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In almost every small organization I’ve been with, and,
surprisingly, in many medium-size organizations as well, there is no online
analytical processing (OLAP) database. This may be in part because of a lack of
human resources.

This article is primarily for developers who wear many hats,
and who haven’t split their database into two: online transaction processing (OLTP) and OLAP. I’ll introduce the notion of an
n-dimensional cube as a way of getting you to think about what your data might
look like in an OLAP solution.

Consider your OLTP database and its most active tables
(e.g., Sales, SalesDetails, Payments).
Then, think about the foreign keys in each of these tables (e.g., SalesOfficeID, SalespersonID, ProductID). Each of these
constitutes a dimension of your data.
More complex dimensions exist (in particular, dates) yet they may not relate to
foreign keys. Besides the division into year, month, and day, there are oddball
divisions such as week, quarter, and even fiscal quarter. Sometimes the group
of interest might be a double-foreign key (i.e., ProductGroupID,
which resides in the Products table not the SalesDetails

Look at a column’s values for repeats of interest. From this
perspective, a column such as SalesID is of no
interest since there are no repeats, whereas StoreID
and ProductID are of interest since there will be
many repeats.

Relational databases are by definition two-dimensional—rows
and columns. OLAP databases introduce additional dimensions, which you can
think of as perspectives or vantage points.

In Analysis Services 2000, everything revolves around a fact table, to which various dimension tables are related. (SQL
Server 2005, formerly code-named Yukon, supports multiple fact tables, which is
a significant improvement.) Analysis Services pre-computes the aggregates of
interest for all the dimensions you define, providing virtually instantaneous
retrievals for said aggregates–no matter how many dimensions you specify or
how deep you want to drill down.

An effective use of Analysis Services depends on your
understanding of dimensions and your appreciation of their use in combinations.
You might create an n-dimensional cube whose dimensions include StoreID, ProductID, ProductGroupID, DateSold, and SalesPersonID. Once you describe your cube clearly, SQL
Server will take a while to calculate all the values for all the points your
dimensions describe (i.e., Total Sales for Q1 2004 by store, product, group,
and salesperson). However, when managers want to know what the big picture is,
you can tell them instantaneously.

The dimensions of interest will vary among organizations,
but the concepts are the same across all organizations. Once you have a clear
grasp of what dimensions are, you can deliver invaluable information to
managers in a form that they can understand and appreciate.