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research-article

Hive: a warehousing solution over a map-reduce framework

Published: 01 August 2009 Publication History

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.

References

[1]
A. Pavlo et. al. A Comparison of Approaches to Large-Scale Data Analysis. Proc. ACM SIGMOD, 2009.
[2]
C. Ronnie et al. SCOPE: Easy and Efficient Parallel Processing of Massive Data Sets. Proc. VLDB Endow., 1(2):1265--1276, 2008.
[3]
Apache Hadoop. Available at http://wiki.apache.org/hadoop.
[4]
Hive Performance Benchmark. Available at https://issues.apache.org/jira/browse/HIVE-396.
[5]
Hive Language Manual. Available at http://wiki.apache.org/hadoop/Hive/LanguageManual.
[6]
Facebook Lexicon. Available at http://www.facebook.com/lexicon.
[7]
Apache Pig. http://wiki.apache.org/pig.
[8]
Apache Thrift. http://incubator.apache.org/thrift.

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cover image Proceedings of the VLDB Endowment
Proceedings of the VLDB Endowment  Volume 2, Issue 2
August 2009
367 pages

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VLDB Endowment

Publication History

Published: 01 August 2009
Published in PVLDB Volume 2, Issue 2

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