(gentle music) >> Welcome to the new abnormal. Yes, you know, the pandemic, it did accelerate the shift to digital, but it's also created disorder in our world. I mean, every day it seems that companies are resetting their office reopening playbooks. They're rethinking policies on large gatherings and vaccination mandates. There's an acute labor shortage in many industries, and we're seeing an inventory glutton in certain goods, like bleach and hand sanitizer. Airline schedules and pricing algorithms, they're all unsettled. Is inflation transitory? Is that a real threat to the economy? GDP forecasts are seesawing. In short, the world is out of whack and the need for fast access to quality, trusted and governed data has never been greater. Can coherent data strategies help solve these problems, or will we have to wait for the world to reach some type of natural equilibrium? And how are companies, like Google, helping customers solve these problems in critical industries, like financial services, retail, manufacturing, and other sectors? And with me to share his perspectives on data is a long-time CUBE alum, Bruno Aziza. He's the head of data analytics at Google. Bruno, my friend, great to see you again, welcome. >> Great to see you, thanks for having me, Dave. >> So you heard my little narrative upfront, how do you see this crazy world of data today? >> I think you're right. I think there's a lot going on in the world of data analytics today. I mean, certainly over the last 30 years, we've all tried to just make the life of people better and give them access more readily to the information that they need. But certainly over the last year and half, two years, we've seen an amazing acceleration in digital transformation. And what I think we're seeing is that even after three decades of investment in the data analytics world, you know, the opportunity is still really out wide and is still available for organizations to get value out of their data. I was looking at some of the latest research in the market, and, you know, only 32% of companies are actually able to say that they get tangible, valuable insights out of their data. So after all these years, we still have a lot of opportunity ahead of us, of course, with the democratization of access to data, but also the advent in machine learning and AI, so that people can make better decisions faster than their competitors. >> So do you think that the pandemic has heightened that sort of awareness as they were sort of forced to pivot to digital, that they're maybe not getting enough out of their data strategies? That maybe their whatever, their organization, their technology, their way they were thinking about data was not adequate and didn't allow them to be agile enough? Why do you think that only 32% are getting that type of value? >> I think it's true. I think, one, digital transformation has been accelerated over the last two years. I think, you know, if you look at research the last two years, I've seen almost a decade of digital acceleration, you know, happening. But I also think that we're hitting a particular time where employees are expecting more from their employers in terms of the type of insights that can get. Consumers are now evolving, right? So they want more information. And I think now technology has evolved to a point where it's a lot easier to provision a data cloud environment so you can get more data out to your constituents. So I think the connection of these three things, expectation of employees, expectation of customers to better customer experiences, and, of course, the global environment, has accelerated quite a bit, you know, where the space can go. And for people like me, you know, 20 years ago, nobody really cared about databases and so forth. And now I feel like, you know, everybody's, you know, understands the value that we can get out of it. And we're kind of getting, you know, in the sexy territory, finally, data now is sexy for everyone and there's a lot of interest in the space. >> You and I met, of course, in the early days of Hadoop. And there were many things about Hadoop that were profound and, of course, many things that, you know, just were overly complex, et cetera. And one of the things we saw was this sort of decentralization. We thought that Hadoop was going to send five megabytes of code to petabytes of data. And what happened is everything, you know, came into this centralized repository and that centralized thinking, the data pipeline organization was very centralized. Are you seeing companies rethink that? I mean, has the cloud changed their thinking? You know, especially as the cloud expands to the edge, on-prem, everywhere. How are you seeing organizations rethink their regimes for data? >> Yeah, I think, you know, we've seen over the last three decades kind of the pendulum, right, from really centralizing everything and making the IT organization kind of the center of excellence for data analytics, all the way to now, you know, providing data as a self-service, you know, application for end-users. And I think what we're seeing now is there's a few forces happening. The first one is, of course, multicloud, right? So the world today is clearly multicloud and it's going to be multicloud for many, many years. So I think not only are now people considering their on-prem information, but they're also looking at data across multiple clouds. And so I think that is a huge force for chief data officers to consider is that, you know, you're not going to have data centralized in one place, nicely organized, because sometimes it's going to be a factor of where you want to be as an organization. Maybe you're going to be partnering with other organizations that have data in other clouds. And so you want to have an architecture that is modern and that accommodates this idea of an open cloud. The second problem that we see is this idea around data governance, intelligent data governance, right? So the world of managing data is becoming more complex because, of course, you're now dealing with many different speeds, you're dealing with many different types of data. And so you want to be able to empower people to get access to the information, without