Authors: Thomas Kejser, John Sirmon, and Denny Lee
Contributors:Bilal Obeidat, John Montgomery
Published: June 2011
Applies to: SQL Server 2005, SQL Server 2008, and SQL Server 2008 R2
Abstract: This white paper describes how operations engineers can test, monitor, capacity plan, and troubleshoot Microsoft SQL Server Analysis Services OLAP solutions in SQL Server 2005, SQL Server 2008, and SQL Server 2008 R2.
Executive Summary In this guide you will find information on how to test and run Microsoft SQL Server Analysis Services in SQL Server 2005, SQL Server 2008, and SQL Server 2008 R2 in a production environment. The focus of this guide is how you can test, monitor, diagnose, and remove production issues on even the largest scaled cubes. This paper also provides guidance on how to configure the server for best possible performance.
Analysis Services cubes are a very powerful tool in the hands of the business intelligence (BI) developer. They provide an easy way to expose even large data models directly to business users. Unlike traditional, static reporting, where the query workload is known in advance, cubes support ad-hoc queries. Typically, such queries are generated by the Microsoft Excel spreadsheet software – without the business user being aware of the intricacies of the query engine. Because cubes allow such great freedom for users, the power they give to developers comes with responsibility. It is in the interaction between development and operations that the business value of the cubes is realized and where the proper steps can be taken to ensure that the power of the cubes is used responsibly.
It is the goal of this guide to make your operations processes as painless as possible, and to have you run with the best possible performance without any additional development effort to your deployed cubes. In this guide, you will learn how to get the best out of your existing data model by making changes transparent to the data model and by making configuration changes that improve the user experience of the cube.
However, no amount of operational readiness can cure a poorly designed cube. Although this guide shows you where you can make changes transparent to end users, it is important to be aware that there are cases where design change is the only viable path to good performance and reliability. Cubes do not take away the ubiquitous need for informed data modeling. Fortunately, this operations guide has a companion volume targeted at developers: the Analysis Services Performance Guide. We highly recommend that your developers read that white paper and follow the guidance in it.
Cubes do not exist in isolation – they rely on relational data sources to build their data structures. Although a full treatment of good relational data warehouse modeling out of scope for this document, it still provides some pointers on how to tune the database sources feeding the cube. Relational engines vary in their functionality; this paper focuses on guidance for SQL Server data sources. Much of the information here should apply equally to other engines and your DBA should be able to transfer the guidance here to other database systems you run.
In every IT project, preproduction testing is a crucial part of the development and deployment cycle. Even with the most careful design, testing will still be able to shake out errors and avoid production issues. Designing and running a test run of an enterprise cube is time well invested. Hence, this guide includes a description of the test methods available to you.
To continue reading, please download the whitepaper SQL Server 2008 R2 Analysis Services Operations Guide
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