>> Narrator: From theCUBE Studios in Palo Alto, in Boston bringing you data-driven insights from theCUBE and ETR. This is "Breaking Analysis" with Dave Vellante. >> The next 10 years of cloud, they're going to differ dramatically from the past decade. The early days of cloud, deployed virtualization of standard off-the-shelf components, X86 microprocessors, disk drives et cetera, to then scale out and build a large distributed system. The coming decade is going to see a much more data-centric, real-time, intelligent, call it even hyper-decentralized cloud that will comprise on-prem, hybrid, cross-cloud and edge workloads with a services layer that will obstruct the underlying complexity of the infrastructure which will also comprise much more custom and varied components. This was a key takeaway of the guests from theCUBE on Cloud, an event hosted by SiliconANGLE on theCUBE. Welcome to this week's Wikibon CUBE Insights Powered by ETR. In this episode, we'll summarize the findings of our recent event and extract the signal from our great guests with a couple of series and comments and clips from the show. CUBE on Cloud is our very first virtual editorial event. It was designed to bring together our community in an open forum. We ran the day on our 365 software platform and had a great lineup of CEOs, CIOs, data practitioners technologists. We had cloud experts, analysts and many opinion leaders all brought together in a day long series of sessions that we developed in order to unpack the future of cloud computing in the coming decade. Let me briefly frame up the conversation and then turn it over to some of our guests. First, we put forth our view of how modern cloud has evolved and where it's headed. This graphic that we're showing here, talks about the progression of cloud innovation over time. A cloud like many innovations, it started as a novelty. When AWS announced S3 in March of 2006, nobody in the vendor or user communities really even in the trade press really paid too much attention to it. Then later that year, Amazon announced EC2 and people started to think about a new model of computing. But it was largely tire kickers, bleeding-edge developers that took notice and really leaned in. Now the financial crisis of 2007 to 2009, really created what we call a cloud awakening and it put cloud on the radar of many CFOs. Shadow IT emerged within departments that wanted to take IT in bite-sized chunks and along with the CFO wanted to take it as OPEX versus CAPEX. And then I teach transformation that really took hold. We came out of the financial crisis and we've been on an 11-year cloud boom. And it doesn't look like it's going to stop anytime soon, cloud has really disrupted the on-prem model as we've reported and completely transformed IT. Ironically, the pandemic hit at the beginning of this decade, and created a mandate to go digital. And so it accelerated the industry transformation that we're highlighting here, which probably would have taken several more years to mature but overnight the forced March to digital happened. And it looks like it's here to stay. Now the next wave, we think we'll be much more about business or industry transformation. We're seeing the first glimpses of that. Holger Mueller of Constellation Research summed it up at our event very well I thought, he basically said the cloud is the big winner of COVID. Of course we know that now normally we talk about seven-year economic cycles. He said he was talking about for planning and investment cycles. Now we operate in seven-day cycles. The examples he gave where do we open or close the store? How do we pivot to support remote workers without the burden of CAPEX? And we think that the things listed on this chart are going to be front and center in the coming years, data AI, a fully digitized and intelligence stack that will support next gen disruptions in autos, manufacturing, finance, farming and virtually every industry where the system will expand to the edge. And the underlying infrastructure across physical locations will be hidden. Many issues remain, not the least of which is latency which we talked about at the event in quite some detail. So let's talk about how the Big 3 cloud players are going to participate in this next era. Well, in short, the consensus from the event was that the rich get richer. Let's take a look at some data. This chart shows our most recent estimates of IaaS and PaaS spending for the Big 3. And we're going to update this after earning season but there's a couple of points stand out. First, we want to make the point that combined the Big 3 now account for almost $80 billion of infrastructure spend last year. That $80 billion, was not all incremental (laughs) No it's caused consolidation and disruption in the on-prem data center business and within IT shops companies like Dell, HPE, IBM, Oracle many others have felt the heat and have had to respond with hybrid and cross cloud strategies. Second while it's true that Azure and GCP they appear to be growing faster than AWS. We don't know really the exact numbers, of course because only AWS provides a clean view of IaaS and passwords, Microsoft and Google. They kind of hide them all ball on their numbers which by the way, I don't blame them but they do leave breadcrumbs and clues on growth rates. And we have other means of estimating through surveys and the like, but it's undeniable Azure is closing the revenue gap on AWS. The third is that I like the fact that Azure and Google are growing faster than AWS. AWS is the only company by our estimates to grow its