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This is the first of a series of posts about business intelligence tools, particularly OLAP (or online analytical processing) tools using MySQL and other free open source software. OLAP tools are a part of the larger topic of business intelligence, a topic that has not had a lot of coverage on MPB. Because of this, I am going to start out talking about these topics in general, rather than getting right to gritty details of their performance. I plan on covering the following topics: • Introduction to and business intelligence. (this post) • Identifying the differences between a data warehouse, and a data mart. • Introduction to queries and the kind of SQL which a ROLAP tool must generate to answer those queries. • Performance challenges with larger databases, and some ways to help performance using aggregation.

• Using materialized views to automate that aggregation process. • Comparing the performance of OLAP with and without aggregation over multiple MySQL storage engines at various data scales. Chances are that you have heard the term business intelligence. Business intelligence (or BI) is a term which encompasses many different tools and methods for analyzing data, usually presenting it in a way that is easily consumed by upper management. This analysis is often used to determine how effectively the business has been at meeting certain performance goals, and to forecast how they will do in the future. To put it another way the tools are designed to provide insight about the business process, hence the name.

Probably the most popular BI activity for web sites is click analysis. As far as BI is concerned, this series of posts focuses on OLAP analysis and in a lesser sense, on data warehousing. Data warehouses often provide the information upon which OLAP analysis is performed, but more on this in post #2. What is that?