Big Data Virtualization

Explaining big data virtualization requires understanding the general principles of virtualization as a whole. The essential idea with virtualization is that heterogeneous or distributed systems are represented as complex systems through specific interfaces that replace physical hardware or data storage designations with virtual components. For example, in hardware virtualization, software makes a system of physical computers into a system of "logical," or virtual, computers. This virtualization system can present parts of two or more different storage drives on two or more computers as a single "Drive A" that users access as a unified whole. In network virtualization, systems may represent a set of physical nodes and resources as a different set of virtual components.

One way to think about a big data virtualization resource is as an interface that’s created to make big data analytics more user-friendly for end users. Some professionals also explain this as creating a "layer of abstraction" between the physical big data systems, i.e. where each bit of data is individually housed on computers or servers, and creating a virtual environment that is much easier to understand and navigate. Big data virtualization aims to combines all of these distributed locations into one easy virtual element.

The business world has developed a sophisticated set of big data analytics tools, but not all of them support the principle of big data virtualization, and this kind of work has its own challenges. Some claim that companies are slow to tackle big data virtualization, because its implementation is considered tedious and difficult. However, this may change as service providers continue to craft products and services that companies want, and skilled IT professionals look at the best ways to make changes between how a system is physically set up, and how it is used through an overall software architecture.