(upbeat music) >> Narrator: Live from Vancouver, Canada, it's theCUBE covering OpenStack Summit North America, 2018, Brought to you by Red Hat, the OpenStack foundation, and its ecosystem partners. >> Welcome back, I'm Stew Miniman here with theCUBE's coverage of OpenStack Summit 2018 in Vancouver. My co-host is John Troyer. Happy to welcome to the program, we have Berna Devrim, who is the Senior Director of Product Marketing of Platform at Red Hat. And we are thrilled to have a customer on, Nico Wellner, who's a Unix Systems Engineer with Finanz Informatik out of Germany. Thank you both so much for joining. Alright, Berna let's start with you. Just give, your first time on the program I believe, so a little bit about your background. You've been with Red Hat less than a year so tell us your role there. >> Yeah, yeah I've been at Red Hat for nine, 10 months now. I've very very excited to be here in the Open Source community development model. It's a very unique opportunity, as I've been leading the platform's marketing, which includes Red Hat Enterprise Linux, as well as Red Hat Virtualization, and Red Hat OpenStack platform, of course, which is why we are here at OpenStack summit. >> Great. We've got Rhel, and RHV, and RHOSP, and lots of other "LMNOP's." So Nico, give us a little bit about your background. Tell us about your organization and then lets get into the mini case study we'll do with you. >> It's an honor for me to be here. Thank you very much for this. I working for Finanz Informatiks, as you said, and it's a centralized IT service provider in the S Finance Group in Germany, for savings banks and state banks. We always have about 400 institutes. Savings banks, individual savings banks. On our systems we are supporting more than 120 million accounts, bank accounts, nearly half of them online accounts. We also develop the software for the savings banks for our customers, not savings banks only. Also, assurances and state banks. We operate the applications we developed previously. It's a huge and amazing company with a lot of different groups and systems. >> Well, we're really glad you could make it here. With GDPR banging down the door in just a couple of days we expect everybody in Europe to be pretty busy getting ready for that. Tell us your role inside the organization. What's your team do? Your title has Unix in it, so what's that entail? Give us the scope of what you cover. >> I'm assistant engineer, as you said and I'm working in the department. We are integrating and operating the Unix systems, which are AIX, we have a huge AIX, and why-mite and a huge Lenox, and why-mite in our data centers. On these Lenox systems, we started with OpenStack in 2014, with testing, and went into production in 2015, half a year later. We integrated OpenStack. We operated and served for our customers internally on the OpenStack platform. We host one of our core applications, it's the internet banking for our customers, as I said for about 50 millions account. We have multiple OpenStack Clouds. My department is responsible for the clouds and for operating them well. >> Nico I wonder if we step back for a second and what led to you going down this path. Was the company figuring out its cloud strategy? Obviously financial institutions, we understand there's governance, compliance, security is a huge concern. What does Cloud mean to your team? What led you to OpenStack? Let's start with kind of that problem statement that you had. >> Yeah, it was the main reason we introduced OpenStack was the time to market was our applications, it was our environment. And it's a plummet process. Took a long time normally and the environment. With OpenStack we could dramatically increase the time to deploy the systems from days or weeks to minutes. So we solved one huge problem with OpenStack. What was another reason was vendor lock-in. We wanted to avoid vendor lock-in. So we decided for OpenStack because it's a huge open source software, great community, and very stable, in our case. So it's OpenStack for us. >> So Berna, I've actually had the opportunity to interview quite a few Red Hat customers. I remember three years ago we were actually in the other hallway here talking to FICO about their role out of Red Hat OpenStack. I hear some similar themes, but you've got access to way more customers than I do. What are you hearing from customers in general? Is this kind of the typical? Is speed and agility at the top of the list when it comes to their Cloud environment? >> Exactly Stew, just like Nico said, actually. Our customers tell us all the time that it is about speed and agility. But it's also about different types of use cases and the workloads that they're actually looking at in their environments. Very popular ones, the use cases are. For example, scale-out IS, as well as they have test environments for the clouds needs applications, for example. Also we do see that big data analytics, NFV also. So there are many different types of use cases we see from our customers. We also have been hearing that they are actually using Open Shift on top of the Red Hat OpenStack platform. Majority of them are either deploying it or planning to deploy containers. So we do see a lot of different, but similar, aspects as well. >> Yeah. Nico have you started to go down that path with containers, Kubernetes, all that stuff yet? >> Not so far. We plan to do so. In general will use containers, we are planning to. But we already started the process, but it would take a little bit. I'm saying that we're not sure if start with OpenStack, containers on OpenStack or plain, but I think that with OpenStack could be a great way to do so. Because one of the reasons is our OpenStack environment is very reliable. This is important for us, very important for us and our customers. Over the years, as I said, since 