necessarily having to move this data, so they can make quick decisions on the data. So this idea of a data fabric is becoming really important. And then the third trend that we see, of course, is this idea around data sharing, right? People are now looking to use their own data to create a data economy around their business. And so the ability to augment their existing data with external data and create data products around it is becoming more and more important to the chief data officers. So it's really interesting we're seeing a switch from, you know, this chief data officer really only worried about governance, to this we're now worried about innovation, while making sure that security and governance is taken care of. You know, we call this freedom within the framework, which is a great challenge, but a great opportunity for many of these data leaders. >> You mentioned several things there. Self-service, multicloud, the governance key, especially if we can federate that governance in a decentralized world. Data fabric is interesting. I was talking to Zhamak Dehghani this weekend on email. She coined the term data mesh. And there seems to be some confusion, data mesh, data fabric. I think Gartner's using the term fabric. I know like NetApp, I think coined that term, which to me is like an infrastructure layer, you know. But what do you mean by data fabric? >> Well, the first thing that I would say is that it's not up to the vendors to define what it is. It really is up to the customer. The problem that we're seeing these customers trying to fix is you have a diversity of data, right? So you have data stored in the data mart, in a data lake, in a data warehouse, and they all have their specific, you know, reasons for being there. And so this idea of a data fabric is that without moving the data, can you, one, govern it intelligently? And, two, can you provide landing zones for people to actually do their work without having to go through the pain of setting up new infrastructure, or moving information left and right, and creating new applications? So it's this idea of basically taking advantage of your existing environment, but also governing it centrally, and also now providing self-service capabilities so people can do their job easily. So, you know, you might call it a data mesh, you might call it a data fabric. You know, the terminology to me, you know, doesn't seem to be the barrier. The issue today is how do we enable, you know, this freedom for customers? Because, you know, I think what we've seen with vendors out there is they're trying to just take the customer down to their paradigms. So if they believe in all the answers need to be in a data warehouse, they're going to guide the customer there. If they believe that, you know, everything needs to be in a data lake, they're going to guide the customer there. What we believe in is this idea of choice. You should be able to do every single use case. And we should be able to enable you to manage it intelligently, both from an access standpoint, as well as a governance standpoint. >> So when you think about those different, and I like that, you're making it somewhat technology agnostic, so whether it's a data warehouse, or a data lake, or a data hub, a data mart, those are nodes within the mesh or the fabric, right? That are discoverable, accessible, I guess, governed. I think that there's got to be some kind of centralized governance edict, but in a federated governance model so you don't have to move the data around. Is that how you're thinking about it? >> Absolutely, you know, in our recent event, in the Data Cloud Summit, we had Equifax. So the gentleman there was the VP of data governance and data fabric. So you can start seeing now these roles, you know, created around this problem. And really when you listen to what they're trying to do, they're trying to provide as much value as they can without changing the habits of their users. I think that's what's key here, is that the minute you start changing habits, force people into paradigms that maybe, you know, are useful for you as a vendor, but not so useful to the customer, you get into the danger zone. So the idea here is how can you provide a broad enough platform, a platform that is deep enough, so the data can be intelligently managed and also distributed and activated at the point of interaction for the end-user, so they can do their job a lot easier? And that's really what we're about, is how do you make data simpler? How do you make, you know, the process of getting to insight a lot more fluid without changing habits necessarily, both on the IT side and the business side? >> I want to get to specifics on what Google is doing, but the last sort of uber-trends I want to ask you about 'cause, again, we've known each other for a long time. We've seen this data world grow up. And you're right, 20, 30 years ago, nobody cared about database. Well, maybe 30 years ago. But 20 years ago, it was a boring market, right now it's like the hottest thing going. But we saw, you know, bromide like data is the new oil. Well, we found out, well, actually data is more valuable than oil 'cause you can use, you know, data in a lot of different places, oil you can use once. And then the term like data as an asset, and you said data sharing. And it brings up the notion that, you know, you don't want to share your assets, but you do want to share your data as long as it can be governed. So we're starting to change the language that we use to describe data and our thinking is changing. And so it says to me that the next 10 years, aren't going to be like the last 10 years. What are your thoughts on that? >> I think you're absolutely right. I think if you look at how companies are maturing their use of data, obviously the first barrier is, "How do I, as a company, make sure that I take advantage of my data as an asset? How do I turn, you know, all this information into a sustainable, competitive advantage, really top of mind for organizations?" The second piece around it is, "How do I create now this innovation flywheel so that I can create value for my customers, and my employees, and my partners?" And then, finally, "How do I