Minimizing the MOLAP/ROLAP Divide: You Can Have Your Performance and Scale It Too

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Provided by: Korean Institute of Information Scientists and Engineers
Topic: Big Data
Format: PDF
Over the past generation, data warehousing and On-Line Analytical Processing (OLAP) applications have become the cornerstone of contemporary decision support environments. Typically, OLAP servers are implemented on top of either proprietary array-based storage engines (MOLAP) or as extensions to conventional Relational DBMSs (RDBMS). While MOLAP systems do indeed provide impressive performance on common analytics queries, they tend to have limited scalability. Conversely, ROLAP's table oriented model scales quite nicely, but offers mediocre performance at best relative to the MOLAP systems.
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