Ashish Thusoo
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.
- A. Pavlo et. al. A Comparison of Approaches to Large-Scale Data Analysis. Proc. ACM SIGMOD, 2009. Google Scholar
Digital Library
- C. Ronnie et al. SCOPE: Easy and Efficient Parallel Processing of Massive Data Sets. Proc. VLDB Endow., 1(2):1265--1276, 2008. Google ScholarDigital Library
- Apache Hadoop. Available at http://wiki.apache.org/hadoop.Google Scholar
- Hive Performance Benchmark. Available at https://issues.apache.org/jira/browse/HIVE-396.Google Scholar
- Hive Language Manual. Available at http://wiki.apache.org/hadoop/Hive/LanguageManual.Google Scholar
- Facebook Lexicon. Available at http://www.facebook.com/lexicon.Google Scholar
- Apache Pig. http://wiki.apache.org/pig.Google Scholar
- Apache Thrift. http://incubator.apache.org/thrift.Google Scholar
Index Terms
- Hive: a warehousing solution over a map-reduce framework
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