In-Memory Performance is More than Just a Database Issue

A key component of a successful business intelligence (BI) implementation—defined as one that
promotes widespread adoption and continued usage—is creating a positive experience for a user audience
characterized by a wide variety of needs and spectrum of technical abilities. Regardless of the user’s
background, high performance—whether referring to the ability to analyze large data sets or provide
rapid responses to user requests or actions—is an important component of this positive user experience.

As a result, the concept of “in-memory” databases, or “caching,” recently has grown in popularity as BI
vendors tout the storage of data or metadata as the mechanism driving fast access and analysis. With
regards to BI, the concept refers to storing data or metadata in memory (i.e. RAM), rather than physically
on a disk, which enables users to more easily and readily access the data—or any manipulation of that
data—on-demand.

While this concept may not be a new one, its popularity continues to grow as the cost of hardware
memory decreases and CPU processing speed expands. Combine that scenario with the vast amounts of
data generated in businesses every day and the desire to find more valuable insight within that data, and
suddenly in-memory becomes a very appealing approach for many companies.

Yet caching of data in memory is just one of the many components that can help deliver high performance
in BI. If the rush to in-memory caching results in the neglect of other key areas of the solution, it’s likely
the gains achieved by such an approach will be offset and likely outweighed by the losses, resulting in
waning adoption and usage.

To address this concern, SwiftKnowledge advocates a multi-faceted, hybrid approach to high performance,
one that optimizes performance for end users throughout their entire experience in the BI solution.
We believe this approach provides specific advantages in the areas of connection pooling, session
state, query creation and data record set loading over a standard, database-centric, in-memory approach.

This tech note explains SwiftKnowledge’s approach to in-memory analytics—an approach powered by our
patented Interactive Data Streaming™ (IDS) technology—and the benefits it helps organizations realize.

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