In-memory processing: Difference between revisions

Revision as of 19:57, 15 December 2011

Revision as of 20:03, 8 January 2012

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== Disadvantages of traditional BI ==

To avoid performance issues and provide faster query processing when dealing with large volumes of data, organizations needed optimized database methods like creating [[indexesindex (database)|index]]es, use specialized data structures and aggregate tables.

The point of having a data warehouse is to be able to get results for any queries asked at any time. But in order to achieve better response time for users many data marts are designed to pre calculate summaries and answer specific queries defeating the purpose of a data warehouse. Optimized aggregation algorithms needed to be used to increase performance.

Traditional BI tools couldn’t keep up with the ever growing BI requirements and were unable to deliver real time data for end users.<ref>{{cite web|title=In_memory Analytics|url=http://www.yellowfinbi.com/Document.i4?DocumentId=104879|publisher=yellowfin|pages=6}}</ref>

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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.

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.

Reduced Costs: In-memory processing comes at a lower cost and can be easily deployed and maintained when compared to traditional BI tools. According to Gartner survey deploying traditional BI tools can take as long as 17 months. Many data warehouse vendors are choosing In-memory technology over traditional BI to speed up implementation times.