use data as the center of a product that I can then further monetize and create further value into my ecosystem?" I think the piece that's been happening that people have not talked a lot about I think, with the cloud, what's come is it's given us the opportunity to think about data as an ecosystem. Now you and I are partnering on insights. You and I are creating assets that might be the combination of your data and my data. Maybe it's an intelligent application on top of that data that now has become an intelligent, rich experience, if you will, that we can either both monetize or that we can drive value from. And so I think, you know, it's just scratching the surface on that. But I think that's where the next 10 years, to your point, are going to be, is that the companies that win with data are going to create products, intelligent products, out of that data. And they're just going to take us to places that, you know, we are not even thinking about right now. >> Yeah, and I think you're right on. That is going to be one of the big differences in the coming years is data as product. And that brings up sort of the line of business, right? I mean the lines of business heads historically have been kind of removed from the data group, that's why I was asking you about the organization before. But let's get into Google. How do you describe Google's strategy, its approach, and why it's unique? >> You know, I think one of the reasons, so I just, you know, started about a year ago, and one of the reasons for why I found, you know, the Google mission interesting, is that it's really rooted at who we are and what we do. If you think about it, we make data simple. That's really what we're about. And we live that value. If you go to google.com today, what's happening? Right, as an end-user, you don't need any training. You're going to type in whatever it is that you're looking for, and then we're going to return to you highly personalized, highly actionable insights to you as a consumer of insights, if you will. And I think that's where the market is going to. Now, you know, making data simple doesn't mean that you have to have simple infrastructure. In fact, you need to be able to handle sophistication at scale. And so simply our differentiation here is how do we go from highly sophisticated world of the internet, disconnected data, changing all the time, vast volume, and a lot of different types of data, to a simple answer that's actionable to the end-user? It's intelligence. And so our differentiation is around that. Our mission is to make data simple and we use intelligence to take the sophistication and provide to you an answer that's highly actionable, highly relevant, highly personalized for you, so you can go on and do your job, 'cause ultimately the majority of people are not in the data business. And so they need to get the information just like you said, as a business user, that's relevant, actionable, timely, so they can go off and, you know, create value for their organization. >> So I don't think anybody would argue that Google, obviously, are data experts, arguably the best in the world. But it's interesting, some of the uniqueness here that I'm hearing in your language. You used the word multicloud, Amazon doesn't, you know, use that term. So that's a differentiation. And you sell a cloud, right? You sell cloud services, but you're talking about multicloud. You sell databases, but, of course, you host other databases, like Snowflake. So where do you fit in all this? Do you see your role, as the head of data analytics, is to sort of be the chef that helps combine all these different capabilities? Or are you sort of trying to help people adopt Google products and services? How should we think about that? >> Yeah, the best way to think about, you know, I spend 60 to 70% of my time with customers. And the best way I can think about our role is to be your innovation partner as an organization. And, you know, whichever is the scenario that you're going to be using, I think you talked about open cloud, I think another uniqueness of Google is that we have a very partner friendly, you know, approach to the business. Because we realized that when you walk into an enterprise or a digital native, and so forth, they already have a lot of assets that they have accumulated over the years. And it might be technology assets, but also might be knowledge, and know-how, right? So we want to be able to be the innovation vendor that enables you to take these assets, put them together, and create simplicity towards the data. You know, ultimately, you can have all types of complexity in the backend. But what we can do the best for you is make that really simple, really integrated, really unified, so you, as a business user, you don't have to worry about, "Where is my data? Do I need to think about moving data from here to there? Are there things that I can do only if the data is formatted that way and this way?" We want to remove all that complexity, just like we do it on google.com, so you can do your job. And so that's our job, and that's the reason for why people come to us, is because they see that we can be their best innovation partner, regardless where the data is and regardless, you know, what part of the stack they're using. >> Well, I want to take an example, because my example, I mean, I don't know Google's portfolio like you do, obviously, but one of the things I hear from customers is, "We're trying to inject as much machine intelligence into our data as possible. We see opportunities to automate." So I look at something like BigQuery, which has a strong affinity in embedded machine learning and machine intelligence, as an example, maybe of that simplification. But maybe you could pick up on that and give us some other concrete examples. >> Yeah, specifically on products, I mean, there are a lot products we can talk about, and certainly BigQuery has tremendous market momentum. You know, and it's really anchored on this idea that, you know, the idea behind BigQuery is that just add data and we'll do the rest, right? So that's kind of the idea where you can start small and you can scale at incredible, you know, volumes without really