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

Overcoming BI challenges and barriers


Self-Service BI is a good, and still improving answer for bridging the Business Intelligence technology and business gap. More than just tools and technology, Self-Service BI involves a commitment to cooperation and continuous—organic—improvement. With the right tools and cooperation between IT and business, it's now possible to provide long-term and high-quality managed data while also giving businesses the capability to meet their information needs in their needed time frame.

The Self-Service tools, such as Power Pivot, Power View, and the Analysis Services Tabular Model introduced with the SQL Server 2012, allow business resources to acquire, analyze, and share information relatively independent of IT and with a relatively low requirement for technical skill—the emphasis is on "relatively". It is possible for a business person to acquire data from a variety of resources through the use of tools provided by wizards and graphical interfaces. However, there remains the need for a higher than average technical capability—not a developer level but an analyst level resource is the typical profile. Also, though there is no requirement to involve IT or the managed data environment, these resources remain a source of considerable capability and information, and Self-Service users should look to them first to check if their needs may be met.

Traditional managed data and emerging Self-Service BI are, therefore, not competitive nor alternative technologies but rather complimentary technologies that together are a comprehensive, robust, and nimble information environment. Self-Service BI is the pointed end of the spear in which analysts self-serving information are in direct contact with the business and are tasked with responding quickly to information requests. As such, these analysts are the first to be aware of emerging and recurring questions and the information needs that answer those questions. By regularly harvesting this knowledge, those in charge of maintaining the managed data environment have a clear direction as to how their environment should evolve. Incorporating the newly identified, and vetted by Self-Service, sources and business rules for analysis into the data warehouse continuously improves the quality and depth of the still very valuable managed data environment.