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Advantages of Distributed Data Processing

• Lower Cost

Larger organizations invest in expensive mainframe and supercomputers to function as centralized servers. Each mainframe machine, for example, costs several hundred thousand dollars versus several thousand dollars for a few minicomputers, according to the University of New Mexico. Distributed data processing considerably lowers the cost of data sharing and networking across an organization by comprising several minicomputers that cost significantly less than mainframe machines.

• Reliable

Hardware glitches and software anomalies can cause single-server processing to malfunction and fail, resulting in a complete system breakdown. Distributed data processing is more reliable, since multiple control centers are spread across different machines. A glitch in any one machine does not impact the network, since another machine takes over its processing capability. Faulty machines are quickly isolated and repaired. This makes distributed data processing more reliable than single-server processing systems.

• Improved Performance and Reduced Processing Time

Single computers are limited in their performance and efficiency. An easy way to increase performance is by adding another computer to a network. Adding yet another computer will further augment performance, and so on. Distributed data processing works on this principle and holds that a job gets done faster if multiple machines are handling it in parallel, or synchronously. Complicated statistical problems, for example, are broken into modules and allocated to different machines where they are processed simultaneously. This significantly reduces processing time and improves performance.

• Flexible

Individual computers that comprise a distributed network are present at different geographical locations. For example, an organizational-distributed network comprising of three computers can have each machine in a different branch. The three machines are interconnected via the Internet and are able to process data in parallel, even while at different locations. This makes distributed data-processing networks more flexible. The system is flexible also in terms of increasing or decreasing processing power. For example, adding more nodes or computers to the network increases processing power and overall system capability, while reducing computers from the network decreases processing power.