having to think about tuning it, about creating indexes, and so forth. Also, we think about BigQuery as the place that people start in order to build their ecosystem. That's why we've invested a lot in machine learning. Just a few years ago, we introduced this functionality called BigQuery Machine Learning, or BigQuery ML, if you're familiar with it. And you notice out of the top 100 customers we have, 80% of these customers are using machine learning right out of, you know, BigQuery. So now why is that? Why is it that it's so easy to use machine learning using BigQuery is because it's built in. It was built from the ground up. Instead of thinking about machine learning as an afterthought, or maybe something that only data scientists have access to that you're going to license just for narrow scenarios, we think about you have your data in a warehouse that can scale, that is equally awesome at small volume as very large volume, and we build on top of that. You know, similarly, we just announced our analytics exchange, which is basically the place where you can now build these data analytics assets that we discussed, so you can now build an ecosystem that creates value for end-users. And so BigQuery is really at the center of a lot of that strategy, but it's not unlike any of the other products that we have. We want to make it simple for people to onboard, simple to scale, to really accomplish, you know, whatever success is ahead of them. >> Well, I think ecosystems is another one of those big differences in the coming decade, because you're able to build ecosystems around data, especially if you can share that data, you know, and do so in a governed and secure way. But it leads to my question on industries, and I'm wondering if you see any patterns emerging in industries? And each industry seems to have its own unique disruption scenario. You know, retail obviously has been, you know, disrupted with online commerce. And healthcare with, of course, the pandemic. Financial services, you wonder, "Okay, are traditional banks going to lose control of payment systems?" Manufacturing you see our reliance on China's supply chain in, of course, North America. Are you seeing any patterns in industry as it pertains to data? And what can you share with us in terms of insights there? >> Yeah, we are. And, I mean, you know, there's obviously the industries that are, you know, very data savvy or data hungry. You think about, you know, the telecommunication industry, you think about manufacturing, you think about financial services and retail. I mean, financial services and retailers are particularly interesting, because they're kind of both in the retail business and having to deal with this level of complexity of they have physical locations and they also have a relationship with people online, so they really want to be able to bring these two worlds together. You know, I think, you know, about those scenarios of Carrefour, for instance. It's a large retailer in Europe that has been able to not only to, you know, onboard on our platform and they're using, you know, everything from BigQuery, all the way to Looker, but also now create the data assets that enable them to differentiate within their own industry. And so we see a lot of that happening across pretty much all industries. It's difficult to think about an industry that is not really taking a hard look at their data strategy recently, especially over the last two years, and really thought about how they're creating innovation. We have actually created what we call design patterns, which are basically blueprints for organization to take on. It's free, it's free guidance, it's free datasets and code that can accelerate their building of these innovative solutions. So think about the, you know, ability to determine propensity to purchase. Or build, you know, a big trend is recommendation systems. Another one is anomaly detection, and this was great because anomaly detection is a scenario that works in telco, but also in financial services. So we certainly are seeing now companies moving up in their level of maturity, because we're making it easier and simpler for them to assemble these technologies and create, you know, what we call data-rich experiences. >> The last question is how you see the emerging edge, IoT, analytics in that space? You know, a lot of the machine learning or AI today is modeling in the cloud, as you well know. But when you think about a lot of the consumer applications, whether it's voice recognition or, you know, or fingerprinting, et cetera, you're seeing some really interesting use cases that could bleed into the enterprise. And we think about AI inferencing at the edge as really driving a lot of value. How do you see that playing out and what's Google's role there? >> So there's a lot going on in that space. I'll give you just a simple example. Maybe something that's easy for the community to understand is there's still ways that we define certain metrics that are not taking into account what actually is happening in reality. I was just talking to a company whose job is to deliver meals to people. And what they have realized is that in order for them to predict exactly the time it's going to take them from the kitchen to your desk, they have to take into account the fact that distance sometimes it's not just horizontal, it's also vertical. So if you're distributing and you're delivering meals, you know, in Singapore, for instance, high density, you have to understand maybe the data coming from the elevators. So you can determine, "Oh, if you're on the 20th floor, now my distance to you, and my ability to forecast exactly when you're going to get that meal, is going to be different than if you are on the fifth floor. And, particularly, if you're ordering at 11:32, versus if you're ordering at 11:58." And so what's happening here is that as people are developing these intelligent systems, they're now starting to input a lot of information that historically we might not have thought about, but that actually is very relevant to the end-user. And so, you know, how do you do that? Again, and you have to have a platform that enables you to have a large diversity