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Essay: MapReduce for knowledge management

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  • Subject area(s): Computer science essays
  • Reading time: 2 minutes
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  • Published: 18 September 2015*
  • File format: Text
  • Words: 479 (approx)
  • Number of pages: 2 (approx)

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The explosive growth of knowledge is making important challenges for several firms. Challenges embrace physical storage, lack of knowledge quality, and also the escalating prices of managing, storing and process massive, growing information sets. Organizations would like the flexibility to investigate and gain insights from huge amounts of maintained information for a range of business functions as well as client charge, fraud detection and data processing to search out new revenue opportunities. This assortment of challenges is usually said because the huge information downside.
Driven by economic necessity and also the have to be compelled to store and method massive amounts of unstructured information a lot of cost-effectively, several organizations have turned to distributed file systems and information grids as some way to faithfully store and method huge information. As organizations begin to deploy new technologies to subsume the challenge of storage and analytics, these environments quickly become business crucial. Organizations have to be compelled to think about factors like measurability, responsibleness, manageableness and on-going prices in their strategic designing even as they might for the other infrastructure supporting enterprise-class applications.
MapReduce has emerged as a preferred resolution for handling a range of huge information issues. MapReduce may be a programming model which will analyze massive amounts {of information of knowledge of information} quickly by distributing calculations in parallel and by moving process nearer to the physical data. Massive datasets square measure segmental into multiple smaller ‘chunks’ against those tasks will execute severally and in parallel on a distributed grid. Distributing each calculates and information during this fashion across an ascendable grid of artifact systems provides organizations with {a better far better much better higher a stronger a lot of robust an improved} thanks to manage massive volumes of knowledge on a more price effective design.
At a high level, Hadoop are often viewed as having 3 logical layers. The primary layer is that the application layer. MapReduce applications square measure written to a customary Java MapReduce application programming interface (API). nowadays there square measure a range of economic and open supply applications written to the MapReduce programming interface as well as Pig (a application-oriented language for making MapReduce applications) and Hive (a information warehouse infrastructure designed on prime of MapReduce).
The second layer is that the Hadoop MapReduce layer comprised of the MapReduce middleware and a distributed execution and work management layer. MapReduce starts processes like the task hunter and Task Trackers on cluster nodes. Apache Hadoop incorporates basic work hardware to meet these management and programming needs.
The third layer is that the distributed filing system layer wherever the particular information is keep. The Hadoop distribution includes a distributed filing system known as Hadoop filing system (HDFS) for storing information. Most business implementations of Hadoop offer support for the HDFS filing system, but betting on the distribution, it’s conjointly potential to leverage tried business distributed file systems.

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