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Employing SQL and Dimensional Modeling To Optimize Datasets for BI tools

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  Simply put, dimensional modeling is all about separating your data between entities and events pertaining to those entities. Three fundamental concepts are dimension tables, fact tables, and measures. Fact tables: a stack of data that characterizes business events (a fact). It bears connections to measures and dimensions to pull out additional details. Measures: numeric characteristics of the fact table. It may perhaps be calculations, or simply values pertaining to an event. Furthermore, helps with questions that can be answered via numbers for example: How much? At which rate? Dimension tables: entities that reply to clear-cut business questions about the fact table: Who? What? Where? When? Why? How can dimensional modeling help us to reduce datasets? Easy! Imagine if you needed to get all of this data from a single source. It will be a large table, right? With SQL server dimensional modeling you are compressing the amount of repeated information. As a result, you ar