of use cases, and that thinks ahead, if you will, of the problems you might run into. Lots and lots of innovation in this space. I mean, we work with, you know, companies like Ford to, you know, reinvent the connected, you know, cars. We work with companies like Vodafone, 700 use cases, to think about how they're going to deal with what they call their data ocean. You know, I thought you would like this term, because we've gone from data lakes to data oceans. And so there is certainly a ton of innovation and certainly, you know, the chief data officers that I have the opportunity to work with are really not short of ideas. I think what's been happening up until now, they haven't had this kind of single, unified, simple experience that they can use in order to onboard quickly and then enable their people to build great, rich-data applications. >> Yeah, we certainly had fun with that over the years, data lake or data ocean. And thank you for remembering that, Bruno. Always a pleasure seeing you. Thanks so much for your time and sharing your perspectives, and informing us about what Google's up to. Can't wait to have you back. >> Thanks for having me, Dave. >> All right, and thank you for watching, everybody. This is Dave Vellante. Appreciate you watching this CUBE Conversation, and we'll see you next time. (gentle music)

>>From around the globe. It's the cube with coverage of coop con and cloud native con North America, 2020 virtual brought to you by red hat, the cloud native computing foundation and ecosystem partners. >>Hey, welcome back, everybody Jeffrey here with the cube coming to you from our Palo Alto studios today with our ongoing coverage of coupon cloud native con North America, 2020. It's not really North America, it's virtual like everything else, but you know that the European show earlier in the summer, and this is the, this is the late fall show. So we're excited to welcome in our very next two guests. Uh, first joining us from Madrid. Spain is Miguel Perez, Kaleena. He is a principal product manager from red hat, Miguel. Great to see you. >>Good to see you happy to be in the cube. >>Yes. Great. Well welcome. And joining us from North Carolina is rich Sharples. He is a senior director, product management of red hat. Rich. Great to see you. >>Yeah, likewise, thanks for inviting me again. >>So we're talking about Java today and before we kind of jump into it, you know, in preparing for this rich, I saw an interview that you did, I think earlier about halfway through the year, uh, celebrating the 25th anniversary of Java and talking about the 25th anniversary Java. And before we kind of get into the future, I think it's worthwhile to take a look back at, you know, kind of where Java came from and how it's lasted for 25 years of such an important enterprise, you know, kind of application framework, because we always hear jokes about people looking for COBOL programmers or, you know, all these old language programmers, because they have some old system that's that needs a little assist. What's special about Java. Why are we 25 years into it? And you guys are still excited about Java yesterday, today and in the future. >>Yeah. And I should add that, um, in terms of languages, uh, twenty-five is actually still pretty young. Java's, uh, kind of middle aged, I guess. Um, you know, things like CC plus bus rrr you're 45, 50 years old Python, I think is about the same as Java in terms of years. So, you know, the languages do tend to move at a, um, at a, they do tend to stick around, uh, uh, a bit, well what's made Java really, really important for enterprises building business critical applications is it started off with a very large ecosystem of big vendors supporting it. Um, it was open in a sense from the very start and it's remained open as in open source and an open community as well. So that's really, really helped, um, you know, keep the language innovating and moving along and attracting new developers. And, um, it's, it's still a fairly modern language in terms of some of the new features it's advancing with the industry taking on new kinds of workloads and new kinds of per program paradigms as well. So, you know, it's, it's evolved very well and has a huge base out somewhere between 11 and 13 million developers still use it as a primary development language in professional settings. Yeah. >>What struck me about what you said though in that interview was kind of the evolution and how Java has been able to continue to adapt based on kind of what the new frameworks are. So whether it was early days in a machine, like you talked about being in a set top box, or, you know, kind of really lightweight kind of almost IOT applications then to be calming, you know, this really a great application to deliver enterprise applications via a web browser and that, you know, and it continues to morph and change and adapt over time. I thought that was pretty interesting given the vast change in the way applications are delivered today versus what they were 25 years ago. >>Yeah, absolutely. It's, you know, the very early days were around embedded devices, uh, intelligent toasters and, you know, whatever. Um, and, and then where it really, really took off was, but the building supporting big backend systems, big transactional workloads, whether you're a bank or an airline you're running both the scale, but also running really, really complex transactional systems that were business critical. And that's that's for the last, you know, 15 years has been, um, where it's, it's really shown building backend, um, systems. Now, as we kind of move forward, you know, the idea of, uh, um, like server side, uh, server side application versus a front end is kind of changed. You know, now we're talking microservices, we're talking about running in containers. So really the focus of where we run Java and the kinds of applications we're building with Java as this has radically changed. And as such the language has to change as well, which is, you know, one, I'm pretty excited to talk about caucus today. >>So let's, let's jump into it and talk about corcus cause the other big trend, you know, along with, with, with obviously, uh, uh, browsers being