business sequentially last quarter. And in and of itself, that's not really enough important. What is significant is that because AWS is so large now at 45 billion, even at their slower growth rates it grows much more in absolute terms than its competitors. So we think AWS is going to keep its lead for some time. We think Microsoft and AWS will continue to lead the pack. You know, they might converge maybe it will be a 200 just race in terms of who's first who's second in terms of cloud revenue and how it's counted depending on what they count in their numbers. And Google look with its balance sheet and global network. It's going to play the long game and virtually everyone else with the exception of perhaps Alibaba is going to be secondary players on these platforms. Now this next graphic underscores that reality and kind of lays out the competitive landscape. What we're showing here is survey data from ETR of more than 1400 CIOs and IT buyers and on the vertical axis is Net Score which measures spending momentum on the horizontal axis is so-called Market Share which is a measure of pervasiveness in the data set. The key points are AWS and Microsoft look at it. They stand alone so far ahead of the pack. I mean, they really literally, it would have to fall down to lose their lead high spending velocity and large share of the market or the hallmarks of these two companies. And we don't think that's going to change anytime soon. Now, Google, even though it's far behind they have the financial strength to continue to position themselves as an alternative to AWS. And of course, an analytics specialist. So it will continue to grow, but it will be challenged. We think to catch up to the leaders. Now take a look at the hybrid zone where the field is playing. These are companies that have a large on-prem presence and have been forced to initiate a coherent cloud strategy. And of course, including multicloud. And we include Google in this so pack because they're behind and they have to take a differentiated approach relative to AWS, and maybe cozy up to some of these traditional enterprise vendors to help Google get to the enterprise. And you can see from the on-prem crowd, VMware Cloud on AWS is stands out as having some, some momentum as does Red Hat OpenShift, which is it's cloudy, but it's really sort of an ingredient it's not really broad IaaS specifically but it's a component of cloud VMware cloud which includes VCF or VMware Cloud Foundation. And even Dell's cloud. We would expect HPE with its GreenLake strategy. Its financials is shoring up, should be picking up momentum in the future in terms of what the customers of this survey consider cloud. And then of course you could see IBM and Oracle you're in the game, but they don't have the spending momentum and they don't have the CAPEX chops to compete with the hyperscalers IBM's cloud revenue actually dropped 7% last quarter. So that highlights the challenges that that company facing Oracle's cloud business is growing in the single digits. It's kind of up and down, but again underscores these two companies are really about migrating their software install basis to their captive clouds and as well for IBM, for example it's launched a financial cloud as a way to differentiate and not take AWS head-on an infrastructure as a service. The bottom line is that other than the Big 3 in Alibaba the rest of the pack will be plugging into hybridizing and cross-clouding those platforms. And there are definitely opportunities there specifically related to creating that abstraction layer that we talked about earlier and hiding that underlying complexity and importantly creating incremental value good examples, snowfallLike what snowflake is doing with its data cloud, what the data protection guys are doing. A company like Loomio is headed in that direction as are others. So, you keep an eye on that and think about where the white space is and where the value can be across-clouds. That's where the opportunity is. So let's see, what is this all going to look like? How does the cube community think it's going to unfold? Let's hear from theCUBE Guests and theCUBE on Cloud speakers and some of those highlights. Now, unfortunately we don't have time to show you clips from every speaker. We are like 10-plus hours of video content but we've tried to pull together some comments that summarize the sentiment from the community. So I'm going to have John Furrier briefly explain what theCUBE on Cloud is all about and then let the guests speak for themselves. After John, Pradeep Sindhu is going to give a nice technical overview of how the cloud was built out and what's changing in the future. I'll give you a hint it has to do with data. And then speaking of data, Mai-Lan Bukovec, who heads up AWS is storage portfolio. She'll explain how she views the coming changes in cloud and how they look at storage. Again, no surprise, it's all about data. Now, one of the themes that you'll hear from guests is the notion of a distributed cloud model. And Zhamak Deghani, he was a data architect. She'll explain her view of the future of data architectures. We also have thoughts from analysts like Zeus Karavalla and Maribel Lopez, and some comments from both Microsoft and Google to compliment AWS's view of the world. In fact, we asked JG Chirapurath from Microsoft to comment on the common narrative that Microsoft products are not best-to-breed. They put out a one dot O and then they get better, or sometimes people say, well, they're just good enough. So we'll see what his response is to that. And Paul Gillin