HadoopDB: An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads

HadoopDB: An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads

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by Azza Abouzeid , Kamil Bajda-pawlikowski , Daniel Abadi , Avi Silberschatz , Er Rasin

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Datum	Value	Source
TITLE	HadoopDB: An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads	SVM HeaderParse 0.2
AUTHOR NAME	Azza Abouzeid	SVM HeaderParse 0.2
AUTHOR AFFIL	1	SVM HeaderParse 0.2
AUTHOR NAME	Kamil Bajda-pawlikowski	SVM HeaderParse 0.2
AUTHOR AFFIL	1	SVM HeaderParse 0.2
AUTHOR NAME	Daniel Abadi	SVM HeaderParse 0.2
AUTHOR AFFIL	1	SVM HeaderParse 0.2
AUTHOR NAME	Avi Silberschatz	SVM HeaderParse 0.2
AUTHOR AFFIL	1	SVM HeaderParse 0.2
AUTHOR NAME	Er Rasin	SVM HeaderParse 0.2
AUTHOR AFFIL	1	SVM HeaderParse 0.2
ABSTRACT	The production environment for analytical data management applications is rapidly changing. Many enterprises are shifting away from deploying their analytical databases on high-end proprietary machines, and moving towards cheaper, lower-end, commodity hardware, typically arranged in a shared-nothing MPP architecture, often in a virtualized environment inside public or private “clouds”. At the same time, the amount of data that needs to be analyzed is exploding, requiring hundreds to thousands of machines to work in parallel to perform the analysis. There tend to be two schools of thought regarding what technology to use for data analysis in such an environment. Proponents of parallel databases argue that the strong emphasis on performance and efficiency of parallel databases makes them wellsuited to perform such analysis. On the other hand, others argue that MapReduce-based systems are better suited due to their superior scalability, fault tolerance, and flexibility to handle unstructured data. In this paper, we explore the feasibility of building a hybrid system that takes the best features from both technologies; the prototype we built approaches parallel databases in performance and efficiency, yet still yields the scalability, fault tolerance, and flexibility of MapReduce-based systems. 1.	SVM HeaderParse 0.2
CITATIONS	20 found	ParsCit 1.0