 Thank you for making it on time. This is awesome. I was a little worried it was going to be empty. I'm going to talk about something that is really near and dear to my heart, which is application, recovery, and resiliency, as well as data management in cloud and Kubernetes architectures. And I think it's a challenge that many of us are starting to struggle with. And the key reason for that, of course, is that more and more customers and organizations are building really critical infrastructure into Kubernetes. These stats are kind of evolving continuously, but it's a trend that keeps being enforced. We see that in the future, we're going to have a lot more applications and services being architected in Kubernetes. The other key thing is vendors are starting to actually provide their shrink-wrapped applications and workloads into Kubernetes frameworks as well. So they're containerizing their well-known applications and making them ready to run in Kubernetes. That means our critical applications are going to go in there as well. The counter to this, though, is that when we survey our field or our customers, and they're using the cloud-native capability to protect their data sets, they're struggling. A lot of them are dealing with long recovery times and even data loss. So if this trend is going to continue, and that we think it will, we're going to see really critical applications, big data sources, transactional data sources, databases, et cetera, moving into Kubernetes. And they're going to need persistent storage. They're going to need data protection. And they're going to need disaster recovery orchestration. But it's not just that. The capability to deliver that functionality should be containerized. It should be elastic in nature, and it should be cloud-native, integrated with the platforms and functionality that we need. We have a vision for this. It's called autonomous data management, a platform that essentially is cloud optimized. API-enabled, microservices containerized that is elastic in nature. The capability to actually deliver these services as a service or build your own within a services framework. Make sure it's multi-cloud and multi-tenant, so it works the same way in Kubernetes and infrastructure as a service or for past platforms. That it can deliver those advanced functionalities like automated capacity management, self-optimization, recovery of service and resiliency of service when things fail, as well as security end-to-end. So we can start to alleviate some of the pressures on the admins and take away, through automation, through AI and ML, some of those hard challenges that we have to deal with on a daily basis, to actually deliver on SLAs that our applications have, make sure that they're ransomware proof, and integrate them with a CECD infrastructure. So what would this actually look like if we actually deliver it? Well, think of a data management platform where we apply a set of criteria for how we're going to protect a workload, whether it's a namespace in Kubernetes, past platform, past service, or even infrastructure as a service in public cloud or in private cloud on-prem. Developers would pick up the criteria as part of their structure, or we would apply it automatically to the workload as those workloads get deployed based on the criteria that the business might have set. So that we ensure we drive an outcome of security for our data, and predictability for recovery, meaning that you get an outcome where, essentially, data is protected end-to-end out of the box. Resiliency can be managed, tested, and validated. That you can have confidence that you can recover when you need to, that you're protected against ransomware, and that you have an optimized and efficient usage of your infrastructure. Now, a lot of work and a lot of capability to integrate this into Kubernetes has gone on. And if you'd like to come and see and understand exactly what we have done and how we've contributed to actually deliver this capability, you can come to our booth later on today with D3 in the main hall. And with that, I'm just going to say thank you very much. Really appreciate your time.