In-memory processing: Difference between revisions

Revision as of 16:47, 1 June 2013

Revision as of 16:52, 1 June 2013

Line 23:

== Factors driving In-memory products ==

Cheaper and higher performing hardware: According to [[Moore’s law]] the computing power doubles every two to three years while decreasing in costs. CPU processing, RAMmemory and disk storage are all subject to some variation of this law. Also hardware innovations like multi-core architecture, NAND flash memory, parallel servers, increased memory processing capability, etc. and software innovations like column centric databases, compression techniques and handling aggregate tables, etc. have all contributed to the demand of In-memory products.<ref>{{cite web|last=Kote|first=Sparjan|title=In-memory computing in Business Intelligence|url=http://www.infosysblogs.com/oracle/2011/03/in-memory_computing_in_busines.html}}</ref>

64-bits operating system: Though the idea of In-memory technology is not new, it is only recently emerging thanks to the widely popular and affordable 64-bit processors and declining memory chips prices. [[64 bit]] operating systems allows access to far more RAM (up to 100GB or more) than the 2 or 4 GB accessible on 32-bit systems. By providing Terabytes (1 TB = 1,024 GB) of space available for storage and analysis, 64-bit operating systems make in-memory processing scalable. The use of flash memory enables systems to scale to many Terabytes more economically.

Data Volumes: As the data used by organizations grew traditional data warehouses just couldn’t deliver a timely, accurate and real time data. The extract, transform, load ([[Extract, transform, load|ETL]]) process that periodically updates data warehouses with operational data can take anywhere from a few hours to weeks to complete. So at any given point of time data is at least a day old. In-memory processing makes easy to have instant access to terabytes of data for real time reporting.