Why Data Virtualization Is Good For Big Data Analytics?
- What Is Data Virtualization ? Data virtualization, like any virtualization, is an approach that allows you to access, administer, and optimize a heterogeneous infrastructure as if it were a single, logically unified resource. This enables you to abstract the external interface from the internal implementation of some service, functionality, or other resource.
- Data Virtualization has three characteristics that support the scalability and operating efficiency required for big data environments:
- Partitioning In virtualization, many applications and operating systems are supported in a single physical system by partitioning the available resources.
- Isolation Each virtual machine is isolated from its host physical system and other virtualized machines. Because of this isolation, if one virtual instance crashes, the other virtual machines and the host system aren’t affected. In addition, data isn’t shared between one virtual instance and another.
- Encapsulation A virtual machine can be represented as a single file. It helps in identifying it based on the services it provides. By encapsulating an entire system, a virtual machine becomes compatible with all standard operating systems and applications
- Conclusion Virtualization of servers, storage, applications, processor, memory, and network ensures effective sharing of resources. For instance, using server virtualization, processing power utilization increases by ~65%. Virtualization isn’t considered a technical requirement of “big data.” However, software frameworks seem to display more efficiency in a virtualized environment..