What Is Data Virtualization?
Data virtualization is similar to a virtual magician in that it gathers information from various locations and makes it look like it is all located in the exact location. Data virtualization lets you use data from numerous sources as if stored in one place. Imagine you want to bake a cake and have all the ingredients spread throughout your kitchen. With data virtualization, you can treat all those components as contained within a single bowl, even though they are dispersed throughout the kitchen. The same holds for your data, which may be stored in various databases, file systems, cloud services, and other locations; nonetheless, data virtualization gives the impression that all your data is kept in a single location. Data virtualization's technical term is "federation." If you had a map that showed you exactly where all of the components in your kitchen were located, you wouldn't need to look for them, and that's precisely what Federation is. You can view all of your data as if it were located in a single location when you use data federation because it shows you exactly where it is kept. Another essential word is "metadata," which can be compared to compiling a list of all the elements required to bake a cake in advance so that one knows what is necessary to get started. Metadata is information about data that is used in data virtualization. This information may include the data's structure, format, and relationships with other data. The phrase "virtual data layer" is another term used in the industry. This is like having a mixing bowl in your kitchen to combine items while they're still in their containers. The virtual data layer in data virtualization gives a single access point to all the data, simplifying the process of gaining access to and utilizing the data. Data virtualization is like having a virtual kitchen assistant who takes all your ingredients and makes them look like they're all in one spot, making cake baking easier (or using your data). The accessibility and utilization of data must be made as easy as humanly feasible!
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