A comparison of approaches to large-scale data analysis
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Benchmarking cloud serving systems with YCSB
Proceedings of the 1st ACM symposium on Cloud computing
Column-oriented storage techniques for MapReduce
Proceedings of the VLDB Endowment
CoHadoop: flexible data placement and its exploitation in Hadoop
Proceedings of the VLDB Endowment
RCFile: A fast and space-efficient data placement structure in MapReduce-based warehouse systems
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Clydesdale: structured data processing on MapReduce
Proceedings of the 15th International Conference on Extending Database Technology
A performance comparison of parallel DBMSs and MapReduce on large-scale text analytics
Proceedings of the 16th International Conference on Extending Database Technology
Performance evaluation of a MongoDB and hadoop platform for scientific data analysis
Proceedings of the 4th ACM workshop on Scientific cloud computing
A comparison of two physical data designs for interactive social networking actions
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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In this new era of "big data", traditional DBMSs are under attack from two sides. At one end of the spectrum, the use of document store NoSQL systems (e.g. MongoDB) threatens to move modern Web 2.0 applications away from traditional RDBMSs. At the other end of the spectrum, big data DSS analytics that used to be the domain of parallel RDBMSs is now under attack by another class of NoSQL data analytics systems, such as Hive on Hadoop. So, are the traditional RDBMSs, aka "big elephants", doomed as they are challenged from both ends of this "big data" spectrum? In this paper, we compare one representative NoSQL system from each end of this spectrum with SQL Server, and analyze the performance and scalability aspects of each of these approaches (NoSQL vs. SQL) on two workloads (decision support analysis and interactive data-serving) that represent the two ends of the application spectrum. We present insights from this evaluation and speculate on potential trends for the future.