great enterprise applications, delivery vehicles is this thing called containers, right? And, and specifically more recently Kubernetes is the one that's grabbing all the attention and grabbing all the, all the momentum. Um, so I wonder Miguel, if you could talk about, you know, kind of as, as the popularity of containerized applications and containerized to everything right, containerized storage, or you even talked about containerizing networking, troll, how that's impacted, uh, what you guys are doing and the impact of Java, uh, and making it work with kind of a containerized Kubernetes world. >>Well, what we found is that the paradigm of development has teeth. So we have this top up, uh, uh, paradigm that the people are following to be able to do the best with containers, to the best with Kubernetes on the, this has worked quite fine in Greenfield on for, for many cases has been a way to develop applications faster, to be able to obtain variably salts. And the thing is that for many, uh, users, for many companies that we work with, uh, they also want to bring some of their stuff that the applications that are currently are running into this world. And, uh, I mean, we, we walk especially a lot in helping these customers be able to adopt those obligations, but we try to do it, uh, as we say, the N pixie dust, you know, we really dig into the code, we'll review the code with modernize. The application will help their customer with that application. We provide the tools are open for anyone to be able to review it and to be able to take it. So we are moving away from Greenfield into brownfield and not a way we are evolving together to say we more precise, you know, all these Greenfield applications keep coming, but also the current applications want to be more organized. >>Right. Right. So it's pretty interesting. Cause that's always the big conversation. There's, it's, it's all fine. And good if you're just building something new, uh, to use the latest tools. But as you mentioned, there's a whole lot of conversation about application modernization and this is really an opportunity to apply some of these techniques to do that. So quirky. So I wonder if you just give, let's just jump into it. What is it at the highest level? Uh, what's it all about? What should people know? >>Yeah. So, so Corker says I'm reading an attempt by red hat to ensure Java is a first-class citizen in containerized environments, but building reactive applications, uh, cloud native applications, uh, functions, Java is an incredible piece of engineering. It does some incredible things. It sudden can self optimize. As it's running in line code, it can do some really amazing things the longer it runs, but in a containerized environment, you're likely not going to be running huge amounts of code. You'd likely be running microservices and your, your services are likely to have a kind of limited life cycle as we you're able to deploy more frequently or in a function environment where, you know, you've been bought once and then you're done, um, you know, during all those long, um, kind of, um, those optimizations over time, don't really, um, make a lot of sense. So what we can do is remove a lot of the, um, the weights of Java, a lot of the complexity of Java, and we can optimize for an environment where your code is maybe just running for a few microseconds as in the case of the function or something running in native, cause you scale up and scale down. >>So we move a lot of the op side. We move a lot of the, um, the, the efforts within the application, uh, to compile time, we pre compile all of your, of your config and initialization, so that doesn't have to happen in your, um, your, your, your runtime or your production environment. Um, and then we can optimize the code week. We can, we can remove that code. We can remove, you know, whole, uh, trees and class libraries and really slimmed down the memory footprint and radically, um, slim, the Maddie memory footprint, um, increase the startup time as well. So, you know, you have less downtime in your applications. Um, and we've recently done a S a study with ADC that shows some pretty stunning results compared to, you know, some existing frameworks. And, you know, we get, um, you know, sort of like, you know, overall cost savings of, you know, 60, 64%. >>Um, we can get eight times better density. You're running more in a, in a, in a cluster and, um, you know, reduction in memory up to 90% as well. So it's, these are significant changes now. That's all good, you know, saving, saving 60, 60% on your operational costs is significant. But what we find is that most organizations, they come for the performance and the optimizations, but what actually stay for is the speed of development. So I think, I think caucus real silver bullets is, um, the developer productivity, you know, for organizations, the cost of development is still one of the major costs. I mean, the operational costs, the hosting costs a significant, but development costs, time to market will always be top of mind for organizations that are trying to move faster than the competition. And I think that's really where, um, um, caucus special and coupled in, uh, in, uh, OpenShift or Coobernetti's environment really, really does shine. Yeah, >>It's pretty interesting. So people can go to corcus.io and see a lot of the statistics that you just referenced in terms of memory usage and speed and, and whole bunch of stuff. But what struck me when I went to the site was that was this big, uh, uh, two words that jumped out developer joy. And it's funny that you talked on that just now about really, um, the benefits that come to the developer directly to make them happier. I mean, really calling out their joy. So they're more productive and ultimately that's what you said. That's where the great value is in terms of speed of deployment, happy developers, and productive developers. You know, Miguel, you get your, you get down into the weeds of this stuff. Again, the presentations on your LinkedIn, everyone needs to go look and you talk a lot about at migration and you lot talk a lot about app modernization. So without going through all 120 some odd slides that I