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Expert Data Modeling with Power BI

Expert Data Modeling with Power BI

By : Soheil Bakhshi
5 (8)
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Expert Data Modeling with Power BI

Expert Data Modeling with Power BI

5 (8)
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)
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1
Section 1: Data Modeling in Power BI
4
Section 2: Data Preparation in Query Editor
10
Section 3: Data Modeling
13
Section 4: Advanced Data Modeling

Optimizing the size of queries

In this section, we'll look at some other data preparation best practices that can make our model a better model. Optimizing queries' sizes can reduce the data refresh time. A model with an optimized size performs better after we import the data into the data model. In the following sub-sections, we'll look at some techniques that help us in having more optimized queries.

Removing unnecessary columns and rows

In real-world scenarios, we might deal with large tables with lots of rows and columns. Some Power BI developers tend to import all columns and rows from the source, ending up having poor-performing reports. Power BI uses the xVelocity engine, which uses in-memory data processing for data analytics that works based on column cardinality. Therefore fewer columns directly translate to less memory consumption and as a result, a more performant data model. In many real-world cases, we need to discuss with the business to make sure...

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