Distributed Data Processing (DDP)
It is a configuration in which many geographically dispensed or
distributed independent computers are connected by means of computer
networks. In this type of configuration Programs, data and other
resources are shared among several users who are geographically far away
from one another. This provides the facility of better resource use to
the end users.
This approach is different from centralized system because computers are installed at different locations and each of them performs independent data processing. Each computer in DDS is designed for a specific task.
Advantages of Distributed Data Processing
1) In Distributed data processing resources can be used in centralized or non centralized manner.
2) Smaller or complex jobs can be easily handled. Small jobs are done with the help of Personal Computers whereas complex jobs are performed by complex systems.
3) Quick and better access to data regardless of geographical location.
4) Failure in one system does not result in whole system failure.
5) With the help of multiple processors peak load time of network can be reduced.
6) Better resources are available to end users.
Disadvantages of Distributed Data Processing
1) Since data is transmitted on the network which can be hacked. Hence security is major concern in this technique.
2) Resources can be shared only if they follow the same protocols.
3) Due to decentralization of resources centralized monitoring and control is not possible.
4) It is very difficult to diagnose failures.
5) Management and Control is more complex.
This approach is different from centralized system because computers are installed at different locations and each of them performs independent data processing. Each computer in DDS is designed for a specific task.
Advantages of Distributed Data Processing
1) In Distributed data processing resources can be used in centralized or non centralized manner.
2) Smaller or complex jobs can be easily handled. Small jobs are done with the help of Personal Computers whereas complex jobs are performed by complex systems.
3) Quick and better access to data regardless of geographical location.
4) Failure in one system does not result in whole system failure.
5) With the help of multiple processors peak load time of network can be reduced.
6) Better resources are available to end users.
Disadvantages of Distributed Data Processing
1) Since data is transmitted on the network which can be hacked. Hence security is major concern in this technique.
2) Resources can be shared only if they follow the same protocols.
3) Due to decentralization of resources centralized monitoring and control is not possible.
4) It is very difficult to diagnose failures.
5) Management and Control is more complex.
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