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What is the difference between parallel and distributed computing if we consider a Hadoop framework?

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Distributed computing is a model in which system models are shared among multiple computer like online game . In parallel computing, all processors may have access to a shared memory to exchange information between processors. In distributed computing, each processor has its own private memory (distributed...
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Distributed computing is a model in which system models are shared among multiple computer like online game . In parallel computing, all processors may have access to a shared memory to exchange information between processors. In distributed computing, each processor has its own private memory (distributed memory). Information is exchanged by passing messages between the processors read less
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Parallel computing is a generic thing: two or more processes run at the same time to cooperatively achieve a common task. Map reduce is parallel computing framework too but provides only two constructs:map and reduce. While this may seem too restrictive there are quite a bit of problems ranging from...
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Parallel computing is a generic thing: two or more processes run at the same time to cooperatively achieve a common task. Map reduce is parallel computing framework too but provides only two constructs:map and reduce. While this may seem too restrictive there are quite a bit of problems ranging from simple ETL (extract, transform, and load) to machine learning that can be expressed using maps and reduces. Map reduce frameworks such as Hadoop let’s the user worry about writing the logic for Map and Reduce tasks and takes care of doing the distribution of tasks. With built-in fault tolerance, this makes it great to come up with solutions to large scale data analytics problems easily. read less
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