From: JTP PR on
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
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.