously, Cove. It has restricted our ability to have our in person races. Eso we've really gone gone gangbusters with our virtual league. So we have monthly races for competitors that culminate in the championship. Um, at reinvent. So this year we've got over 100 competitors who have qualified and who are racing virtually with us this year at reinvent. They're participating in a series of knockout rounds that are being broadcast live on twitch over the next week. That will whittle the group down to AH Group of 32 which will have a Siris of single elimination brackets leading to eight finalists who will race Grand Prix style five laps, eight cars on the track at the same time and will crown the champion at the closing keynote on December 15th this year. >>Exciting? So you're bringing a reinforcement, learning together with with sports that so many of us have been missing during the pandemic. We talked to me a little bit about some of the things that air that you've improved with Deep Racer and some of the things that are coming next year. Yeah, >>absolutely so, First of all, Deep Racer not only has been interesting for individuals to participate in the league, but we continue to see great traction and adoption amongst big customers on dare, using Deep Racer for hands on learning for machine learning, and many of them are turning to Deep Racer to train their workforce in machine learning. So over 150 customers from the likes of Capital One Moody's, Accenture, DBS Bank, JPMorgan Chase, BMW and Toyota have held Deep Racer events for their workforces. And in fact, three of those customers Accenture, DBS Bank and J. P. Morgan Chase have each trained over 1000 employees in their organization because they're just super excited. And they find that deep racers away to drive that excitement and engagement across their customers. We even have Capital one expanded this to their families, so Capital One ran a deep raise. Their Kids Cup, a family friendly virtual competition this past year were over. 250 Children and 200 families got to get hands on with machine learning. >>So I envisioned some. You know, this being a big facilitator during the pandemic when there's been this massive shift to remote work has have you seen an uptick in it for companies that talking about training need to be ableto higher? Many, many more people remotely but also train them? Is deep Racer facilitator of that? Yeah, >>absolutely. Deep Racer has ah core component of the experience, which is all virtualized. So we have, ah, console and integration with other AWS services so that racers can participate using a three d racing simulator. They can actually see their car driving around a track in a three D world simulation. Um, we're also selling the physical devices. So you know, if participants want to get the one of those devices and translate what they've done in the virtual world to the real world, they can start doing that. And in fact, just this past year, we made our deep race or car available for purchase internationally through the Amazon Com website to help facilitate that. >>So how maney deep racers air out there? I'm just curious. >>Oh, thousands. Um, you know, And there what? What we've seen is some companies will purchase you, know them in bulk and use them for their internal leagues. Just like you know, JP Morgan Chase on DBS Bank. These folks have their own kind of tracks and racers that they'll use to facilitate both in person as well as the virtual racing. >>I'm curious with this shift to remote that we mentioned a minute ago. How are you seeing deepracer as a facilitator of engagement. You mentioned engagement. And that's one of the biggest challenges that so Maney teams develops. Processes have without being co located with each other deep Brister help with that. I mean, from an engagement perspective, I think >>so. What we've seen is that Deep Racer is just fun to get your hands on. And we really lower the learning curve for machine learning. And in particular, this branch called reinforcement Learning, which is where you train this agent through trial and error toe, learn how to do a new, complex task. Um, and what we've seen is that customers who have introduced Deep Racer, um, as an event for their employees have seen ah, very wide variety of employees. Skill sets, um, kind of get engaged. So you've got not just the hardcore deep data scientists or the M L engineers. You've got Web front end programmers. You even have some non technical folks who want to get their hands dirty. Onda learn about machine learning and Deep Racer really is a nice, gradual introduction to doing that. You can get engaged with it with very little kind of coding knowledge at all. >>So talk to me about some of the new services. And let's look at some specific use case customer use cases with each service. Yeah, >>absolutely. So just to set the context. You know, Amazon's got hundreds. A ws has hundreds of thousands of customers doing machine learning on AWS. No customers of all sizes are embedding machine learning into their no core business processes. And one of the things that we always do it Amazon is We're listening to customers. You know, 90 to 95% of our road maps are driven by customer feedback. And so, as we've been talking to these industrial manufacturing customers, they've been telling us, Hey, we've got data. We've got these processes that are happening in our industrial sites. Um, and we just need some help connecting the dots like, how do we really most effectively use machine learning to improve our processes in these industrial and manufacturing sites? And so we've come up with these five services. They're focused on industrial manufacturing customers, uh, two of the services air focused around, um, predictive maintenance and, uh, the other three services air focused on computer vision. Um, and so let's start with the predictive maintenance side. So we