asks, Amit Zavery of Google his thoughts on the cloud leaderboard and how Google thinks about their third-place position. Dheeraj Pandey gives his perspective on how technology has progressed and been miniaturized over time. And what's coming in the future. And then Simon Crosby gives us a framework to think about the edge as the most logical opportunity to process data not necessarily a physical place. And this was echoed by John Roese, and Chris Wolf to experience CTOs who went into some great depth on this topic. Unfortunately, I don't have the clips of those two but their comments can be found on the CTO power panel the technical edge it's called that's the segment at theCUBE on Cloud events site which we'll share the URL later. Now, the highlight reel ends with CEO Joni Klippert she talks about the changes in securing the cloud from a developer angle. And finally, we wrap up with a CIO perspective, Dan Sheehan. He provides some practical advice on building on his experience as a CIO, COO and CTO specifically how do you as a business technology leader deal with the rapid pace of change and still be able to drive business results? Okay, so let's now hear from the community please run the highlights. >> Well, I think one of the things we talked about COVID is the personal impact to me but other people as well one of the things that people are craving right now is information, factual information, truth, textures that we call it. But here this event for us Dave is our first inaugural editorial event. Rob, both Kristen Nicole the entire cube team, SiliconANGLE on theCUBE we're really trying to put together more of a cadence. We're going to do more of these events where we can put out and feature the best people in our community that have great fresh voices. You know, we do interview the big names Andy Jassy, Michael Dell, the billionaires of people making things happen, but it's often the people under them that are the real Newsmakers. >> If you look at the architecture of cloud data centers the single most important invention was scale-out. Scale-out of identical or near identical servers all connected to a standard IP ethernet network. That's the architecture. Now the building blocks of this architecture is ethernet switches which make up the network, IP ethernet switches. And then the server is all built using general purpose x86 CPU's with DRAM, with SSD, with hard drives all connected to inside the CPU. Now, the fact that you scale these server nodes as they're called out was very, very important in addressing the problem of how do you build very large scale infrastructure using general purpose compute but this architecture, Dave is a compute centric architecture. And the reason it's a compute centric architecture is if you open this, is server node. What you see is a connection to the network typically with a simple network interface card. And then you have CPU's which are in the middle of the action. Not only are the CPU's processing the application workload but they're processing all of the IO workload what we call data centric workload. And so when you connect SSDs and hard drives and GPU is everything to the CPU, as well as to the network you can now imagine that the CPU is doing two functions. It's running the applications but it's also playing traffic cop for the IO. So every IO has to go to the CPU and you're executing instructions typically in the operating system. And you're interrupting the CPU many many millions of times a second. Now general purpose CPU and the architecture of the CPU's was never designed to play traffic cop because the traffic cop function is a function that requires you to be interrupted very, very frequently. So it's critical that in this new architecture where does a lot of data, a lot of these stress traffic the percentage of workload, which is data centric has gone from maybe one to 2% to 30 to 40%. >> The path to innovation is paved by data. If you don't have data, you don't have machine learning you don't have the next generation of analytics applications that helps you chart a path forward into a world that seems to be changing every week. And so in order to have that insight in order to have that predictive forecasting that every company needs, regardless of what industry that you're in today, it all starts from data. And I think the key shift that I've seen is how customers are thinking about that data, about being instantly usable. Whereas in the past, it might've been a backup. Now it's part of a data Lake. And if you can bring that data into a data lake you can have not just analytics or machine learning or auditing applications it's really what does your application do for your business and how can it take advantage of that vast amount of shared data set in your business? >> We are actually moving towards decentralization if we think today, like if it let's move data aside if we said is the only way web would work the only way we get access to various applications on the web or pages to centralize it We would laugh at that idea. But for some reason we don't question that when it comes to data, right? So I think it's time to embrace the complexity that comes with the growth of number of sources, the proliferation of sources and consumptions models, embrace the distribution of sources of data that they're not just within one part of organization. They're not just within even bounds of organizations that are beyond the bounds of organization. And then look back and say, okay, if that's the trend of our industry in general, given the fabric of compensation and data that