2015 we had no outage due to OpenStack and the whole environment is great for us. >> That's great. So where are you now in your Red Hat OpenStack deployment? You have an OpenStack in production and now you're already a Red Hat customer in other products and you're now going out with Red Hat OpenStack platform, is that correct? >> Yes that's correct, yeah. >> I'm kind of curious. One of the conversations around OpenStack is the component nature of it and that many OpenStack deployments are different. So as you're now deploying Red Hat and you were already on OpenStack, are the skills transferable? Do you find the the processes transferable? Do you feel that this was a good investment, no up time for three years now and now you're moving to this new platform. Do you and your team feel like you're able to properly instrument and maintain and operate it? >> I think it's the best platform for us for infrastructure and management, Lenix and why-mite. We want to in-wolf it furthermore. >> Stew: And the skills will still transfer? The skills you've known for years will still transfer to the new OpenStack? >> Yes we have only a few people working actively on the design and the architecture, and operating for OpenStack. It's turned out that we could do fine with them. Now we have huge experience with OpenStack, feel comfortable with it. We are planning to increase the OpenStack environment, slightly I think. But scale out works great for us. The OpenStack itself, in our case, we could very flexible do a systems releases, which is one important thing for us. I think the OpenStack itself is the best platform for us and our application tools. >> That's great. Berna I was at Red Hat Summit and the interesting thing there for me was the portfolio, the breadth of portfolio, right? One of the messages was clear. You've always depended on Red Hat Enterprise Lenix, and that's still there and containers are Lenix. There was lot of multi-cloud talk and stuff like that, and OpenStack was part of the mix. Can you talk a little bit about OpenStack as part of the Red Hat portfolio and what you all are bringing to the table, and how you're thinking of open shift on OpenStack and that sort of thing? >> Yeah, exactly. As you pointed out Red Hat is all about open hybrid cloud. Within that Red Hat OpenStack platform plays a big role, of course as you can imagine. What we are trying to do at OpenStack platform is to help our customers like Nico get towards the digital transformation. With that comes, again, the need for speed and agility. What we are enabling with OpenStack platform is we would like to call it powering the digital transformation through enabling our customers to accelerate their businesses by simplifying their applications and delivery as well as the services delivery, which then, of course, moves towards innovation, fast innovation at the speed of the business. At the same time, we are trying to enable IT teams to be empowered so that they can actually do the innovations at their own pace without worry, with all of the Red Hat portfolio, as you pointed out. Yeah. >> Nico, we'd love to hear your take on digital transformation. I think back, five years ago we were talking about financial institutions, oh well we need to go mobile. Well it's much more than that for most companies that I talk to. Do you consider a digital transformation in your company? How does that relate to what IT does to what the business does, to what your users need? >> It's one of our core tasks in our company to help our customers for digital transformation. Finanz Informatik itself sees itself to be the best partner for our customers to do this transformation. With leading technologies like OpenStack and a special case was Red Hat OpenStack, of course, which is a product which enables us to be flexible, secure, and fast with our environment, and to drive this process of digital transformation in the S Finance Group, Savings Finance Group. >> Alright, so you've been at this for three or four years now with OpenStack, I'd love to get what learnings you've had for peers of yours that might be earlier in their journey. What have you learned? What advice might you give them? Let's start there. >> Overall I would say the OpenStack environment is very reliable. More reliable as I thought at the beginning. But it's turned out it's really good. From the automation perspective it's a really nice, let's say tool, for our environment. I found OpenStack is a great project with a lot of software components you can combine. We have a flexible platform. We can add some components we do not have today, but are part of OpenStack community of OpenStack product at all, to enable additional functionalities to the environment, let's say for containers, for object solid, and something like that, and new services for our customers to decrease the time to market. >> Okay. One of the things that this show we're seeing is looking beyond where we've been. I think the keynote this word, people are asking to do more and in more places. Everything from containers, and edge, and server lists, and the like. What's interesting you these days as you look down the road? Different technologies that are in your roadmap in the future, inside or outside OpenStack? >> For our company, we are in the process to integrate new needs for our customers and we are planning to do a lot of big data. Maybe OpenStack could be part of the white platform forward for the future we are planning. I think it will be much more diverse in future because right now we do have one application running on it, one co-application. It's a co-application where we partnered for us. But we will maybe will spread it or enable it for other applications, because of the great experience we've made with it. >> Nico and Berna, thank you so much for giving us the updates on where you stand with OpenStack and all of your deployment. We'll be back here with lots more coverage here at OpenStack Summit 2018 in Vancouver for John Troyer. I'm Stew Miniman. Thanks for watching theCUBE. (techno music)

