Getting More for Less in Optimized MapReduce Workflows

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Provided by: University of Peloponnese
Topic: Big Data
Format: PDF
Big data requires new technologies to process large quantities of data in scalable, efficient, and cost-effective way. As digital convergence leads to new sources of data and as the cost of data storage is decreasing, the businesses are exploiting the MapReduce paradigm and its open-source implementation Hadoop as a platform choice for efficient big data processing and advanced analytics over unstructured information. The data-driven insights become a critical competitive differentiator for driving and optimizing business decisions.
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