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== Definition ==
With businesses demanding faster and easy access to information in order to make reliable and smart decisions, In-memory processing is an emerging technology that is gaining attention.<ref>{{cite journal|author=Hao Zhang|author2=Gang Chen|author3=Beng Chin Ooi|author4=Kian-Lee Tan|author5=Meihui Zhang|title=In-Memory Big Data Management and Processing: A Survey|journal=IEEE Transactions on Knowledge and Data Engineering|date=July 2015|volume=27|issue=7|pages=1920–1948|url=http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7097722}}</ref> It enables users to have immediate access to right information which results in more informed decisions. Traditional [[Business Intelligence]] (BI) technology loads data onto the disk in the form of tables and multi-dimensional cubes against which queries are run. Using In-memory data is loaded into memory ([[Random Access Memory]] (RAM) or [[flash memory]]) instead of hard disks and therefore information technology (IT) staff spends less development time on [[data modeling]], query analysis, cube building and table design.<ref>{{cite book|last=Earls|first=A|title=Tips on evaluating, deploying and managing in-memory analytics tools|year=2011|publisher=Tableau|url=http://www.analyticsearches.com/site/files/776/66977/259607/579091/In-Memory_Analytics_11.10.11.pdf}}{{dead link|date=September 2015}}</ref>
== Traditional BI ==
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To avoid performance issues and provide faster query processing when dealing with large volumes of data, organizations needed optimized database methods like creating [[index (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|
== How does In-memory processing Work? ==
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== 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, memory 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}}{{dead link|date=September 2015}}</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.
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With In-memory processing the source database is queried only once instead of accessing the database every time a query is run thereby eliminating repetitive processing and reducing the burden on database servers. By scheduling to populate In-memory database overnight the database servers can be used for operational purposes during peak hours.
In-memory processing can be a blessing in disguise for operational workers such as call center representatives or warehouse managers who need instant and accurate data to make fast decisions.<ref>{{cite web|title=In_memory Analytics|url=http://www.yellowfinbi.com/Document.i4?DocumentId=104879|publisher=yellowfin|
== Disadvantages of In-memory BI ==
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While In-memory processing has a great potential for end users it is not the answer to everyone. Important question organizations need to ask is if slower query response times are preventing users from making important decisions. If company is a slow moving business where things don’t change often then in-memory solution is not effective. Organizations where there is a significant growth in data volume and increase in demand for reporting functionalities that facilitate new opportunities would be a right scenario to deploy in-memory BI.
Security needs to be the first and foremost concern when deploying In-memory tools as they expose huge amounts of data to end users. Care should be taken as to who has access to the data, how and where data is stored. End users download huge amounts of data onto their desktops and there is danger of data getting compromised. It could get lost or stolen. Measures should be taken to provide access to the data only to authorized users.<ref>{{cite web|title=In_memory Analytics|url=http://www.yellowfinbi.com/Document.i4?DocumentId=104879|publisher=yellowfin|
== References ==
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