>> Okay, we're now going to explore the vision of the future of cloud computing from the perspective of one of the leaders in the field, JG Chirapurath is the Vice President of Azure Data AI and Edge at Microsoft. JG, welcome to theCUBE on Cloud, thanks so much for participating. >> Well, thank you, Dave. And it's a real pleasure to be here with you and just want to welcome the audience as well. >> Well, JG, judging from your title, we have a lot of ground to cover and our audience is definitely interested in all the topics that are implied there. So let's get right into it. We've said many times in theCUBE that the new innovation cocktail comprises machine intelligence or AI applied to troves of data with the scale of the cloud. It's no longer we're driven by Moore's law. It's really those three factors and those ingredients are going to power the next wave of value creation in the economy. So first, do you buy into that premise? >> Yes, absolutely. We do buy into it and I think one of the reasons why we put data analytics and AI together, is because all of that really begins with the collection of data and managing it and governing it, unlocking analytics in it. And we tend to see things like AI, the value creation that comes from AI as being on that continuum of having started off with really things like analytics and proceeding to be machine learning and the use of data in interesting ways. >> Yes, I'd like to get some more thoughts around data and how you see the future of data and the role of cloud and maybe how Microsoft strategy fits in there. I mean, your portfolio, you've got SQL Server, Azure SQL, you got Arc which is kind of Azure everywhere for people that aren't familiar with that you got Synapse which course does all the integration, the data warehouse and it gets things ready for BI and consumption by the business and the whole data pipeline. And then all the other services, Azure Databricks, you got you got Cosmos in there, you got Blockchain, you've got Open Source services like PostgreSQL and MySQL. So lots of choices there. And I'm wondering, how do you think about the future of cloud data platforms? It looks like your strategy is right tool for the right job. Is that fair? >> It is fair, but it's also just to step back and look at it. It's fundamentally what we see in this market today, is that customers they seek really a comprehensive proposition. And when I say a comprehensive proposition it is sometimes not just about saying that, "Hey, listen "we know you're a sequence of a company, "we absolutely trust that you have the best "Azure SQL database in the cloud. "But tell us more." We've got data that is sitting in Hadoop systems. We've got data that is sitting in PostgreSQL, in things like MongoDB. So that open source proposition today in data and data management and database management has become front and center. So our real sort of push there is when it comes to migration management modernization of data to present the broadest possible choice to our customers, so we can meet them where they are. However, when it comes to analytics, one of the things they ask for is give us lot more convergence use. It really, it isn't about having 50 different services. It's really about having that one comprehensive service that is converged. That's where things like Synapse fits in where you can just land any kind of data in the lake and then use any compute engine on top of it to drive insights from it. So fundamentally, it is that flexibility that we really sort of focus on to meet our customers where they are. And really not pushing our dogma and our beliefs on it but to meet our customers according to the way they've deployed stuff like this. >> So that's great. I want to stick on this for a minute because when I have guests on like yourself they never want to talk about the competition but that's all we ever talk about. And that's all your customers ever talk about. Because the counter to that right tool for the right job and that I would say is really kind of Amazon's approach is that you got the single unified data platform, the mega database. So it does it all. And that's kind of Oracle's approach. It sounds like you want to have your cake and eat it too. So you got the right tool with the right job approach but you've got an integration layer that allows you to have that converged database. I wonder if you could add color to that and confirm or deny what I just said. >> No, that's a very fair observation but I'd say there's a nuance in what I sort of described. When it comes to data management, when it comes to apps, we have then customers with the broadest choice. Even in that perspective, we also offer convergence. So case in point, when you think about cosmos DB under that one sort of service, you get multiple engines but with the same properties. Right, global distribution, the five nines availability. It gives customers the ability to basically choose when they have to build that new cloud native app to adopt cosmos DB and adopt it in a way that is an choose an engine that is most flexible to them. However, when it comes to say, writing a SequenceServer for example, if modernizing it, you want sometimes, you just want to lift and shift it into things like IS. In other cases, you want to completely rewrite it. So you need to have the flexibility of choice there that is presented by a legacy of what sits on premises. When you move into things like analytics, we absolutely believe in convergence. So we don't believe that look, you need to have a relational data warehouse that is separate from a Hadoop system that is separate from