Abstract
In recent years MapReduce has risen to be the de-facto tool for big data processing. MapReduce is a disruptive innovation. It has changed the landscape of database market, the landscape of technologies, as well as the landscape of saying power. The article will give a reflection on the popularity of the technique and some observations of its position in a unified big data platform.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. In: Symposium on Operating Systems Design and Implementation (OSDI), pp. 137–150. USENIX Association, San Francisco (2004)
Lee, K.H., Lee, Y.J., Choi, H., Chung, Y.D., Moon, B.: Parallel data processing with MapReduce: a survey. SIGMOD Record 40(4), 11–20 (2011)
Sakr, S., Liu, A., Fayoumi, A.G.: The Family of MapReduce and Large Scale Data Processing Systems (2013), http://arxiv.org/abs/1302.2966
Foley, M.J.: Microsoft drops Dryad; puts its big-data bets on Hadoop (2011), http://www.zdnet.com/blog/microsoft/microsoft-drops-dryad-puts-its-big-data-bets-on-hadoop/11226
Kraska, T.: Finding the Needle in the Big Data Systems Haystack. IEEE Internet Computing 17(1), 84–86 (2013)
Winckler, M.: Apache Hadoop takes top prize at Media Guardian Innovation Awards (2011), http://www.guardian.co.uk/technology/2011/mar/25/media-guardian-innovation-awards-apache-hadoop
Melnik, S., Gubarev, A., Long, J.J., Romer, G., Shivakumar, S., Tolton, M., Vassilakis, T.: Dremel: Interactive Analysis of WebScale Datasets. Proceedings of the VLDB Endowment 3(1-2), 330–339 (2010)
He, Y., Lee, R., Huai, Y., Shao, Z., Jain, N., Zhang, X., Xu, Z.: RCFile: A Fast and Space-efficient Data Placement Structure in MapReduce-based Warehouse Systems. In: International Conference on Data Engineering (ICDE), pp. 1199–1208. IEEE Computer Society, Hannover (2011)
Ferguson, M.: Architecting a Big Data Platform for Analytics. A Whitepaper Prepared for IBM (2012)
Oracle: Oracle: Big Data for the Enterprise. Oracle White Paper (2012)
Dewitt, D.: Polybase: What, Why, How. SQL PASS Summit Keynote (2012)
EMC: Unified Analytics Platform (2013), http://www.greenplum.com/products/greenplum-uap
TeraData: TeraData Unified Data Architecture. TeraData Whitepaper (2012)
Friedman, E., Pawlowski, P., Cieslewicz, J.: SQL/MapReduce: A practical approach to self describing, polymorphic, and parallelizable user defined functions. Proceedings of the VLDB Endowment 2(2), 1402–1413 (2009)
Gates, A.: The Stinger Initiative: Making Apache Hive 100 Times Faster (2013), http://hortonworks.com/blog/100x-faster-hive/
Incubator Wiki: Drill Proposal (2013), http://wiki.apache.org/incubator/DrillProposal
Abouzeid, A., Bajda-Pawlikowski, K., Abadi, D., Silberschatz, A., Rasin, A.: HadoopDB: an architectural hybrid of MapReduce and DBMS technologies for analytical workloads. Proceedings of the VLDB Endowment 2(1), 922–933 (2009)
Platfora: Platfora Homepage (2013), http://www.platfora.com/
Murthy, A.C.: The Next Generation of Apache Hadoop MapReduce (2011), http://developer.yahoo.com/blogs/hadoop/posts/2011/02/mapreduce-nextgen/
BUSINESS WIRE: HortonWorks to Deliver Next-Generation of Apache Hadoop (2012), http://www.businesswire.com/news/home/20120119005825/en/Hortonworks-Deliver-Next-Generation-Apache-Hadoop
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Qin, X., Qin, B., Du, X., Wang, S. (2013). Reflection on the Popularity of MapReduce and Observation of Its Position in a Unified Big Data Platform. In: Gao, Y., et al. Web-Age Information Management. WAIM 2013. Lecture Notes in Computer Science, vol 7901. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39527-7_33
Download citation
DOI: https://doi.org/10.1007/978-3-642-39527-7_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39526-0
Online ISBN: 978-3-642-39527-7
eBook Packages: Computer ScienceComputer Science (R0)