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  • At the present time, Pig's infrastructure layer consists of a compiler that produces sequences of Map-Reduce programs, for which large-scale parallel implementations already exist e.g., the Hadoop subproject.

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  • The Map-Reduce paradigm, and Hadoop in particular, have been a big success.

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  • That means that not only Erlang and interpreted languages like Javascript or Python can be used to write Map-Reduce jobs but also JVM based languages.

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  • The more I look at Python generator functions and the idea of using Map-Reduce, the more I realize that Visitors and Subclass Extension are not the best design pattern.

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  • Azure High Scale Compute (HSC) is a Windows Azure implementation of the Map-Reduce parallel processing architecture.

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  • That said, Map-Reduce is not the only important piece of compute infrastructure.

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  • Writing complex or even simple computations in raw Map-Reduce is verbose for programmers and intimidating for others who might want to play with the data.

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  • Abstractions over Map-Reduce like Cascading make hard things possible.

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  • That said, there are very generic abstractions like Map-Reduce that work well for a lot of use cases.

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  • At the present time, Pig's infrastructure layer consists of a compiler that produces sequences of Map-Reduce programs, for which large-scale parallel implementations already exist (e.g., the Hadoop subproject).

    Softpedia - Windows - All

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