announced Amazon Monitor On and Amazon look out for equipment. So these services both enable predictive maintenance powered by machine learning in a way that doesn't require the customer to have any machine learning expertise. So Mono Tron is an end to end machine learning system with sensors, gateway and an ML service that can detect anomalies and predict when industrial equipment will require maintenance. I've actually got a couple examples here of the sensors in the gateway, so this is Amazon monitor on these little sensors. This little guy is a vibration and temperature sensor that's battery operated, and wireless connects to the gateway, which then transfers the data up to the M L Service in the cloud. And what happens is, um, the sensors can be connected to any rotating machinery like pump. Pour a fan or a compressor, and they will send data up to the machine learning cloud service, which will detect anomalies or sort of irregular kind of sensor readings and then alert via a mobile app. Just a tech or a maintenance technician at an industrial site to go have a look at their equipment and do some preventative maintenance. So um, it's super extreme line to end to end and easy for, you know, a company that has no machine learning expertise to take advantage of >>really helping them get on board quite quickly. Yeah, >>absolutely. It's simple tea set up. There's really very little configuration. It's just a matter of placing the sensors, pairing them up with the mobile app and you're off and running. >>Excellent. I like easy. So some of the other use cases? Yeah, absolutely. >>So So we've seen. So Amazon fulfillment centers actually have, um, enormous amounts of equipment you can imagine, you know, the size of an Amazon fulfillment center. 28 football fields, long miles of conveyor belts and Amazon fulfillment centers have started to use Amazon monitor on, uh, to monitor some of their conveyor belts. And we've got a filament center in Germany that has started using these 1000 sensors, and they've already been able to, you know, do predictive maintenance and prevent downtime, which is super costly, you know, for businesses, we've also got customers like Fender, you know, who makes guitars and amplifiers and musical equipment. Here in the US, they're adopting Amazon monitor on for their industrial machinery, um, to help prevent downtime, which again can cost them a great deal as they kind of hand manufacture these high end guitars. Then there's Amazon. Look out for equipment, which is one step further from Amazon monitor on Amazon. Look out for equipment. Um provides a way for customers to send their own sensor data to AWS in order to build and train a model that returns predictions for detecting abnormal equipment behavior. So here we have a customer, for example, like GP uh, E P s in South Korea, or I'm sorry, g S E P s in South Korea there in industrial conglomerate, and they've been collecting their own data. So they have their own sensors from industrial equipment for a decade. And they've been using just kind of rule basic rules based systems to try to gain insight into that data. Well, now they're using Amazon, look out for equipment to take all of their existing sensor data, have Amazon for equipment, automatically generate machine learning models on, then process the sensor data to know when they're abnormalities or when some predictive maintenance needs to occur. >>So you've got the capabilities of working with with customers and industry that that don't have any ML training to those that do have been using sensors. So really, everybody has an opportunity here to leverage this new Amazon technology, not only for predicted, but one of the things I'm hearing is contact list, being able to understand what's going on without having to have someone physically there unless there is an issue in contact. This is not one of the words of 2020 but I think it probably should be. >>Yeah, absolutely. And in fact, that that was some of the genesis of some of the next industrial services that we announced that are based on computer vision. What we saw on what we heard when talking to these customers is they have what we call human inspection processes or manual inspection processes that are required today for everything from, you know, monitoring you like workplace safety, too, you know, quality of goods coming off of a machinery line or monitoring their yard and sort of their, you know, truck entry and exit on their looking for computer vision toe automate a lot of these tasks. And so we just announced a couple new services that use computer vision to do that to automate these once previously manual inspection tasks. So let's start with a W A. W s Panorama uses computer vision toe improve those operations and workplace safety. AWS Panorama is, uh, comes in two flavors. There's an appliance, which is, ah, box like this. Um, it basically can go get installed on your network, and it will automatically discover and start processing the video feeds from existing cameras. So there's no additional capital expense to take a W s panorama and have it apply computer vision to the cameras that you've already got deployed, you know, So customers are are seeing that, um, you know, computer vision is valuable, but the reason they want to do this at the edge and put this computer vision on site is because sometimes they need to make very low Leighton see decisions where if you have, like a fast moving industrial process, you can use computer vision. But I don't really want to incur the cost of sending data to the cloud and back. I need to make a split second decision, so we need machine learning that happens on premise. Sometimes they don't want to stream high bandwidth video. Or they just don't have the bandwidth to get this video back to the cloud and sometimes their data governance or privacy restrictions that restrict the company's ability to send