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Shark: Fast Data Analysis Using Coarse-grained Distributed Memory

Summary: Shark is a data-analysis system built on a coarse-grained distributed shared-memory abstraction, unifying SQL querying with near-data analytics. Scales to thousands of fault-tolerant nodes; delivers 40x faster queries vs Hive and 25x faster ML vs MapReduce on large datasets. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4645
Venue
SIGMOD
Year
2012
Pagerank
8.9565862e-05
Overall Rank
2,207 | 84.86%
DOI
10.1145/2213836.2213934

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{engle_sigmod12,
        title = {{Shark: Fast Data Analysis Using Coarse-grained Distributed Memory}},
        author = {Engle, Cliff and Lupher, Antonio and Xin, Reynold and Zaharia, Matei and Franklin, Michael J. and Shenker, Scott and Stoica, Ion},
        series = {{SIGMOD} '12},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2213836.2213934},
        url = {https://dl.acm.org/doi/10.1145/2213836.2213934},
        year = {2012}
}

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
44 A Comparison of Approaches to Large-Scale Data Analysis 2009 SIGMOD 0.00046055057
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