we put in, you know, globally in place then how the architecture and technology and organizational structure incentives need to move to embrace that complexity. And to me that requires a paradigm shift a full stack from how we organize our organizations how we organize our teams, how we put a technology in place to look at it from a decentralized angle. >> I actually think we're in the midst of the transition to what's called a distributed cloud, where if you look at modernized cloud apps today they're actually made up of services from different clouds. And also distributed edge locations. And that's going to have a pretty profound impact on the way we go vast. >> We wake up every day, worrying about our customer and worrying about the customer condition and to absolutely make sure we dealt with the best in the first attempt that we do. So when you take the plethora of products we've dealt with 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 and Azure overview, I would, I would humbly submit to you that we are leading the way. >> How important are rankings within the Google cloud team or are you focused mainly more on growth and just consistency? >> No, I don't think again, I'm not worried about we are not focused on ranking or any of that stuff. Typically I think we are worried about making sure customers are satisfied and the adding more and more customers. So if you look at the volume of customers we are signing up a lot of the large deals we did doing. If you look at the announcement we've made over the last year has been tremendous momentum around that. >> The thing that is really interesting about where we have been versus where we're going is we spend a lot of time talking about virtualizing hardware and moving that around. And what does that look like? And creating that as more of a software paradigm. And the thing we're talking about now is what does cloud as an operating model look like? What is the manageability of that? What is the security of that? What, you know, we've talked a lot about containers and moving into different, DevSecOps and all those different trends that we've been talking about. Like now we're doing them. So we've only gotten to the first crank of that. And I think every technology vendor we talked to now has to address how are they are going to do a highly distributed management insecurity landscape? Like, what are they going to layer on top of that? Because it's not just about, oh, I've taken a rack of something, server storage, compute, and virtualized it. I know have to create a new operating model around it in a way we're almost redoing what the OSI stack looks like and what the software and solutions are for that. >> And the whole idea of we in every recession we make things smaller. You know, in 91 we said we're going to go away from mainframes into Unix servers. And we made the unit of compute smaller. Then in the year, 2000 windows the next bubble burst and the recession afterwards we moved from Unix servers to Wintel windows and Intel x86 and eventually Linux as well. Again, we made things smaller going from million dollar servers to $5,000 servers, shorter lib servers. And that's what we did in 2008, 2009. I said, look, we don't even need to buy servers. We can do things with virtual machines which are servers that are an incarnation in the digital world. There's nothing in the physical world that actually even lives but we made it even smaller. And now with cloud in the last three, four years and what will happen in this coming decade. They're going to make it even smaller not just in space, which is size, with functions and containers and virtual machines, but also in time. >> So I think the right way to think about edges where can you reasonably process the data? And it obviously makes sense to process data at the first opportunity you have but much data is encrypted between the original device say and the application. And so edge as a place doesn't make as much sense as edge as an opportunity to decrypt and analyze it in the care. >> When I think of Shift-left, I think of that Mobius that we all look at all of the time and how we deliver and like plan, write code, deliver software, and then manage it, monitor it, right like that entire DevOps workflow. And today, when we think about where security lives, it either is a blocker to deploying production or most commonly it lives long after code has been deployed to production. And there's a security team constantly playing catch up trying to ensure that the development team whose job is to deliver value to their customers quickly, right? Deploy as fast as we can as many great customer facing features. They're then looking at it months after software has been deployed and then hurrying and trying to assess where the bugs are and trying to get that information back to software developers so that they can fix those issues. Shifting left to me means software engineers are finding those bugs as they're writing code or in the CIC CD pipeline long before code has been deployed to production. >> During this for quite a while now, it still comes down to the people. I can get the technology to do what it needs to do as long as they have the right requirements. So that goes back to people making sure we have the partnership that goes back to leadership and the people and then the change management aspects right out of the gate, you should be worrying about how this change is going to be how it's going to affect, and then the adoption and an engagement, because adoption is critical because you can go create the best thing