Distributed data processing is related to parallel data processing in that the same principle of divide and conquer is used.
options:
True
False
The correct answer and explanation is:
The correct answer is True.
Both distributed data processing and parallel data processing share the same underlying principle of divide and conquer, but they operate in different contexts. In distributed data processing, tasks or data are distributed across multiple computing nodes, often located on different machines or even in different geographical locations. Each node processes a subset of the data, and the results are combined to produce the final output. This method allows for scalability and fault tolerance, as the system can continue to function even if one or more nodes fail.
On the other hand, parallel data processing typically occurs within a single machine or a tightly coupled system, where multiple processors or cores work on different parts of a data set simultaneously. This type of processing maximizes the speed and efficiency of computations by leveraging multiple processors to handle different tasks concurrently.
The key similarity lies in how the tasks are broken down into smaller, independent units of work, which are then processed in parallel or distributed across multiple systems. Both methods aim to improve performance by reducing the overall time needed for data processing. In parallel processing, tasks are split across processors within a single system, while in distributed processing, tasks are distributed across multiple systems or nodes.
The distinction between these two approaches is primarily in the scope and infrastructure. Distributed systems can involve significant network overhead, which introduces latency and communication costs between nodes. Parallel processing, while potentially more efficient within a single system, may be limited by hardware resources such as the number of available processors.
In conclusion, while distributed and parallel processing both rely on the divide and conquer principle to improve data processing efficiency, their implementation and the systems they operate on can differ significantly.