SQL Server Analysis Services (SSAS) provides online analytical processing (OLAP) and data mining functionality using a combination of client- and server-side components.
A data warehouse is a data repository used to overcome issues arising from performing strategic analysis on data in an online transaction processing (OLTP) database. An OLTP database supports the day-to-day business activity of the organization and is configured to let applications write data for a single transaction as quickly as possible. A data warehouse provides users easy access to information used to make strategic business decisions.
Dimension tables store information used to categorize and hierarchically organize the information stored in fact tables. The columns of a dimension table are called attributes . Attributes are used to hierarchically organize the rows of dimension tables in a way that is meaningful for business users.
OLAP is a combination of products and processes used to aggregate large amounts of heterogeneous data and interactively examine the results in a dimensional model. OLAP evolved from the need to interactively examine large volumes of data warehouse information.
Like a data warehouse, OLAP uses dimensional modeling to represent data. Unlike a data warehouse, which typically uses a relational database to store and access data, OLAP uses cubesmultidimensional data structures organized hierarchically along a business attribute for each dimension of the cube, with each cell containing one or more measures.
This chapter provides an overview of SSAS, the languages used with SSAS, programmatically querying data and metadata, and programmatically administering an SSAS instance and its objects. Because SSAS is a very large topic, the goal of this chapter is simply to provide an introduction to key elements and concepts. See Microsoft SQL Server 2005 Books Online for in-depth information about SSAS.