you think from a technology perspective. But if it doesn't get used correctly, it's not worth the investment. So I agree, what is a digital transformation or innovation? It still comes down to understand the business model and injecting and utilizing technology to grow our reduce costs, grow the business or reduce costs. >> Okay, so look, there's so much other content on theCUBE on Cloud events site we'll put the link in the description below. We have other CEOs like Kathy Southwick and Ellen Nance. We have the CIO of UI path. Daniel Dienes talks about automation in the cloud and Appenzell from Anaplan. And a plan is not her company. By the way, Dave Humphrey from Bain also talks about his $750 million investment in Nutanix. Interesting, Rachel Stevens from red monk talks about the future of software development in the cloud and CTO, Hillary Hunter talks about the cloud going vertical into financial services. And of course, John Furrier and I along with special guests like Sergeant Joe Hall share our take on key trends, data and perspectives. So right here, you see the coupon cloud. There's a URL, check it out again. We'll, we'll pop this URL in the description of the video. So there's some great content there. I want to thank everybody who participated and thank you for watching this special episode of theCUBE Insights Powered by ETR. This is Dave Vellante and I'd appreciate any feedback you might have on how we can deliver better event content for you in the future. We'll be doing a number of these and we look forward to your participation and feedback. Thank you, all right, take care, we'll see you next time. (upbeat music)

>> Live from Orlando, Florida. It's theCUBE. Covering Microsoft Ignite. Brought to you by Cohesity, and theCUBE's ecosystem partners. >> Welcome back to the CUBE's live coverage of Microsoft Ignite here in Orlando, Florida. I'm your host, Rebecca Knight. Along with my co-host Stu Miniman. We're joined by Andrew Liu. He is the senior product manager at Azure Cosmos DB. Thanks so much for coming on the show Andrew. >> Oh, thank you for hosting. >> You're a first timer, so this will be a lot of fun. So, talk to me a little bit. Azure Cosmos DB is a database for building blazing fast planet scale applications. Can you tell our viewers a little bit about what you do and about the history of Azure Cosmos? >> Sure, so Azure Cosmos DB started with, about eight years ago, where we were also outgrowing a lot of our own database needs with what we had previously built. And a lot of the challenges that we had was really around partitioning, replication, and resource governance. So, I'll talk a little bit about each one. Partitioning is really about solving the problem of scale. Right? I have so much data, doesn't fit on a single machine, and I have so many requests per second. Also doesn't, can't be served out of a single machine. So how do I go and build a system, a database that can elastically scale over a cluster of machines, so I don't have to manually shard, and as a user have to shard a database across many, many instances. This way I really want to be able to scale just seamlessly. The velocity problem is, we also wanted to build something that, can respond in a very fast manner, in terms of latency. So, it's great and all that we can serve lots of request per second, but, what is the response time of each one of those requests? And the resource governance was there to really actually build this as a cloud native database in which we wanted to exploit the properties of our cloud. We wanted to use the economies of scale that we can have basically data centers built all around the world, and build this as a multi, truly multi-tenant service. And by doing so we can also afford the total cost of ownership for us, as well as, a guaranteed predictable performance for the tenants. Now we did this, for initially our first party tenants at Microsoft, where we have made a bet on everything from our Microsoft live platform, to Office, to Azure itself as built on Azure Cosmos DB. And about four years ago we found that hey, this is not really just a Microsoft problem that we're solving, but it's an everybody problem, it's become universal, and so we've launched it out to the open. >> Yeah, Andrew that's, great point, and I want you to help unpack that for us a little bit because you know, we've been saying on theCUBE for many years, distributed architectures are some of the toughest challenges of our time, but, if I'm a Facebook, or a Google, or a Microsoft, I understand some of the challenges, and I understand why I need it, but, when you talk about scale, well, scale means a lot of different things to a lot of different people. So, how does Cosmos? What does that mean to your users, end users, why do they need this? You know, haven't they just felt some microservices architecture? And they'll just leverage, ya know what's in Azure. And things like that. How does this global scale impact the typical user? >> So I'm actually seeing this come in different types of patterns for different types of industries. So for example, in manufacturing we're commonly seeing Cosmos DB used really for that scalability for the write scalability, and having many, many concurrent writes per second. Typically this is done in an IoT telemetry, or an IoT device registry case. So let's use one of our customers for example, Toyota. Each year they're shipping millions of vehicles on the road, and they're building a big connected car platform. The connected car platform allows you to do things like, whenever it alerts an airbag gets