 From around the globe, it's theCUBE, presenting Cube on Cloud, brought to you by SiliconANGLE. 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. J.G. Chirapyarath is the Vice President of Azure Data AI and Edge at Microsoft. J.G., 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, J.G., 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. You know, we've said many times on 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, you know, 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, you know, do you buy into that premise? Yes, absolutely. We do buy into it. And I think, you know, 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, you know, machine learning and the use of data in interesting ways. Yeah, so I'd like to get some more thoughts around data and how you see the future data and the role of cloud and maybe how Microsoft's strategy fits in there. I mean, your portfolio, you got SQL server, Azure SQL, you got ARC, which is kind of Azure everywhere for the people that aren't familiar with that. You got Synapse, which of course does all the integration and data warehouse and gets things ready for BI and consumption by the business and the whole data pipeline. And then a lot of other services, Azure Databricks, you got Cosmos in there, you got Blockchain, you got open source services like Postgres and MySQL. So lots of choices there. And I'm wondering, you know, 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 SQL server 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 Postgres in things like MongoDB, right? 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 a lot more convergence. Use, it really 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 computer engine on top of it to derive 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 that 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 to learn 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, 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 and choose an engine that is most flexible to them. However, when it comes to say, writing a SQL server, for example, modernizing it, you want sometimes you just want to lift and shift it into things like IaaS. In other cases, you want to completely rewrite it. So you need to have the flexibility or 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, right? 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, you know, 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, you know, 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're seeing a lot of that in the marketplace. You've got some companies that 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 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 and it takes them too long to go from, you know, 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 lifecycle and pipeline and big 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 in the area of analytics and AI. It's almost as if what worked in the past will not work going forward, right? So when you think about analytics in the enterprise today, you have relational systems, you have Purdue systems, you've got data mats, you've got data warehouses, you've got enterprise data warehouses, you know, those large honking databases that you use to close your books with, right? 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, you know, three, four decades and simply migrate it on mass 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 land it 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 them is complete simplicity, right? 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 the type of data, sort of the 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 data bricks, you can in fact use data bricks in sort of an integrated way with 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 that Synapse is sort of managing that technology on top. Do you buy into 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 what a customer comes and says is, 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 reimagining its logistics system that basically the system that delivers what is it, the three million packages a day. And they're doing so in this COVID times with the view of basically delivering our 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 their logistic processes, where 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 is 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 Park's initiative. It is the premier white rhino conservancy in the world. They essentially are using data that is 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 park with about 200 rangers 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, landed 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 wanna ask you though that some people might say that putting in that layer while it clearly adds 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 peace 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, so it's an art, 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. Right, 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 data breaks 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 in 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 the way we sort of think about it. Well, I like the strategy because, one of our guests, Jamak Degani, I think one of the foremost thinkers on this notion of the data mesh in her premise is that, that 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 data breaks a couple of times. There's another partner that you have which is Snowflake. They're kind of trying to build out their own data cloud, if you will, and global mesh. And on 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. You know, when we see, essentially, you know, 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, right? 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 data breaks as well, where they are effectively on multiple cloud and our opportunity with data breaks is to essentially integrate them in a way where we offer the best experience, the best integrations on Azure, partner. That's always been our focus. That's hard to argue with the strategy our data, with our data partner, 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 more we've built in ensuring the research posture that we've taken there. What we've then done is essentially a couple of things. We focused on essentially looking at the spectrum that is AI, both from saying that, hey listen, it's got to work for data analysts who are looking to basically use machine learning techniques to developers who are essentially coding and building machine learning models from scratch. So that proposition manifests 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 in things like responsible AI. So if you ask 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, right? Where they can infer 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, multi-cloud, obviously one of your competitors, Amazon won't even say the word multi-cloud, you guys have a different approach there but what's the strategy with regard to hybrid? Do you see the cloud 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, I'll be the first one to say that the word edge itself is conflated, okay? But I will tell you just focusing on hybrid. This is one of the places where, I would say the 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 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 pattern, platforms like say containers, orchestrate Kubernetes so that we can essentially deploy it wherever we want. And so when we designed things like Arc, it was built for that flexibility in mind. So here is 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, multi-cloud is here to stay. You know, we are never going to be the people that says, I'm sorry, we will never say the word multi-cloud, 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 know, you've got Microsoft on an incredible job of 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 its strategy. And many have said, me included, that you got there by creating products that are good enough. You know, we do a one-dotto, it's not that great than a two-dotto, 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 heard my feelings with that question. Don't hate me, JG. We're getting it out there, right? Well, first of all, thank you for asking me that. You know, I am absolutely the biggest cheerleader you'll find at Microsoft. I absolutely believe that, you know, 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 delivered 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 sort of offered the world's first comprehensive data governance solution in Azure Purview, I would humbly submit to you that we are leading the way, and we are essentially showing how the future of data, AI, and the edge should work in the cloud. Yeah, I'd be disappointed if you had, if you capitulated in any way, JG. So thank you for that. And the 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, management and deployment, sassifying 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 outside's value out of this. You know, what happened in the last decade where people laid the foundation, and people essentially looked at the world and said, look, we've got to make the move. You know, they're largely hybrid, but we're going to start making steps to basically digitize and modernize our platforms. I would 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, for business outcomes, explode. You're also going to see a huge sort of focus on things like governance. You know, 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, they're compliance posture. So I think the unlocking of outcomes versus simply, hey, I've saved money. Second, really putting this comprehensive sort of, you know, 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's number one on the CIO list. J.G., great conversation. Obviously, the strategy is working. And thanks so much for participating in Cuba on cloud. Thank you. Thank you, Dave. And I appreciate it. And thank you to everybody who's tuning in today. All right. And keep it right there. I'll be back with our next guest right after this short break.