images or video from their site, um, off site to the cloud. And so this is why Panorama takes this machine learning and makes it happen right here on the edge for customers. So we've got customers like Cargill who uses or who is going to use Panorama to improve their yard management. They wanna use computer vision to detect the size of trucks that drive into their granaries and then automatically assign them to an appropriately sized loading dock. You've got a customer like Siemens Mobility who you know, works with municipalities on, you know, traffic on by other transport solutions. They're going to use AWS Panorama to take advantage of those existing kind of traffic cameras and build machine learning models that can, you know, improve congestion, allocate curbside space, optimize parking. We've also got retail customers. For instance, Parkland is a Canadian fuel station, um, and retailer, you know, like a little quick stop, and they want to use Panorama to do things like count the people coming in and out of their stores and do heat maps like, Where are people visiting my store so I can optimize retail promotions and product placement? >>That's fantastic. The number of use cases is just, I imagine if we had more time like you could keep going and going. But thank you so much for not only sharing what's going on with Deep Racer and the innovations, but also for show until even though we weren't in person at reinvent this year, Great to have you back on the Cube. Mike. We appreciate your time. Yeah, thanks, Lisa, for having me. I appreciate it for Mike Miller. I'm Lisa Martin. You're watching the cubes Live coverage of aws reinvent 2020.

>> Narrator: From around the globe. It's theCUBE with digital coverage of A New Era In Database Management. Brought to you by Nutanix. >> Hi, I'm Stu Miniman. And welcome to this special presentation with Nutanix. We're talking about A New Era In Database Management. To help us dig into it, first of all, I have the Senior Vice President and General Manager of Nutanix Era Databases and Business Critical Applications, that is Bala Kuchibhotla. And one of our other CUBE alongs, Monica Kumar. Who's an SVP also with Nutanix. Bala, Monica, thank you so much for joining us. >> Thank you, thank you so... >> Great to be here. All right, so first of all, Bala a new Era. We, have a little bit of a punj. You've got me with some punjs there. Of course we know that the database for Nutanix solution is Era. So, we always like to bring out the news first. Why don't you tell us, what does this mean? What is Nutanix announcing today? >> Awesome. Thank you, Stu. Yeah, so today's a very big day for us. I'm super excited to inform all of us and our audience that we are announcing the Eratory dot two GA bits for customers to enjoy it. Some customers can download and start playing with it. So what's new with Nutanix Eratory dot two? As you knows 1.0 is a single cluster solution meaning the customers have to have a Nutanix cluster and then have around the same cluster to enjoy the databases. But with Eratory dot two, it becomes multi-cluster solution. It's not just a multi-cluster solution, but customers can enjoy database across clusters, That means that they can have their Always On Availability Groups SQL servers, their Postgres servers across Nutanix clusters. That means that they can spread across Azure Availability Zones. Now, the most interesting point of this is, it's not just across clusters, customers can place these clusters in the cloud. That is AWS. You can have Nutanix cluster in the AWS cluster and then the primary production clusters maybe on the Nutanix and primary enterprise cloud kind of stuff, that's number one. Number two, we have extended our data management capabilities, data management platform capabilities, and what we call them as global time mission. Global time mission with a data access management. Like racing river, that you need to harness the racing river by constructing a dam and then harness it for multipurpose either irrigation projects or hydroelectric project kind of stuff. You need to kind of do the similar things for your data in company, enterprise company. You need to make sure that the right persons get the right amount of data, so that you don't kind of give all production data to everyone in the company. At the same time, they also need the accessible, with one click they can get the database, the data they want. So that's the data access management. Imagine a QA person only gets the sanitized snapshots or sanitize database backups for them to create the copies. And then we are extending our database engine portfolios too to introduce SAP HANA to the thing. As you know, that we support Oracle today, Postgres, MalSQL, Mariadb SQL server. I'm excited to inform that we are introducing SAP HANA. Our customers can do one click sandbox creation into an environment for SAP HANA predown intense platform. And lastly, I'm super excited to inform that we are becoming a Postgres vendor. We are willing to give 24 by seven, 365 day support but Postgres database engine, that's kind of a provision through Nutanix setup platform. So this way the customers can enjoy the engine, platform, service all together in one single shot with a single 180 company that they can call and get the support they want. I'm super duper excited that this is going to make the customers a truly multicloud multi cluster data management platform. Thank you. >> Yeah. And I'll just add to that too. It's fantastic that we are now offering this new capability. I just want to kind of remind our audience that Nutanix for many years has been providing the foundation the infrastructure software, where you can run all these multiple workloads including databases today. And what we're doing with Era is fantastic because now they are giving our customers the ability to take that database that they run on top of Nutanix to provide