Sequential Data Access With Oracle And Hadoop: A Performance Comparison

The Hadoop framework has proven to be an effective and popular approach for dealing with "Big Data" and, thanks to its scaling ability and optimised storage access, Hadoop Distributed File System-based projects such as Map Reduce or HBase are seen as candidates to replace traditional relational database management systems whenever scalable speed of data processing is a priority. But do these projects deliver in practice? Does migrating to Hadoop's "shared nothing" architecture really improve data access throughput? And, if so, at what cost? Authors answer these questions addressing cost/performance as well as raw performance based on a performance comparison between an Oracle-based relational database and Hadoop's distributed solutions like Map Reduce or HBase for sequential data access. A key feature of our approach is the use of an unbiased data model as certain data models can significantly favour one of the technologies tested.