Book Image

Expert Data Modeling with Power BI

By : Soheil Bakhshi
Book Image

Expert Data Modeling with Power BI

By: Soheil Bakhshi

Overview of this book

This book is a comprehensive guide to understanding the ins and outs of data modeling and how to create data models using Power BI confidently. You'll learn how to connect data from multiple sources, understand data, define and manage relationships between data, and shape data models to gain deep and detailed insights about your organization. In this book, you'll explore how to use data modeling and navigation techniques to define relationships and create a data model before defining new metrics and performing custom calculations using modeling features. As you advance through the chapters, the book will demonstrate how to create full-fledged data models, enabling you to create efficient data models and simpler DAX code with new data modeling features. With the help of examples, you'll discover how you can solve business challenges by building optimal data models and changing your existing data models to meet evolving business requirements. Finally, you'll learn how to use some new and advanced modeling features to enhance your data models to carry out a wide variety of complex tasks. By the end of this Power BI book, you'll have gained the skills you need to structure data coming from multiple sources in different ways to create optimized data models that support reporting and data analytics.
Table of Contents (18 chapters)
Section 1: Data Modeling in Power BI
Section 2: Data Preparation in Query Editor
Section 3: Data Modeling
Section 4: Advanced Data Modeling

Extracting numbers from text

Another common data preparation step is when we need to extract a number from text values. An excellent example is when we want to extract a flat number or a zip code from an address. Other examples include extracting the numeric part of a sales order number or cleaning fullnames of typos, such as when some names contain numbers. In our scenario, we want to add two new columns to the Customer table, as follows:

  • Extract Flat Number as a new column from AddressLine1
  • Extract the rest of the address, Street Name, as a new column

The AddressLine1 column reveals that the flat number appears in different parts of the address; therefore, splitting by transitioning from digit to non-digit would not work:

Figure 5.43 – Flat Number appears in different places in AddressLine1

To achieve our goal, we need to extract the numbers from text. To do so, we can use the Text.Select(Text as nullable text, SelectChars as any...