One of the foundational structures of a business intelligence (BI) solution is the data warehouse. To understand the most basic schema design for a data warehouse (i.e., the star schema), you must first understand the relationship between BI and data warehousing.
BI is a business management term that refers to the applications and technologies used to gather, provide access to, and analyze data and information about a company's operations. A data warehouse is a repository for a company's historical data. Data warehouses can be physical or virtual, and they can be structurally relational, quasi-relational, summarized, cubes, flat files, or a combination of styles. Data warehousing is the set of technologies and techniques that you use to build and manage the data warehouse.
Figure A illustrates the relationship between BI and data warehousing. The data warehouse gets its data from a variety of sources, including the extraction, transformation, and loading (ETL) staging database, the online transaction processing (OLTP) transactional database, or even directly from external data sources. Then, depending on the data needed for a BI project, you can spin off multiple OLAP cubes (also called multidimensional databases) from the data warehouse. (For example, a bank might analyze ATM transactions for behavior, time of day, or queue information, whereas a retail operation might perform a basket analysis on point of sale—POS—transactions.) On top of this underlying architecture, with the ability to tap into any or all of the data sources, is the BI software tools layer. This layer represents numerous BI packages that you can use to analyze data, generate reports, and find information for making business decisions. You can even feed information into automated activities and other processes for additional analysis.
