An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads.
HadoopDB is: 1. A hybrid of DBMS and MapReduce technologies that targets analytical workloads 2. Designed to run on a shared-nothing cluster of commodity machines, or in the cloud 3. An attempt to fill the gap in the market for a free and open source parallel DBMS 4. Much more scalable than currently available parallel database systems and DBMS/MapReduce hybrid systems. 5. As scalable as Hadoop, while achieving superior performance on structured data analysis workloadsDBMS Musings: Announcing release of HadoopDB (longer version)
my students Azza Abouzeid and Kamil Bajda-Pawlikowski developed HadoopDB. It's an open source stack that includes PostgreSQL, Hadoop, and Hive, along with some glue between PostgreSQL and Hadoop, a catalog, a data loader, and an interface that accepts queries in MapReduce or SQL and generates query plans that are processed partly in Hadoop and partly in different PostgreSQL instances spread across many nodes in a shared-nothing cluster of machines. In essence it is a hybrid of MapReduce and parallel DBMS technologies. But unlike Aster Data, Greenplum, Pig, and Hive, it is not a hybrid simply at the language/interface level. It is a hybrid at a deeper, systems implementation level. Also unlike Aster Data and Greenplum, it is free and open source.