Introduction to MSSQL Server Analysis Services: Point-and-Click Cube Schema Simplification

About the Series …

This
article is a member of the series Introduction to MSSQL Server 2000 Analysis
Services
. The series is designed to provide hands-on application of
the fundamentals of MS SQL Server 2000 Analysis Services, with each
installment progressively adding features and techniques designed to meet
specific real – world needs. For more information on the series, as well as
the hardware / software requirements to prepare for the exercises we
will undertake, please see my initial article, Creating Our First Cube.

Note: Service Pack 3 updates are assumed for MSSQL Server 2000, MSSQL
Server 2000 Analysis Services
, and the related Books Online
and Samples. Images are from a Windows 2003 Server
environment, upon which I have also implemented MS Office 2003, but the
steps performed in the articles, together with the views that result, will be
quite similar within any environment that supports MSSQL Server 2000 and MSSQL Server 2000 Analysis Services ("Analysis
Services"
or "MSAS"). The same is generally true,
except where differences are specifically noted, when MS Office 2000 and
above are used in the environment, in cases where MS Office components
are presented in the article.

Introduction

In this article, we will explore another tool that MSAS
offers for the enhancement of cube processing, the Optimize Schema
option. Optimize Schema attempts to identify unnecessary joins between
our fact and dimension tables, and then to remove them. In many cases, the tool
works effectively to accomplish this, leading to a significant reduction in a
cube’s processing time. Elimination of the joins means more rapid resolution
of MSAS’ queries to the relational database, upon which our cube is dependent
as a data source. This, in turn, means that data is pulled into Analysis
Services in less time, contributing to a more rapid cube build overall.

The
operation of the Optimize Schema option takes advantage of a common
circumstance within the construction of many star or snowflake schemas: the foreign
key
that serves as the basis of a join between the fact table and a given
dimension table is identical to the member key itself. When this is the
case, MSAS can eliminate the join, and source the member key directly from the
fact table, instead of relying upon a join to the dimension table to obtain the
key.

In this article, we will examine the use of Optimize
Schema
in making our cubes process faster. To accomplish this objective, we
will:

  • Overview the Optimize
    Schema
    option;

  • Create a copy
    of the Warehouse sample cube for use in our practice session;

  • Prepare the
    cube copy further by processing;

  • Discuss the Member
    Key Column
    property, and examine existing settings within our practice cube;

  • Discuss
    possible considerations in determining the appropriateness of the use of the Optimize
    Schema
    option in our respective business environments;

  • Perform a
    practice exercise within which we employ the Optimize Schema option;

  • Examine some
    of the effects of using Optimize Schema.
William Pearson
William Pearson
Bill has been working with computers since before becoming a "big eight" CPA, after which he carried his growing information systems knowledge into management accounting, internal auditing, and various capacities of controllership. Bill entered the world of databases and financial systems when he became a consultant for CODA-Financials, a U.K. - based software company that hired only CPA's as application consultants to implement and maintain its integrated financial database - one of the most conceptually powerful, even in his current assessment, to have emerged. At CODA Bill deployed financial databases and business intelligence systems for many global clients. Working with SQL Server, Oracle, Sybase and Informix, and focusing on MSSQL Server, Bill created Island Technologies Inc. in 1997, and has developed a large and diverse customer base over the years since. Bill's background as a CPA, Internal Auditor and Management Accountant enable him to provide value to clients as a liaison between Accounting / Finance and Information Services. Moreover, as a Certified Information Technology Professional (CITP) - a Certified Public Accountant recognized for his or her unique ability to provide business insight by leveraging knowledge of information relationships and supporting technologies - Bill offers his clients the CPA's perspective and ability to understand the complicated business implications and risks associated with technology. From this perspective, he helps them to effectively manage information while ensuring the data's reliability, security, accessibility and relevance. Bill has implemented enterprise business intelligence systems over the years for many Fortune 500 companies, focusing his practice (since the advent of MSSQL Server 2000) upon the integrated Microsoft business intelligence solution. He leverages his years of experience with other enterprise OLAP and reporting applications (Cognos, Business Objects, Crystal, and others) in regular conversions of these once-dominant applications to the Microsoft BI stack. Bill believes it is easier to teach technical skills to people with non-technical training than vice-versa, and he constantly seeks ways to graft new technology into the Accounting and Finance arenas. Bill was awarded Microsoft SQL Server MVP in 2009. Hobbies include advanced literature studies and occasional lectures, with recent concentration upon the works of William Faulkner, Henry James, Marcel Proust, James Joyce, Honoré de Balzac, and Charles Dickens. Other long-time interests have included the exploration of generative music sourced from database architecture.

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