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August 1, 2009Proceedings of the VLDB Endowment1,548 citations

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ATAshish ThusooJSJoydeep Sen SarmaNJNamit Jain

Key Points

  • The aim is to explore Hadoop's role and effectiveness in handling large data sets for business intelligence applications.
  • Analysis of Hadoop as an open-source map-reduce implementation.
  • Evaluation of traditional warehousing solutions against Hadoop's performance.
  • Examination of the programming model requirements for developers.
  • Hadoop provides a sustainable alternative for storing and processing large data sets.
  • Traditional warehousing solutions are deemed prohibitively expensive, encouraging the shift to Hadoop.
  • The complexity of the map-reduce programming model poses challenges in maintenance and reusability.

Abstract

The size of data sets being collected and analyzed in the industry for business intelligence is growing rapidly, making traditional warehousing solutions prohibitively expensive. Hadoop 3 is a popular open-source map-reduce implementation which is being used as an alternative to store and process extremely large data sets on commodity hardware. However, the map-reduce programming model is very low level and requires developers to write custom programs which are hard to maintain and reuse.

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Thusoo et al. (2009) studied this question.

synapsesocial.com/papers/6a0e9a047b06478e784c5bcfhttps://doi.org/10.14778/1687553.1687609
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