deployed, they can go and make sure and call their driver, hey, I saw the airbag was deployed are you okay? And if the user doesn't pick up their phone, immediately notify emergency services. But the challenge here is if each year I'm shipping millions of vehicles on the road, and each of 'em has a heartbeat every second, I'm dealing with millions of writes per second, and I need a database that can scale to that. In contrast, in retail I'm actually seeing very different use cases. They're using more of the replication side of our stock where they have a global user base, and they're trying to expand an eCommerce shop. So for example ASOS is a big fashion retailer, they ship to 200 different countries globally, and they want to make sure that they can deliver real-time experiences like real-time personalization, and based off of who the user is recommended set of products that is tailored to that user. Well now what I need is a data set that can expand to my shoppers across two different hundred, 200 countries around the globe, and deliver that with very, very low latency so that my web experience is also very robust. So what they use is our global distribution, and our multi-mastering technology. Where we can actually have a database presence, similar to like what a CDN does for static content, we're doing for our dynamic evolving content. So in a database your work load, typically your data set is evolving, and you want to be able to run queries with consistency over that. As opposed to in CDN you're typically serving static assets. Well here we can actually support those dynamic content, and then build these low latency experiences to users all around the globe. The other area we see a lot of usage is in ISV's for mission critical workloads. And the replication actually gets us two awesome properties, right? One is the low latency by shipping data closer to where the user is, but the other property you get is a lot of redundancy, and so we actually also offer industry leading SLA's where we guarantee five nines of availability, and the way we're able to do so is, with a highly redundant architecture you don't care if let's say a machine were to bomb out at any given time, because we have multiple redundant copies in different parts of the globe. You're guaranteed that your workload is always online. >> So my question for you is, when you have these, you just described some really, really interesting customer use cases in manufacturing, in retail, do you then create products and services for each of these industries? Or do you say hey other retail customers, we've noticed this really works for this customer over here, how do you go out to the community with what you're selling? >> Ah, got it. So we actually have found that this can be a challenging space for some of our customers today, 'cause we have so many products. The way we kind of view it is we want to have a portfolio, so that you can always choose the right tool for the right job. And I think a lot of how Microsoft has evolved as a business actually is around this. Previously we would sell a hammer, and we'd tell you don't worry everything's a nail, even if it looks like a screw let's just pretend it's a nail and whack it down. But today we've built this big vast toolbox, and you can think of Cosmos DB as just one of many tools in our vast toolbox. So if you have a screw maybe you pickup a screwdriver, and screw that in. And the way Azure works is then if we have a very comprehensive toolbox, depending on what precise scenario you have, you can kind of mix and match the tools that fit your problem. So think of them as like individual Lego blocks, and whether you're building like a death star, or an x-wing, you can go, and assemble the right pieces for your application. >> Andrew, some news at the show around Cosmos DB. Share us what the updates are. >> Oh sure, so we're really excited to launch a few new features. The highlights are multi-master, and Cassandra API. So multi-master really exploits the replicated nature of our database. Before multi-master what we would do is, we would allow you to have a globally distributed database in which you can have write requests go to single region, and reads being served out of any of these other locations. With multi-master we've actually made it so that each of those replicas we've deployed around the globe can also accept write requests. What that translates to from a user point of view is number one, your write requests are a lot faster, they're super low latency, single-digit millisecond latency in fact. No matter where the user is around the globe. And number two, you also get much higher write availability. So even if let's say, we're having a natural disaster, we had a nasty hurricane as you know pass through on the east coast last week, but with a globally distributed database the nice thing is even if you have, let's say, a power disruption in one region of the world, it doesn't matter cause you can then just fail over, and talk to another data center, where you have a live replica already located. So we just came out with multi-master. The short summary is low latency writes, as well as high available writes. The other feature that we launched is Cassandra API, and as you know this is a multi-model, multi-API database. What that means is, what we're trying to do is also meet our users where they are. As opposed to pushing our proprietary software on them, and we take the whole concept of vendor lock-in very, very seriously. Which is why we make such a big bet on the open source ecosystem. If you