say a BI system that is just, it's a bolt-on. For us, we love the proposition of really building things that are so integrated that once you land data, once you prep it inside the Lake you can use it for analytics, you can use it for BI, you can use it for machine learning. So I think, our sort of differentiated approach speaks for itself there. >> Well, that's interesting because essentially again you're not saying it's an either or, and you see a lot of that in the marketplace. You got some companies you say, "No, it's the data lake." And others say "No, no, put it in the data warehouse." And that causes confusion and complexity around the data pipeline and a lot of cutting. And I'd love to get your thoughts on this. A lot of customers struggle to get value out of data and specifically data product builders are frustrated that it takes them too long to go from, this idea of, hey, I have an idea for a data service and it can drive monetization, but to get there you got to go through this complex data life cycle and pipeline and beg people to add new data sources and do you feel like we have to rethink the way that we approach data architecture? >> Look, I think we do in the cloud. And I think what's happening today and I think the place where I see the most amount of rethink and the most amount of push from our customers to really rethink is the area of analytics and AI. It's almost as if what worked in the past will not work going forward. So when you think about analytics only in the enterprise today, you have relational systems, you have Hadoop systems, you've got data marts, you've got data warehouses you've got enterprise data warehouse. So those large honking databases that you use to close your books with. But when you start to modernize it, what people are saying is that we don't want to simply take all of that complexity that we've built over, say three, four decades and simply migrate it en masse exactly as they are into the cloud. What they really want is a completely different way of looking at things. And I think this is where services like Synapse completely provide a differentiated proposition to our customers. What we say there is land the data in any way you see, shape or form inside the lake. Once you landed inside the lake, you can essentially use a Synapse Studio to prep it in the way that you like. Use any compute engine of your choice and operate on this data in any way that you see fit. So case in point, if you want to hydrate a relational data warehouse, you can do so. If you want to do ad hoc analytics using something like Spark, you can do so. If you want to invoke Power BI on that data or BI on that data, you can do so. If you want to bring in a machine learning model on this prep data, you can do so. So inherently, so when customers buy into this proposition, what it solves for them and what it gives to them is complete simplicity. One way to land the data multiple ways to use it. And it's all integrated. >> So should we think of Synapse as an abstraction layer that abstracts away the complexity of the underlying technology? Is that a fair way to think about it? >> Yeah, you can think of it that way. It abstracts away Dave, a couple of things. It takes away that type of data. Sort of complexities related to the type of data. It takes away the complexity related to the size of data. It takes away the complexity related to creating pipelines around all these different types of data. And fundamentally puts it in a place where it can be now consumed by any sort of entity inside the Azure proposition. And by that token, even Databricks. You can in fact use Databricks in sort of an integrated way with the Azure Synapse >> Right, well, so that leads me to this notion of and I wonder if you buy into it. So my inference is that a data warehouse or a data lake could just be a node inside of a global data mesh. And then it's Synapse is sort of managing that technology on top. Do you buy into that? That global data mesh concept? >> We do and we actually do see our customers using Synapse and the value proposition that it brings together in that way. Now it's not where they start, oftentimes when a customer comes and says, "Look, I've got an enterprise data warehouse, "I want to migrate it." Or "I have a Hadoop system, I want to migrate it." But from there, the evolution is absolutely interesting to see. I'll give you an example. One of the customers that we're very proud of is FedEx. And what FedEx is doing is it's completely re-imagining its logistics system. That basically the system that delivers, what is it? The 3 million packages a day. And in doing so, in this COVID times, with the view of basically delivering on COVID vaccines. One of the ways they're doing it, is basically using Synapse. Synapse is essentially that analytic hub where they can get complete view into the logistic processes, way things are moving, understand things like delays and really put all of that together in a way that they can essentially get our packages and these vaccines delivered as quickly as possible. Another example, it's one of my favorite. We see once customers buy into it, they essentially can do other things with it. So an example of this is really my favorite story is Peace Parks initiative. It is the premier of white rhino conservancy in the world. They essentially are using data that has landed in Azure, images in particular to basically use drones over the vast area that they patrol and use machine learning on this data to really figure out where is an issue and where there isn't an issue. So that this part with about 200 radios can scramble surgically versus having to range across the vast area that they cover. So, what you see here is, the importance is really getting your data in order, landing consistently whatever the kind of data it is, build the right