Yellowfin has released its latest Business Intelligence Whitepaper, this time on "In-memory Analytics"
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As the name suggests, the key difference between conventional BI tools and in-memory products is that the former query data on disk while the latter query data in random access memory (RAM).
When a user runs a query against a typical data warehouse, for example, the query normally goes to a database that reads the information from multiple tables stored on a server’s hard disk.
With a server-based in-memory database, all information is initially loaded into memory. Users then query and interact with the data loaded into the machine’s memory. Accessing data in-memory means it is literally “turbo charged” as opposed to accessing that same data from disk.
This is the real advantage of in-memory analysis.
In-memory BI may sound like caching, a common approach to speeding query performance, but in-memory databases do not suffer from the same limitations. Caches are typically subsets of data, stored on and retrieved from disk (though some may load into RAM)
The key difference is that the cached data is usually predefined and very specific, often to an individual query; but with an in-memory database, the data available for analysis is potentially as large as an entire data mart.