that as a service now. So now are talking to a whole different organization here. It's database administrations, it's administrators, it's teams that run databases, it teams that care about data and providing access to data and organizations. >> Well, first of all, congratulations, I've taught for a couple of years to the teams at Nutanix especially some of the people working on PostgreSQL really exciting stuff and you've both seen really the unlocking of database. It used to be ,we talked about, I have one database it's kind of the one that everything runs on. Now, customers they have more databases. You talked about that flexibility is then, where we run it. We'd love to hear, maybe Monica we start with you. You talk about the customers, what does this really mean for them? Because one of our most mission critical applications we talk about, we're not just throwing our databases or what. I don't wake up in the morning and say, Oh let me move it to this cloud and put it in this data center. This needs to be reliable. I need to have access to the data. I need to be able to work with it. So, what does this really mean? And what does it unlock for your customers? >> Yes absolutely, I love to talk about this topic. I mean, if you think about databases, they are means to an end. And in this case, the end is being able to mine insights from the data and then make meaningful decisions based on that. So when we talk to customers, it's really clear that data has not become one of the most valuable assets that an organization owns. Well, of course, in addition to the employees that are part of the organization and our customers. Data is one of the most important assets. But most organizations, the challenges they face is a lot of data gets collected. And in fact, we've heard numbers thrown around for many years like, almost 80% of world's data has been created in the last like three or four years. And data is doubling every two years in terms of volume. Well guess what? Data gets collected. It sits there and organizations are struggling to get access to it with the right performance, the right security and regulation compliance, the reliability, availability, by persona, developers need certain access, analysts needs different access line of businesses need different access. So what we see is organizations are struggling in getting access to data at the right time by the right person on the team and when they need it. And I think that's where database as a service is critical. It's not just about having the database software which is of course important but how you know not make that service available to your stakeholders, to developers to lines of business within the SLAs that they demand. So is it instantly? How quickly can you make it available? How quickly can you use have access to data and do something meaningful with it? And mind the insights for smarter business? And then the one thing I'd like to add is that's where IT and business really come together. That's the glue. If you think about it today, what is the blue between an IT Organization and a business organization? It's the data. And that's where they're really coming together to say how can we together deliver the right service? So you, the business owner can deliver the right outcome for our business. >> That's very true. Maybe I'll just add a couple of comments there. What we're trying to do is we are trying to bring the cloud experience, the RDS-like experience to the enterprise cloud and then hybrid cloud. So the customers will now have a choice of cloud. They don't need to be locked in a particular cloud, at the same time enjoy the true cloud utility experience. We help customers create clouds, database clouds either by themselves if that's big enough to manage the cloud themselves or they can partner with a GSIs like Wipro, WorkHCL and then create a completely managed database service kind of stuff. So, this brings this cloud neutrality, portability for customers and give them the choice and their terms, Stu. >> Well Bala, absolutely we've seen a huge growth in managed services as you've said, maybe bring us inside a little bit. What is free up customers? What we've said for so long that back when HCI first started, it was some of the storage administrators might bristle because you were taking things away from them. It was like, no, we're going to free you up to do other things that as Monica said, deliver more business value not mapping LUNs and doing that. How about from the DBA standpoint? What are some of those repetitive, undifferentiated heavy lifting that we're going to take away from them so that they can focus on the business value. >> Yep. Thank you Stu. So think about this. We all do copy paste operations in laptops. Something of that sort happens in data center at a much larger scale. Meaning that the same kind of copy paste operation happens to databases and petabytes and terabytes of scale. Hundreds of petabytes. It has become the most dreaded complex, long running error prone operation. Why should it be that way? Why should the DBS spend all this mundane tasks and then get busy for every cloning operation? It's a two day job for me, every backup job. It's like a hobby job for provisioning takes like three days. We can take this undifferentiated heavy lifting by this and then let the DBS focus on designing the cloud for them. Looking for the database tuning, design data modeling, ML aspects of the data kind of stuff. So we are freeing up the database Ops people, in a way that they can design the database cloud, and make sure that they are energy focused on high valid things and more towards the business center kind of stuff. >> Yeah. And you know automation is really important. You were talking about is automating mundane grunt work. Like IT spends 80% of its time in maintaining systems. So then where is the time for innovation. So if we can automate