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In-memory analytics is an approach to querying data when it resides in a computer’s random access memory (RAM), as opposed to querying data that is stored on physical disks. This results in vastly shortened query response times, allowing business intelligence (BI) and analytic applications to support faster business decisions.
As the cost of RAM declines, in-memory analytics is becoming feasible for many businesses. BI and analytic applications have long supported caching data in RAM, but older 32-bit operating systems provided only 4 GB of addressable memory. Newer 64-bit operating systems, with up to 1 terabyte (TB) addressable memory (and perhaps more in the future), have made it possible to cache large volumes of data -- potentially an entire data warehouse or data mart -- in a computer’s RAM.
In addition to providing incredibly fast query response times, in-memory analytics can reduce or eliminate the need for data indexing and storing pre-aggregated data in OLAP cubes or aggregate tables. This reduces IT costs and allows faster implementation of BI and analytic applications. It is anticipated that as BI and analytic applications embrace in-memory analytics, traditional data warehouses may eventually be used only for data that is not queried frequently.