How does that fit for data? I'm just putting a container on data sets? Who's addressing the envelope of that container?
How is that addressable? I mean, how does it work? >> Let me give you an analogy. So you go back to the year 1955. There is no standards in any shipping port around the world. Everybody is literally building their own containers, building their own ships, building their own trucks. It's incredibly expensive and takes forever to get cargo to move from one place to the next. 1956, a guy named Malcom McClean, he invents the first intermodal shipping container, patents it. It becomes the standard. So now, every port, every container looks identical. What's the benefit? Sure, it made more flexibility. Saved lot of money, 90% of the cost came out of shipping a container. But the biggest thing is it changed commerce. So, you look at GDP at that time, it took off. All because of the standardization around a form factor that made it accessible to everybody. Now, let's put that in the IT world. We got containers for the application world. Made it much easier to deploy, a standard, again. >> Yeah, and program around. >> More cost-effective, more-- Yep, exactly. What's the cargo in IT? It's data. Data is the cargo, that's what's sitting inside the container. Now you have to say, how do we actually take the same concepts that we did for applications, make that available for data so that my data can fit anywhere? That's what we're doing. >> How does that work and what's the impact to the customer? >> Is it IBM software that you're doing? Is it Kubernetes open source software? Just tie that together for me. So IBM Cloud Private is our Kubernetes distribution, with some different pieces we put on it. When you add the Cloud Private for Data, it's got a Spark Engine, like everything we do it's based on open source to start with. And then we have an experience for a data scientist, an experience for a data analyst. It's your view to your enterprise data. You'll love the UI when you see it. First, above the fold, all my machine learning models in the organization, what's working, what's not working. Below the fold, what's my data? Structured or unstructured? Sensitive, non-sensitive? I click it on, I can see all of my data. Hadoop, Cloud-A, Cloud-B, Cloud-C, on-premise system. It's get a view to all of your data. >> So is the purpose to move the data around? >> No, the purpose is actually the exact opposite. Leave the data in place, but be able to treat it as a single data environment. We're doing a lot of work with Federation, our SQL technology which historically, as we all know, Federation hasn't really performed. We have it performing. >> Okay, so I'm just, in the use case in my head, so I store the data on my private, secure, comfortable, feeling good about it, but I have a public cloud app. How does that work? Is it a replica of the data? Is it just the container that makes it addressable? How does that move across?
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