MapReduce is a technique for processing large datasets in a distributed computing environment. It allows organizations to analyze large, unstructured data like social media signups to determine insights through tasks like sentiment analysis. Companies like Google, Yahoo, Facebook, and Amazon use MapReduce to handle indexing, spam detection, data mining, and product clustering on their big data. MapReduce works by delegating tasks to nodes in a cluster and then combining the results into a single answer.

1. MapReduce with Big Data
Jagriti Srivastava

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Tools

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Large volume of data – structured and unstructured
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It’s what organizations do with the data that matters.
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Helps for better decisions and strategic business moves.
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Map Reduce for big data scenario :
– Data of total social media sign up from different countries.
– Listing of those data using Map Reduce technique.
– Search engines could determine page views, and marketers
could perform sentiment analysis using MapReduce.
Big Data with Map Reduce

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MapReduce Implementation
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At Google:
– Index building for Google Search
– – Article clustering for Google News
– Statistical machine translation
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At Yahoo!:
– Index building for Yahoo! Search
– Spam detection for Yahoo! Mail
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At Facebook:
– Data mining
– Ad optimization
– Spam detection Example
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At Amazon:
– Product clustering
– Statistical machine translation

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Why MapReduce in BigData
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Responsible for delegating work to the different nodes in the cluster/map
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Collects all the results from the query into one cohesive answer.
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Components of MapReduce :
– JobTracker (the master node),
– TaskTrackers (these are agents within each cluster, with functions of their own) and
– JobHistoryServer (deployed as separate function, but a component that tracks jobs.

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