pipelines, and then the possibilities of transformation are just endless. >> Yeah, that's very nice how you worked in some of the customer examples and I appreciate that. I want to ask you though that some people might say that putting in that layer while you clearly add simplification and is I think a great thing that there begins over time to be a gap, if you will, between the ability of that layer to integrate all the primitives and all the piece parts, and that you lose some of that fine grain control and it slows you down. What would you say to that? >> Look, I think that's what we excel at and that's what we completely sort of buy into. And it's our job to basically provide that level of integration and that granularity in the way that it's an art. I absolutely admit it's an art. There are areas where people crave simplicity and not a lot of sort of knobs and dials and things like that. But there are areas where customers want flexibility. And so I think just to give you an example of both of them, in landing the data, in consistency in building pipelines, they want simplicity. They don't want complexity. They don't want 50 different places to do this. There's one way to do it. When it comes to computing and reducing this data, analyzing this data, they want flexibility. This is one of the reasons why we say, "Hey, listen you want to use Databricks. "If you're buying into that proposition. "And you're absolutely happy with them, "you can plug it into it." You want to use BI and essentially do a small data model, you can use BI. If you say that, "Look, I've landed into the lake, "I really only want to use ML." Bring in your ML models and party on. So that's where the flexibility comes in. So that's sort of that we sort of think about it. >> Well, I like the strategy because one of our guests, Jumark Dehghani is I think one of the foremost thinkers on this notion of of the data mesh And her premise is that the data builders, data product and service builders are frustrated because the big data system is generic to context. There's no context in there. But by having context in the big data architecture and system you can get products to market much, much, much faster. So, and that seems to be your philosophy but I'm going to jump ahead to my ecosystem question. You've mentioned Databricks a couple of times. There's another partner that you have, which is Snowflake. They're kind of trying to build out their own DataCloud, if you will and GlobalMesh, and the one hand they're a partner on the other hand they're a competitor. How do you sort of balance and square that circle? >> Look, when I see Snowflake, I actually see a partner. When we see essentially we are when you think about Azure now this is where I sort of step back and look at Azure as a whole. And in Azure as a whole, companies like Snowflake are vital in our ecosystem. I mean, there are places we compete, but effectively by helping them build the best Snowflake service on Azure, we essentially are able to differentiate and offer a differentiated value proposition compared to say a Google or an AWS. In fact, that's been our approach with Databricks as well. Where they are effectively on multiple clouds and our opportunity with Databricks is to essentially integrate them in a way where we offer the best experience the best integrations on Azure Berna. That's always been our focus. >> Yeah, it's hard to argue with the strategy or data with our data partner and ETR shows Microsoft is both pervasive and impressively having a lot of momentum spending velocity within the budget cycles. I want to come back to AI a little bit. It's obviously one of the fastest growing areas in our survey data. As I said, clearly Microsoft is a leader in this space. What's your vision of the future of machine intelligence and how Microsoft will participate in that opportunity? >> Yeah, so fundamentally, we've built on decades of research around essentially vision, speech and language. That's been the three core building blocks and for a really focused period of time, we focused on essentially ensuring human parity. So if you ever wonder what the keys to the kingdom are, it's the boat we built in ensuring that the research or posture that we've taken there. What we've then done is essentially a couple of things. We've focused on essentially looking at the spectrum that is AI. Both from saying that, "Hey, listen, "it's got to work for data analysts." We're looking to basically use machine learning techniques to developers who are essentially, coding and building machine learning models from scratch. So for that select proposition manifest to us as really AI focused on all skill levels. The other core thing we've done is that we've also said, "Look, it'll only work as long "as people trust their data "and they can trust their AI models." So there's a tremendous body of work and research we do and things like responsible AI. So if you asked me where we sort of push on is fundamentally to make sure that we never lose sight of the fact that the spectrum of AI can sort of come together for any skill level. And we keep that responsible AI proposition absolutely strong. Now against that canvas Dave, I'll also tell you that as Edge devices get way more capable, where they can input on the Edge, say a camera or a mic or something like that. You will see us pushing a lot more of that capability onto the edge as well. But to me, that's sort of a modality but the core really is all skill levels and that responsibility in AI. >> Yeah, so that brings me to this notion of, I want to bring an Edge and hybrid cloud, understand how you're thinking about hybrid cloud, multicloud obviously one of your competitors Amazon won't even say the word multicloud. You guys have a different approach there but what's the strategy with regard to hybrid? Do you see the cloud, you're bringing Azure to the edge maybe you could talk about that and talk about how you're different from the competition. >> Yeah, I think in the Edge from an Edge and I even I'll be the first one to say that the word Edge itself is conflated. Okay, a little bit it's but I will tell you just focusing on hybrid, this is one of the places where, I would say 2020 if I were to look back from a COVID perspective in particular, it has been the most informative. Because we absolutely saw customers digitizing, moving to the cloud. And we really saw hybrid in action. 