stuff that's repetitive, stuff that the machine can do, the software can do, why not? And I think that's what our database as a service often does. And I would add this, the big thing our database as a service does really is provide IT organizations and DV organizations a way to manage heterogeneous databases too. It's not like, here's my environment for Postgres. Here's my environment for my SQL. Here's my environment for Oracle. Here's my environment for SQL server. Now with a single offering, a single tool you can manage your heterogeneous environment across different clouds. On premises cloud, or in a public cloud environment. So I think that's the beauty we are talking about with Nutanix's Era. Is a truly, truly gives organizations that single environment to manage heterogeneous databases, apply the same automation and the ease of management across all these different environments. >> Yeah. I'll just add one comment to that. A true managed PaaS obviously customers in like a single shop go to public cloud, just click through and then they get the database and point. And then if someone is managing the database for them. But if you look at the enterprise data centers, they need to bring that enterprise GalNets and structure to these databases. It's not like anyone can do anything to any or these databases. So we are kind of getting the best of both, the needed enterprise GalNets by these enterprise people at the same time bringing the convenience for the application teams and developers they want to consume these databases like utility. So bringing the cloud experience, bringing the enterprise GalNets. At same time, I'm super confident we can cut down the cost. So that is what Nutanix Era is all about across all the clouds, including the enterprise cloud. >> Well, Bala being simpler and being less expensive are one of the original promises of the cloud that don't necessarily always come out there. So, that's super important. One of the other things, you talk about these hybrid environments. It's not just studied, in the public cloud want to understand these environments, if I'm in the public cloud, can I still leverage some of the services that are in the public cloud? So, if I want to run some analytics, if I want to use some of the phenomenal services that are coming out every day. Is that something that can be done in this environment? >> Yeah, beautiful. Thank you Stu. So we are seeing customers who two categories. There is a public cloud customer, completely born in public cloud cloud, native services. They realize that for every database that maintaining five or seven different copies and the management of these copies is prohibited just because every copy is a faulty copy in the public cloud. Meaning you take a backup snapshot and restore it. Your meter like New York taxi, it starts with running for your EBS   and that you are looking at it kind of stuff. So they can leverage Nutanix clusters and then have a highly efficient cloning capability so that they can cut down some of these costs for these secondary environments that I talk about. What we call is copy data management, that's one kind of use case. The other kind of customers that we are seeing who's cloud is a phenomenon. There's no way that people have to move to cloud. That's the something at a C level mandate that happens. These customers are enjoying their database experience on our enterprise cloud. But when they try to go to these big hyperscalers, they are seeing the disconnect that they're not able to enjoy some of the things that they are seeing on the enterprise cloud with us. So this transition, they are talking to us. Can you get this kind of functionality with Nutanix platform onto some of these big hyperscalers? So there are kind of customers moving both sides, some customers that are public cloud they're time to enjoy our facilities other than copy data management and Nutanix. Customers that are on-prem but they have a mandate to good public cloud ,with our hybrid cloud strategy. They get to enjoy the same kind of convenience that they are seeing it on enterprise and bringing the same kind of governance that they used to do it. so that maybe see customers. Yeah. >> Yeah. Monica, I want to go back to something you talked about customers dealing with that heterogeneous environment that they have reminds me of a lot of the themes that we talked about at nutanix.next because customers have they have multiple clouds they're using, requires different skillsets, different tooling. It's that simplicity layer that Nutanix has been working to deliver since day one. What are you from your customers? How are they doing with this? And especially in the database world. What are some of those challenges that they're really facing that we're looking to help solve with the solution today. >> Yeah. I mean, if you think about it, what customers at least in our experience, what they want or what they're looking for is this modern cloud platform that can really work across multiple cloud environments. Cause people don't want to change running, let's say an Oracle database you're on-prem on a certain stack and then using a whole different stack to run Oracle database in the cloud. What they want is the same exact foundation. So be so they can be, for sure have the right performance. Availability, reliability, the applications don't have to be rewritten on top of Oracle database. They want to preserve all of that, but they want the flexibility to be able to run that cloud platform wherever they choose to. So that's one. So that's choosing the right and modernizing and choosing the right cloud platform is definitely very important to our customers, but you nailed it on the head Stu. It's been about how do you manage it? How do you operate it on a daily basis? And that's where our customers are struggling with multiple types of tools out there, custom tool for every