Abstract
Most SQL programmers work with Online Transaction Processing (OLTP) databases and have had no exposure to Online Analytic Processing (OLAP) and data warehousing. OLAP is concerned with summarizing and reporting data; therefore, the schema designs and common operations are very different from the usual Structured Query Language (SQL) queries. The Star Schema is a violation of basic normalization rules. There is a large central fact table. This table contains all the facts about an event to report on, such as sales, in one place. While OLAP systems have the ability to answer “who” and “what” questions, it is their ability to answer “what if” that sets them apart from other business intelligence (BI) tools. The traditional data-warehouse architecture includes an atomic layer of granular data, often normalized, that serves as the only source of data for subsequent subject-specific data marts. Generally, the data marts are implemented as Star Schemas, proprietary MOLAP cubes, or both.
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