HadoopDB: an architectural hybrid of MapReduce and DBMS technologies for analytical workloads
Proceedings of the VLDB Endowment
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The growing need of processing massive amounts of data leads database researchers to explore the possibility of combining their existing single-computer database systems with the popular parallel processing platform Hadoop. These hybrid systems not only can keep the efficiency of database processing, but also achieve a remarkable scalability. This poster intends to propose such a system named Parallel Secondo. It combines Hadoop with a number of extensible Secondo database engines, in order to scale up the capability of processing extensible data models in Secondo to a cluster of computers. It is also evaluated with the join operation on standard, spatial and spatio-temporal data upon different sizes of clusters.