single environment. And that's what they don't want. They want to be able to manage, simply across multiple environments using the same tools and skillsets. And again, and I'm going to beat the same drum, but that's when Nutanix shines. That's a design principle is. It's the exact same technology foundation that you provide to customers to run any applications. In this case it happens to be databases. Exact same foundation you can use to run databases on-prem in the cloud. And then on top of that using Era boom! Simple management, simple operations, simple provisioning simple copy data management, simple patching, all of that becomes easy using just a single framework to manage and operate. And I will tell you this, when we talk to customers, what is it that DBS and database teams are struggling with? They're struggling with SLS and performance on scalability, that's one, number two they're struggling with keeping it up and running and fulfilling the demands of the stakeholders because they cannot keep up with how many databases they need to keep provisioning and patching and updating. So at Nutanix now we are actually solving both those problems with the platform. We are solving the problem of a very specific SLA that we can deliver in any cloud. And with Era, you're solving the issue of that operational complexity. We're making it really easy. So again, IT stakeholders DBS can fulfill the demands of the business stakeholders and really help them monetize the data. >> Yeah. I'll just add on with one concrete examples too. Like we have a big financial customer, they want to run Postgres. They are looking at the public cloud. Can we do a manage services kind of stuff, but you look at this, that the cost difference between a Postgres and your company infrastructure versus managed services almost like $3X to $4X dollars. Now, with Nutanix platform and Era, we were able to show that they can do at much reduced cost, manage their best service experience including their DBA cost are including the cloud administration cost. Like we added the infrastructure picture. We added the people who are going to manage the cloud, internal cloud and then intern experience being, plus plus of what they can see to public cloud. That's what makes the big difference. And this is what data sovereignty, data control, compliance and infrastructure governance, all these things coupled with cloud experiences, what customers really see the value of Era and the enterprise cloud and with an extension to the public cloud, with our hybrid cloud strategy. if they want to move this workload to public cloud they can do it. So, today with AWS clusters and tomorrow with our Azure clusters. So that gives them that kind of insurance not getting locked in by a big hyperscaler, but at same time enjoy the cloud experience. That's what big customers are looking for. >> Alright Bala, all the things you laid out here, what's the availability of Era rotically dot two? >> Era rotically dot two is actually available today. The customers can enjoy download the bits. We already have bunches of beta customers who are trying it out with the recall big telco companies are financial companies, and even big companies that manage big pensions kind of stuff. Let's talk about that kind of stuff. People are looking to us. In fact, there are customers who are looking for, when is this available for Azure cluster so that we can move some of our workloads to and manage the databases in Azure classes. So it is available and I'm looking forward to great feedback from our customers. And I'm hoping that it will solve some of their major critical problems. And in the process they get the best of Nutanix. >> Monica, last question I have for you. This doesn't seem like it's necessarily the same traditional infrastructure go to market for a solution like this. If I think back to, people think of HCI it was like, Oh! well, it was kind of a new box. We know Nutanix is a software company. More of what you do today is subscription based. So, maybe if you could talk a little bit to just how Nutanix goes to market with a solution like this. >> Yeah. And you know what, maybe people don't realize it but I'm hoping a lot of people do that. Nutanix is not just an infrastructure company anymore. In the last many years we've developed a full cloud platform in not only do we offer the infrastructure services with hyperconverged infrastructure which is now really the foundation. It's the hybrid cloud infrastructure. As you know, Stu, we talked to you a month ago and we talked about the evolution of XCI to really becoming the hybrid cloud infrastructure. But in addition to that, we also offer other data center services on storage DR Networking. We also offer DevOps services with application provisioning automation, application orchestration and then of course, database services that we talking about today and we offer desktop services. So Nutanix has really evolved in the last few years to a complete cloud platform really focusing on the application and workloads that run on top of the infrastructure stack. So not just the infrastructure layer but how can we be the best platform to run your databases? Your end is the computing workloads, your analytics applications your enterprise applications, cloud native applications. So that's what this is. And databases is one of our most successful workloads that's that runs a Nutanix very well because of the way the infrastructure software is architected. Because it's really great to scale high performance because again our superior architecture. And now with Era, it's a tool, it's all in one. Now it's also about really simplifying the management of databases and delivering them speedily and with agility to drive innovation in the organizations. >> Yep. Thank you Monica. Thank you. I I'll just add a couple of lines of comments into that. DTM for databases as erotically dots