Introducing Big Data Technologies - Data Warehousing in the Age of Big Data - page 99

real-life implementations of solutions from early adopters of these technologies to solve the large-
scale data processing. In Part 2 of this topic, we see how these technologies will enrich the data ware-
house and data management with Big Data integration.
Further reading
Blanas, S., Patel, J.M., Ercegovac, V., Rao, J., Shekita, E.J., & Tian, Y. A Comparison of Join Algorithms for Log
Processing in MapReduce.
Dean, J., & Ghemawat, S. (2010). MapReduce: a data processing tool. Communications of the ACM , 53 (1),
72-77.
Facebook Data Infrastructure Team. (2009). Hive: A Data Warehousing Solution over a MapReduce Framework.
Journal Proceedings of the VLDB Endowment , 2 (2), 1626-1629.
Friedman, E., Pawlowski, P., & Cieslewicz, J. (2009). SQL/MapReduce: a practical approach to self-describ-
ing, polymorphic, and parallelizable user-deined functions. Proceedings of the VLDB Endowment , 2 (2),
1402-1413.
Ghemawat, S., Gobio, H., & Leung, S. (Oct. 2003). The google ile system. SIGOPS Operating Systems Review ,
37 , 29-43.
Gilbert, S., & Lynch, N. A. (2012). Perspectives on the CAP Theorem. IEEE Computer , 30-36.
Lakshman, A., & Malik, P. (2010). Cassandra: A Decentralized Structured Storage System. ACM SIGOPS
Operating Systems Review, vol. 44, pp. 35-40. SIGOPS ACM Special Interest Group on Operating Systems.
NY, USA: ACM. ISSN: 0163-5980, doi: 10.1145/1773912.1773922
Lamport, L. (1978). Time, Clocks, and the Ordering of Events in a Distributed System. Communications of the
ACM , 21 (7), 558-565. Reprinted in several collections, including Distributed Computing: Concepts and
Implementations, McEntire et al., eds. IEEE Press, 1984.
O'Malley, O., & Murthy, A. C. (2009). Hadoop Sort Benchmarks. < http://sortbenchmark.org/Yahoo2009.pdf >
Pavlo, A., Paulson, E., Rasin, A., Abadi, D. J., Dewitt, D. J., Madden, S., et al. (2009). A comparison of
approaches to large-scale data analysis. In SIGMOD '09: Proceedings of the 35th SIGMOD international
conference on Management of data.
Pavlo, A., Paulson, E., Rasin, A., Abadi, D. J., Dewitt, D. J., Madden, S., et al. A comparison of approaches
to large-scale data analysis. Brown University. Retrieved Jan. 1, 2010, from communications of the ACM
January 2010 vol. 53 no. 1.
Ranger, C., Raghuraman, R., Penmetsa, A., Bradski, G., Kozyrakis, C., Evaluating MapReduce for Multi-
core and Multiprocessor Systems. High Performance Computer Architecture, 2007. HPCA 2007. IEEE
13th International Symposium on , pp.13,24, 10-14 Feb. 2007. doi: 10.1109/HPCA.2007.346181 . ( http://
ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4147644&isnumber=4147636 ).
Ronnie, C., Bob, J., Per-Åke, L., Bill, R., Darren, S., Simon, W., & Jingren, Z. (2008). SCOPE: easy and eficient
parallel processing of massive data sets. Proceedings of the VLDB Endowement , 1 (2), 1265-1276.
Thusoo, A., Sarma, J. S., Jain, N., Shao, Z., Chakka, P., & Anthony, S., et al. (2009). Hive: a warehousing solu-
tion over a MapReduce framework. Proceedings of the VLDB Endowment , 2 (2), 1626-1629.