already have, let's say a MongoDB application, or a Cassandra application, but you'd really love to be able to take advantage of some of the novel properties that we've built with building a fully managed multi-master database. Well, what we've done is we've implemented this as a wire level protocol on the server side. So it can take an existing application, not change a single line of code, and point it to Cosmos DB as a back-end, and then take advantage of Cosmos DB as your database. >> One of the interesting things if you look at the kind of changing face of databases, it's how users are being able to leverage their data. You talk about everything from you know, I think Cassandra back, and some of the big data discussions, today everything's AI which I know is near and dear to Microsoft's heart. Satya Nadella I'm talking about, how do you think of the role of data in this solution set? >> Sorry, can you say that one more time? >> So, how customers think about leveraging data, how things like Cosmos allow them to really extract the value out of data, not just be some database that kind of stuck in the back-end somewhere. >> Yeah, yeah. I mean a lot of it is the new novel experiences people are building. So for example, like the connected car platform, I'm seeing people actually build this, and take advantage of new novel territories that a traditional automobile manufacturer used to not do. Not only are they building experiences around, how do they provide value to their end users? Like the air bag scenario, but they're also using this as a way of building value for their business, and how to make sure that, hey when, next time you're up for an oil change that they can send a helpful reminder, and say hey I noticed you're due for an oil change in terms of mileage. Why don't I just go set up an appointment, just up for you, as well as other experiences for things, like when they want to do fleet management, and do partnerships with either ride sharing companies like Uber, and Lyft, or rental car companies like Avis, Hertz, et cetera. I've also seen people take advantage of, taking kind of new novel experiences through databases, through AI, and machine learning. So for example, the product recommendations. This was something that historically, when I wanted to do recommendations a decade ago, maybe I have some big beefy data lake running somewhere in the back-end, it might take a week to munch through that data, but that's okay, a week later once I'm ready, I'll send out some mail, maybe some email to you, but today when I want to actually show live right when the user is browsing my website, my website has to load fast right? If my goal is to increase conversions on sales, having a slow running website is the fastest way for my user to click the back button. But if I want to build real-time personalization, and want to generate let's say a recommendation within 200 millisecond latency, well now that I have databases that can guarantee me single-digit millisecond latency, it gives me ample time to actually improve the business logic for those recommendations. >> I want to ask you a question about culture, because you are based at the mothership in Redmond, Washington. So we heard Satya Nadella on the main stage today talk about tech intensiveness, tech intensity, sorry, this idea that we need to not only be adopting technology, but also building the latest, and greatest. I'm curious about, how that translates at Microsoft's campus, and sort of how, how this idea is, infuses how you work with your colleagues, and then also how you work with your customers and partners? >> I think some of the biggest positive changes I've seen over the last decade has been how much more of a customer focus we have today then ever. And i think a lot of things have led to that. One is, just the ability to ship much faster. As we move to Cloud services we're no longer in these big box product release cycles of building a product, and waiting like one or two years to ship it to our users. But now we can actually get some real-time feedback. So as we go, and ship, and deploy software, we actually deploy even on a weekly cadence over here. What that allows us to do is actually experiment a lot more, and get real-time feedback, so if we have an idea, and rather than having to go through a long lengthy vetting process, spending years building, and hoping that it really pays off. What we can do is we can just go talk to our users, and say hey, ya know, we have an idea for our future. We'd love to get your feedback, or a lot of times honestly our customers actually come to us, where we're so tightly engaged these days, that when, users even come to us, and say like hey, what do you think about this idea? It would really add a lot of value to my scenario. We go, and try to root cause that, really get an idea of what exactly that they need. But then we can turn that around in blazing fast time. And I think a lot of the shift to Cloud services, and being able to avoid the overhead of well we got to wait for this ship train, and then wait for the right operation personnel to go and deploy the updates. Now that we can control our own destiny, and just ship on a very, very fast cadence, we're closer to our users, and we experiment a lot more, and I think it's a beautiful thing. >> Great, well Andrew thank you so much for coming on theCUBE, it was fun talking to you. >> Oh yeah, thank you for hosting. >> I'm Rebecca Knight, for Stu Miniman, we will have more from theCUBE's live coverage of Microsoft Ignite coming up just after this. (techno music)