two, is going to be a challenge. And historically we are seen as an infrastructure company but the beauty of databases is so and to send to the infrastructure, the storage. So the language slightly becomes easy. And in fact, this holistic way of looking at solving the problem at the solution level rather than infrastructure helps us to go to a different kind of buyer, different kinds of decision maker, and we are learning. And I can tell you confidently the kind of progress that we have seen for in one enough year, the kind of customers that we are winning. And we are proving that we can bring a big difference to them. Though there is a challenge of DTM speaking the language of database, but the sheer nature of cloud platform the way they are a hundred hyperscale work. That's the kind of language that we take. You can run your solution. And here is how you can cut down your database backup time from hours to less than minute. Here's how you can cut down your patching from 16 hours to less than one hour. It is how you can cut down your provisioning time from multiple weeks to let them like matter of minutes. That holistic way of approaching it coupled with the power of the platform, really making the big difference for us. And I usually tell every time I meet, can you give us an opportunity to cut down your database cost, the PC vote, total cost of operations by close to 50%? That gets them excited that lets then move lean in and say, how do you plan to do it? And then we go about how do we do it? And we do a deep dive and PC people and all of it. So I'm excited. I think this is going to be a big play for Nutanix. We're going to make big difference. >> Absolutely well, Bala, congratulations to the team. Monica, both of you thank you so much for joining, really excited for all the announcements. >> Thank you so much. >> Thank you >> Stay with us. We're going to dig in a little bit more with one more interview for this product launch of the New Era and Database Management from Nutanix. I'm Stu Minimam as always, thank you for watching theCUBE. (cool music)

>>from the Cube Studios in Palo Alto and Boston. It's the Cube covering empowering the autonomous enterprise brought to you by Oracle Consulting. >>Welcome to the special digital presentation where we're tracking the rebirth of Oracle Consulting. And my name is Dave Vellante. And we're here with Aaron Millstone. Who's the senior vice president of Oracle Consulting? Aaron, thanks for coming on. Good to talk to you. >>They appreciate you having me, and I like the introduction of the river. >>Well, it really is. I mean, you know, you guys have gone from staff augmentation to being much more of a strategic partner, and we're gonna talk about that. But I want to start with this team that you have about empowering the autonomous enterprise. It sounds good, you know, nice little marketing tag line, but give us give us what's behind that. Put some meat on the bone. >>Sure, So you know what we define is that time in this enterprise is really using artificial intelligence, using machine learning and using it to cognitively understand your actual data and processes using for enterprise and then really embedding that into everything you're doing as a company and using both Dr optimization and costs and increasing revenue. And I know that's a lot of kind of consulting to speak. So you know, we tend to think about and we've been talking about in terms of what we call try motile i t. And it's Ah, this is probably the most exciting space that I've really thought through with with my team as we build up a new consulting business, you pointed out. But this is really about pivoting away from, you know, the systems of record and the systems of interaction, and really building up the systems of intelligence capabilities that we see it all enterprises needing to invest in heavily if they're not already investing there early. >>Well, I want I want talk about a couple of things there. One is that notion of lowering costs and increasing revenue. And you're right, people say, Oh, yeah, that's insulting Speak. But good consultant digs in. It starts peeling the onion. Well, how do you How do you actually make money? You know, where are the inefficiencies in your business? And that's really what you're talking about. And that's what every business wants to know, right? Is is not. That's the end game. But the how to is really what separates the good consultants from the pack. >>Yeah, right. And we're again. We're on this journey now. We've been, you know, you mentioned it right where I'm two years into Oracle consulting myself. I spent 23 plus years at Accenture when I was a managing director with them in part of their North America leadership team. When I came over to work something we did, we pivoted from what you called staff augmentation business to a basic set of offerings which were things that you recognize right migration, services of workloads to cloud or integration of security where you know, even ah ah ah It has for SAS augmentation that we would do But, you know, pretty basic services. We're now pivoting again into sort of two areas. Infrastructure led transformation, which is really our old costs Take out play a Z You just said and sort of good consultants know how to do that. And really, what that is is we're going. I'm looking at companies that still have traditional data centers. Or maybe they've got some things on clouds and some things that still in traditional data centers and we're coming in and we're saying there's a business case here. It looks at your total cost of ownership, and we think we can take out between 40 and 65% of your run rate bus. And that's everything from, you know, facilities, fire suppression systems through to the actual compute costs through to the labor that's required to, you know, do the physical hands on activities in the data center. Right. So, you know, we