Book Image

SQL Server Analysis Services 2012 Cube Development Cookbook

Book Image

SQL Server Analysis Services 2012 Cube Development Cookbook

Overview of this book

Microsoft SQL Server is a relational database management system. As a database, it is a software product whose primary function is to store and retrieve data as requested by other software applications. SQL Server Analysis Services adds OLAP and data mining capabilities for SQL Server databases. OLAP (online analytical processing) is a technique for analyzing business data for effective business intelligence. This practical guide teaches you how to build business intelligence solutions using Microsoft’s core product – SQL Server Analysis Services. The book covers the traditional multi-dimensional model which has been around for over a decade as well as the tabular model introduced with SQL Server 2012. Starting with comparing MultiDimensional and tabular models – discussing the values and limitations of each, you will then cover the essential techniques for building dimensions and cubes. Following on from this, you will be introduced to more advanced topics, such as designing partitions and aggregations, implementing security, and synchronizing databases for solutions serving many users. The book also covers administrative material, such as database backups, server configuration options, and monitoring and tuning performance. We also provide a primer on MultiDimensional eXpressions (MDX) as well as Data Analysis expressions (DAX) languages. This book provides you with data cube development techniques, and also the ongoing monitoring and tuning for Analysis Services.
Table of Contents (19 chapters)
SQL Server Analysis Services 2012 Cube Development Cookbook
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Optimizing and managing a model's design


As the designer of a Tabular Model, your experience is affected by different factors than those of the consumers of the solution you deploy.

Managing memory usage

A common issue that users encounter with the Power Pivot add-in for Excel occurs when a large volume of data is imported that uses all the available memory resources on the local computer. This is especially true when running a 32-bit operating system or Office edition with restricted memory resources. The logical path for many of these solutions is to migrate the desktop Power Pivot model to a Tabular Model that runs on a well-equipped server.

The xVelocity In-Memory Analytics Engine is designed to utilize memory, rather than disk I/O, to provide improved performance when compared to more conventional technologies. Since models reside entirely in memory, the way we think about providing and managing resources must be fundamentally different than it would be for managing relational databases...