think you have, which is good, phenomenal information, what are some of the top things that people need to think about and consider both for app modernization as well as at migration? >>Um, that's, that's, that's an interesting question. Uh, the thing is that, um, the tolling is important on the current code is, and the thing is that normally when, when we started migration project, we tried to find architects in the applications to be able to find patterns. You know, you find parents is much easier because, uh, once you solve one part on the same part on can be solved in a very similar way. So this is one of the parts of that. We focus a lot, but before getting to that point, it's very important how you stop, you know, so the assessment phase is, is very important to be able to review well, what is the status of the applications, the context of the applications. And with that, I mean, things like, for example, the requirements that they have, there's the maintenance that they take in their resiliency and so on. >>So you have to prepare very well, the project by starting with a good assessment, you have to check which applications makes more, make more sense to start with and see which, how to group them together by similarities. And then you can start with the project that saying, okay, let's go for these set of applications that make more sense that are more likely to be containerized because of the way we are developing them because of the dependencies that they have because of the resiliency that is already embedded into them and so on. So that, that the methodology is important. And we normally, for example, when we, when we help partners do a application migration, one of the things that we stress is that this is the methodology that we follow and in the website for my vision, totally for application, you can find also, um, methodology, uh, part that, uh, could help, uh, people understand, okay, these, these are the stages that we normally follow to be successful with migrating applications. >>Yeah. Let go. You don't, we're not friends. We don't hang out a lot, but if we did, you would know I never ever recommend PowerPoint for anything. So, so the fact that I'm calling out your PowerPoint actually means something. Cause I think it's the worst application ever built, but you got some tremendous, tremendous information in there and people do need to go in and look, and again, it's all from your LinkedIn work, but I wanted to shift gears a little bit, right? We're at CubeCon cloud native con. Um, obviously it's virtual is 2020. That's the way the world today. But I just curious to get your guys' take on, on what does this, uh, event mean for you obviously really active, open source community, you know, red hat has a long open-source history. Um, what does CubeCon cloud native con mean for you guys? What do you hope to get out of it? What should people hope to, uh, to learn from red hat? >>Yeah, we, um, yeah, we're, we're buying your DNA. We're very, very collaborative. Uh, we, we love to learn from our customers, users of the technologies, um, in the communities that we support. Um, speaking as a, you know, we're both product guys, there's nothing better than getting with, um, people that actually use the products, um, in anger, in real life, whether they're products are upstream technologies, learning, learning, what they're doing, understanding where, um, some of the gaps are there's. Um, yeah, we just couldn't do our jobs without engaging with developers, users in these kind of conferences. Yeah. A lot of the, um, love interest we've seen with coworkers is, is in the community, you know, um, like I'd been part of many, many successful open source projects, um, um, over red hat. And it's great when your customers, you know, like, uh, Vodafone, Greece or Carrefour in Spain are openly publicly talking about how good your technology is, what they're using it for. And that's really good. So it's just nothing, there's no alternative that, you know, whether it be virtual virtually or physically sitting down with, uh, with users of your technology, >>How about you, Miguel? What are you hoping to get out of, uh, out of the show this year? >>Um, we are working a lot with, on Kubernetes in red hat, on, uh, as part of the community, of course. And, um, I mean, there are so many new stuff that is coming around, Kubernetes that, uh, it's mostly about it, about all the capabilities that were arming, especially for example, several lists, you know, several lessons, there is an important topic with crackers, because for example, as you make the application stopped so much faster and react so much faster, you could have known of them running and just waiting for an event to happen, which saves a lot of resources and makes us super efficient. So this is one of the topics, for example, that we wanted to cover in this edition, you know, how we are implementing serverless with Kubernetes and OpenShift and many other things like pipelines. Like, I don't know, we just had quite a visit in the, uh, uh, video, uh, life of what is coming up. I see for the six. And I recommend people to take a look at it, to get everything that's new because there's a lot. Yeah, >>Yeah. You guys are technical people. You've been doing this for a long time. Why is Kubernetes so special? W Y Y you know, there's been containers in the past, right. And we've seen other kind of branded open source projects that got a lot of momentum, but Kubernetes just seems to be blowing everybody out of the out of its path. Why, what should people know about Kubernetes that aren't necessarily developers? >>Yeah, there's really nothing interesting about a single container or a single microservice, right? That's not, that's not the kind of environment that, um, real organizations live in. They live in organizations where they're going to have hundreds of services, um, who just containers and you need a technology to orchestrate and manage that in that complex environment. And Kubernete's has just quickly become the, the district per standard. Um, yeah, folks are red hat jumped on my very, very early, um, I mean, one of the advantages around her have is where we're embedded with