have that sort of capability, and we're pushing customers hard in that space at the moment. And then driving that into a secondary conversations is, And by the way, with all these savings, you kind of have two choices, right? You can pocket the savings, obviously, or right, we would propose that you go into what we're calling the condoms enterprise space and really building up your artificial intelligence machine learning capability with centralized capabilities. Central as data versus letting every line of business every department do it >>on their own. >>So to me. But let me ask you, does that make sense? But why your cloud? You were sort of ah later entrance into cloud. So where does cloud fit into this How do you respond to a customer? Say Yeah, but you know, you guys relate. Well, >>yeah, we are. We are definitely late coming to cloud, right? There's no no two ways about it. I mean, what we to what we've got is we have what we call a generation too. And, you know, I jokingly tell customers that we have a late mover advantage and that late mover advantage basically means that we've looked at what the first generation clouds have done and, quite frankly, there they're great at what they do. They're fierce competitors. They're tough to compete with. They've got a lot of mindshare, but they fundamentally we're about targeting consumers or targeting enterprise collaboration tools. Right? So if you want cat videos, if you want to watch you humorous videos that people filmed and posted on social media was a great clouds for that stuff. But if you want really mission critical enterprise cloud workloads, right? That's where we come into play. And so when you start to look at really the key differentiators in our cloud and you know fraud, these this is how I describe it, asked me. Right. So you know, we look at sort of three layers. We have an autonomous capability, both in our operating system or database. What that basically means is that we have machine learning and artificial intelligence that's driving the key, you know, administrative activities in our cloud. We then have our exit data platform. So exit data for us is a secret weapon, right? We we think that it is a differentiator in our products. And so, you know, exit data for those for those watching that don't know what it is, right? So exit data emerged out of the sun acquisition that work well did. It is purpose built hardware that is engineered for our software products, specifically about databases. And now we've taken that concept and moved it into our cloud. And so, you know, customers can come in and take very intensive enterprise mission Critical workloads run him straight in our cloud. And then, you know, when we look at the last point, it's probably security. Where again we have total segmentation of our security layers that from the customer workloads, right. So again, we've we've taken the concepts that first generation cloud providers have implemented, and they scale it globally. So it's really tough for them to walk back on it. It's a huge investment and we're have gone into a generation to cloud, and quite frankly, that's I think that's what this is, the frontier that you know. Everyone's racing to kind of crack, >>So we got to wrap. But I want to close on sort of the again. We're talking about good consultants and good consultants have continuous improvement mindset. They got a North Star that they really never get doing that that keeps moving because you got to keep innovating. You gotta keep disrupting yourself. So maybe you could end by sort of talking about some of the things you're watching, some of the milestones you want to hit and some of that transformation that you want to keep going. How are you gonna achieve that? >>Yeah, we'll get some of that. We hit the Deloitte segment too, right? But we're definitely we've moved from. We've definitely moved from, you know, the staff augmentation to offer it to basic offerings. We're now beyond that. We're starting to sell the infrastructure led transformation plays. What's exciting to me about that with our customers is you know, Oracle is a big complex and impress, as you'd expect with, you know, >>a >>company that has a tremendous amount of technology. We're now bringing holistic approaches to our customers. They let us help you optimize everything, and then let's look at your data center. That's not look at a narrow slice. Let's not look at just sys, admin and DBS. We're looking at things comprehensively, so moving there has been a pretty big milestone for us to hit. We started to get some good momentum with our customers. Our next milestone is really gonna be taking that on time center present, blowing it out. We're in use case, an incubation period right now with that, But again, we've got some. I would argue we have the best talent in the world right now that thinks about this stuff and not not just thinking about it from a pure technology stand point, but things about how to actually make it effective for the business. And so once we get some of those motions going >>to >>take the use case for the autonomous enterprise, that's artificial intelligence driven. It should have a continuous pace of change, and it's going to start to evolve in areas that. You know, quite frankly, we can't even predict yet, but we're excited to see where it leads. >>Well, thanks for spending some time with us. I am very excited to talk about that sort of collision course between your deep tech capabilities as Oracle as a product company and this global aside deployed, we're gonna We're gonna bring in those guys in a moment. But so thanks very much for taking us through the transformation and great job. Good luck. >>Thank you. Appreciate it. >>Alright. And thank you, everybody for watching. Keep it right there. We'll be back with more coverage of Oracle's transformation. Right after this short break, you're watching the cube?