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Data Modeling with Microsoft Excel

Data Modeling with Microsoft Excel

By : Bernard Obeng Boateng
4.6 (8)
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Data Modeling with Microsoft Excel

Data Modeling with Microsoft Excel

4.6 (8)
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. Note: To access the supplemental material, subscribers should purchase a print copy of the book. The ebook can be accessed through the QR code or link provided inside the Print book. Proof of purchase is mandatory to access the ebook.
Table of Contents (16 chapters)
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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 the role of primary keys and foreign keys

Keys are very important in a relational database because they help create a relationship between two tables and ensure that each record in a table can be identified in a unique way. When we connect the related tables, the version of the connecting columns in the dimension tables is called the primary key while the version in the fact table is called the foreign key.

In a relational database, the primary key helps us identify a single row in a table. All primary keys must be unique in the columns where they sit. Foreign keys help connect our dimension and fact tables and, more importantly, make our fact tables accessible in our calculations. Foreign keys are typically not unique.

Going back to our mock view of our relational database, we can add primary keys and foreign keys, as shown here:

Figure 2.8: Assigning primary keys and foreign keys

Figure 2.8: Assigning primary keys and foreign keys

In our data model, these primary keys will be linked...

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