 I think one of the things, and we talked about COVID, it's a personal impact to me, but other people as well. One of the things that people are craving right now is information, factual information, truth, tech truth as we call it. But here, this event for us, Dave, is our first inaugural editorial event, Rob, Bo, Kristen, Nicole, the entire CUBE team, SiliconANGLE, 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, the people making things happen, but it's often the people under them that are the real newsmakers. If you look at the architecture of our cloud data centers, the single most important invention was Scaleout. Scaleout 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 servers are all built using general purpose x86 CPUs with DRAM, with SSD, with hard drives all connected 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 is a compute-centric architecture, and the reason it's a compute-centric architecture is if you open this server node, what you see is a connection to the network, typically with a simple network interface card, and then you have CPUs, which are in the middle of the action. Not only are the CPUs 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 GPUs and 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 CPUs and the architecture of these CPUs 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 there's a lot of data, a lot of e-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 have 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 let's move data aside, if we said the only way web would work, the only way we get access to various applications on the web or pages is 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 organization, they're 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 computation and data that we put in 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 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 in the way we build apps. 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've 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. 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're worried about making sure customers are satisfied and we're 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're doing, if you look at the announcement we had made over the last year, has been tremendous momentum around that. You know, the thing that is really interesting about where we have been versus where we're going is we spent 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, you know, dev sec ops 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 going to do a highly distributed management and security 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 sort of a storage compute and virtualized it. I now 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, you know, 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, when there was the next bubble burst and the recession afterwards, we moved from Unix servers to Windows and Intel, x86 and eventually Linux as well. Again, we made things smaller, going from million dollar servers to $5,000 servers, shorter libs 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 the coming decade, they're going to make it even smaller, not just in space, which is size, you know, with functions and containers and virtual machines, but also in time. So I think the right way to think about edge is 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 data in the clear. When I think of shift left, I think of that Mobius that we all look at all of the time in 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 CI CD pipeline, long before code has been deployed to production. Doing 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 I have the right requirements. So that goes back to people, making sure you have the partnership that goes back to leadership in 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 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, whether it digital transformation or innovation, it still comes down to understanding the business model and injecting and utilizing technology to grow or reduce costs, grow the business or reduce costs.