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    Map Reduce is a programming model for processing large amounts of data in a parallel and distributed fashion.It is useful for large, long-running jobs that cannot be handled within the scope of a single request, tasks like:-Analyzing application logs-Aggregating related data from external sources-Transforming data from one format to another-Exporting data for external analysis–Mapper: Consumes input data, analyzes it (usually with filter and sorting operations), and emits tuples (key-value pairs)–Reducer: Consumes tuples emitted by the Mapper and performs a summary operation that creates a smaller, combined result from the Mapper data.I spent some time with the VP of an interesting company recently.The company is pretty good size (several hundred million dollars in revenue and many hundreds of employees).Item counts are displayed categorized under multiple “spaces” (workspaces). The user can click on a space to expand the count of items grouped by status.The screenshots below show how the table looks when fully collapsed and then when one or more spaces are expanded.

    Functionality The target functionality is a one-page application that shows data in a single table.This model would not scale to large clusters (hundreds or thousands of nodes) if the components were allowed to share data arbitrarily.The communication overhead required to keep the data on the nodes synchronized at all times would prevent the system from performing reliably or efficiently at large scale. The intent of these tutorials is to provide good understanding of Map Reduce.In addition to these tutorials, we will also cover common issues, Interview questions and How To’s of Map Reduce.

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