developers and open source communities. We often have a pretty good, it gives us a pretty good crystal ball. So we're often quick to jump on the emerging technologies that are coming out of open source. And that's exactly what happened with Cubanetis. It was clear. It was, um, you're going to be sophisticated for our, you know, most, um, most sophisticated customers running at scale. Um, but, but also, you know, great for development environments as well. So it really a good fit for, uh, where we were headed and, you know, just very, very quickly became the fact that standard. And you, you just gotta go with the de facto standard. Right, right. >>Right. Well, the another thing that you mentioned rich in that other interview that I was watching is it came up the conversation in terms of managing open source projects. And at some point, you know, they kind of start, and then, you know, I think this one, if I go to corcus and look at the bottom of the page sponsored by red hat, but you talked about, you know, at some point, do you move it over to a foundation, um, you know, and kind of what are the things that kind of drive that process, that decision, um, and, you know, I would imagine that part of it has to do with popularity and scale, is that something, you know, potentially down the road, how do you think that you said you've been in lots of open source projects, when does it move from, you know, kind of single point of origin to more of a foundational support? >>Yeah. I mean, in fact the foundation's owner was necessary. Um, you know, when you have a, yeah. If you, if you have a, an open, very open project with, um, um, clear, clear rules for collaboration and kind of the encouragement or others to collaborate and be able to, you know, um, move the project and, you know, the foundation as low as necessarily what we've seen, I've been part of the no GS world where, you know, the, the community reached Belden to keep no GS moving forward. Um, we had to go from a, what we call a benevolent dictator for life, somebody who's well-intentioned, but, um, yeah, we're on stone, the technology, so a foundation, which is much more inclusive and, um, you know, greater collaboration and you can move even quicker. So, you know, um, I think what's required is, is open governance for open source projects and where that doesn't happen. You know, maybe a foundation is, is the right way forward. Right, right now with, with caucus, um, you know, the, the non red hat developers seem pretty happy with the way they can get, uh, get engaged and contribute. Um, but if we get to a point where the community is demanding a foundation and we'll absolutely consider it, that's the best project we'll do. >>So, so we're, we're coming to the end of our time. I want to give you each the last word, really with two questions, one again, you know, just kind of a summary of, of, uh, of CubeCon cloud, native con, you know, what should people be looking for, uh, find you, and, and, and I don't know if you guys are sponsoring any sessions, I'm sure there's a lot of great content. If you want to highlight one or two things. And then most importantly, as we turn the calendars, we come to the end of 2020, uh, thankfully, um, as you look ahead to 2021, you know, what are some of your priorities, uh, as, as we get ready to turn the turn, the calendar, and Miguel let's start with you. >>So, um, I mean, we have been working very hard this year on the migration, took it for applications to help her every user that is using Java to bring the two containers. You know, whether it is data IE or these crackers, but we're putting like a lot of effort in crackers. And now we are bringing in new rules. And, uh, by the, by December, we expect to have the new version of the migration looking for applications that is going to include the, all the bulls to help developers bring their, their code to the Java code, to, to carcass. And, uh, on this, this is the main goal for us right now. We are moving forward to the next year to include more, more capabilities in that project. Everything's up on site. You can go to the conveyor, uh, project and ticket on, uh, on the up capabilities for the assessment phase. So whenever any partner, any, any of our consultants are working on, on migration or anyone that would like to go and try it themselves on adopted, would like to do these migrations to the cloud native world, uh, will feel comfortable with, with this tool. So that is our main goal in, in my, in my team. >>All right. And how about you rich? >>Yeah, I think we're going to see this, um, um, kind of syllabus solidification kind of web of, um, microservices. Um, you know, if you like hate that, I'm sorry, but I'm just going to next generation microservice. There's going to be, as Miguel mentioned, is gonna be based around, um, uh, native, um, advancing, um, serverless functions. I think that's really the, the, the ideal architecture, the building March services, um, on, on Coobernetti's and caucus plays really, really well there. Um, I think there's, there's a, there's a kind of backlog of projects, um, within organizations that, um, you know, hopefully next year, everything really does start to crank up. And I think, um, yeah, I think a lot of the migration that Miguel has talked about is going to be, is going to rise in terms of importance. So app modernization, taking those existing applications, maybe taking aspects of those and, you know, doing some kind of decomposition in some microservices using caucus and a native, I think we'll see a lot of that. So I think we'll see a real drive around both the kind of Greenfield, um, applications, uh, you know, this next generation of microservices, as well as pulling those existing applications forward into these new environments, don't give an answers. So it's going to be excellent. >>Awesome. Well, thank you both for taking a few minutes with us and sharing the story of corcus, uh, and have a great show. Great to see you and a really good the conversation. All right. He's Miguel, he's rich. I'm Jeff. You're watching the cubes ongoing coverage of CubeCon cloud native con 2020 North America. Virtual. Thanks for watching. We'll see you next time.