Book Image

Data Modeling with Microsoft Excel

By : Bernard Obeng Boateng
5 (1)
Book Image

Data Modeling with Microsoft Excel

5 (1)
By: Bernard Obeng Boateng

Overview of this book

Microsoft Excel's BI solutions have evolved, offering users more flexibility and control over analyzing data directly in Excel. Features like PivotTables, Data Model, Power Query, and Power Pivot empower Excel users to efficiently get, transform, model, aggregate, and visualize data. Data Modeling with Microsoft Excel offers a practical way to demystify the use and application of these tools using real-world examples and simple illustrations. This book will introduce you to the world of data modeling in Excel, as well as definitions and best practices in data structuring for both normalized and denormalized data. The next set of chapters will take you through the useful features of Data Model and Power Pivot, helping you get to grips with the types of schemas (snowflake and star) and create relationships within multiple tables. You’ll also understand how to create powerful and flexible measures using DAX and Cube functions. By the end of this book, you’ll be able to apply the acquired knowledge in real-world scenarios and build an interactive dashboard that will help you make important decisions.
Table of Contents (16 chapters)
1
Part 1: Overview and Introduction to Data Modeling in Microsoft Excel
6
Part 2: Creating Insightful Calculations from your Data Model using DAX and Cube Functions
9
Part 3: Putting it all together with a Dashboard

Understanding dimension and fact tables

In a relational database, a fact table is a table that stores quantitative information or facts about a business process or activity, such as sales, inventory, or customer transactions. A fact table typically contains numerical values and foreign keys to link to dimension tables.

On the other hand, a dimension table is a table that stores descriptive information about the objects, events, or entities in a business process or activity. Dimension tables are typically used to provide context and structure to the data in a fact table.

To understand this better, let’s consider an example of a retail business that sells products through its online store. In this example, we can identify the following fact and dimension tables.

Fact table – sales

The sales fact table would contain quantitative information about sales transactions, such as sales revenue, quantity sold, and price. The sales fact table would typically contain...