2020 was the year that hybrid sort of really became real from a cloud computing perspective. And an example of this is we understood that it's not all or nothing. So sometimes customers want Azure consistency in their data centers. This is where things like Azure Stack comes in. Sometimes they basically come to us and say, "We want the flexibility of adopting "flexible button of platforms let's say containers, "orchestrating Kubernetes "so that we can essentially deploy it wherever you want." And so when we designed things like Arc, it was built for that flexibility in mind. So, here's the beauty of what something like Arc can do for you. If you have a Kubernetes endpoint anywhere, we can deploy an Azure service onto it. That is the promise. Which means, if for some reason the customer says that, "Hey, I've got "this Kubernetes endpoint in AWS. And I love Azure SQL. You will be able to run Azure SQL inside AWS. There's nothing that stops you from doing it. So inherently, remember our first principle is always to meet our customers where they are. So from that perspective, multicloud is here to stay. We are never going to be the people that says, "I'm sorry." We will never say (speaks indistinctly) multicloud but it is a reality for our customers. >> So I wonder if we could close, thank you for that. By looking back and then ahead and I want to put forth, maybe it's a criticism, but maybe not. Maybe it's an art of Microsoft. But first, you did Microsoft an incredible job at transitioning its business. Azure is omnipresent, as we said our data shows that. So two-part question first, Microsoft got there by investing in the cloud, really changing its mindset, I think and leveraging its huge software estate and customer base to put Azure at the center of it's strategy. And many have said, me included, that you got there by creating products that are good enough. We do a one Datto, it's still not that great, then a two Datto and maybe not the best, but acceptable for your customers. And that's allowed you to grow very rapidly expand your market. How do you respond to that? Is that a fair comment? Are you more than good enough? I wonder if you could share your thoughts. >> Dave, you hurt my feelings with that question. >> Don't hate me JG. (both laugh) We're getting it out there all right, so. >> First of all, thank you for asking me that. I am absolutely the biggest cheerleader you'll find at Microsoft. I absolutely believe that I represent the work of almost 9,000 engineers. And we wake up every day worrying about our customer and worrying about the customer condition and to absolutely make sure we deliver the best in the first attempt that we do. So when you take the plethora of products we deliver in Azure, be it Azure SQL, be it Azure Cosmos DB, Synapse, Azure Databricks, which we did in partnership with Databricks, Azure Machine Learning. And recently when we premiered, we sort of offered the world's first comprehensive data governance solution in Azure Purview. I would humbly submit it to you that we are leading the way and we're essentially showing how the future of data, AI and the Edge should work in the cloud. >> Yeah, I'd be disappointed if you capitulated in any way, JG. So, thank you for that. And that's kind of last question is looking forward and how you're thinking about the future of cloud. Last decade, a lot about cloud migration, simplifying infrastructure to management and deployment. SaaSifying My Enterprise, a lot of simplification and cost savings and of course redeployment of resources toward digital transformation, other valuable activities. How do you think this coming decade will be defined? Will it be sort of more of the same or is there something else out there? >> I think that the coming decade will be one where customers start to unlock outsize value out of this. What happened to the last decade where people laid the foundation? And people essentially looked at the world and said, "Look, we've got to make a move. "They're largely hybrid, but you're going to start making "steps to basically digitize and modernize our platforms. I will tell you that with the amount of data that people are moving to the cloud, just as an example, you're going to see use of analytics, AI or business outcomes explode. You're also going to see a huge sort of focus on things like governance. People need to know where the data is, what the data catalog continues, how to govern it, how to trust this data and given all of the privacy and compliance regulations out there essentially their compliance posture. So I think the unlocking of outcomes versus simply, Hey, I've saved money. Second, really putting this comprehensive sort of governance regime in place and then finally security and trust. It's going to be more paramount than ever before. >> Yeah, nobody's going to use the data if they don't trust it, I'm glad you brought up security. It's a topic that is at number one on the CIO list. JG, great conversation. Obviously the strategy is working and thanks so much for participating in Cube on Cloud. >> Thank you, thank you, Dave and I appreciate it and thank you to everybody who's tuning into today. >> All right then keep it right there, I'll be back with our next guest right after this short break.