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27 Feb 2015

Tailored Big Data Solutions Using MapReduce Design Patterns

What are MapReduce Design Patterns? MapReduce is basically a programming model and an associated implementation for processing and generating large data sets with a parallel, distributed algorithm on a cluster, while design patterns help in providing a common framework for solutions. Thus, MapReduce design patterns can be labeled as recurring patterns in big data-related problem-solving. Why learn...
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What are MapReduce Design Patterns?

MapReduce is basically a programming model and an associated implementation for processing and generating large data sets with a parallel, distributed algorithm on a cluster, while design patterns help in providing a common framework for solutions. Thus, MapReduce design patterns can be labeled as recurring patterns in big data-related problem-solving.

Why learn MapReduce Design Patterns?

Design Patterns are problem specific templates developers have perfected over the years for writing correct and efficient codes. It encodes correct practices for solving a given piece of problem, so that a developer need not re-invent the wheel in the hard way. MapReduce program bugs can be hard to debug – using well established Design Patterns can alleviate some of that pain.

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