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Expert Data Modeling with Power BI - Second Edition

You're reading from  Expert Data Modeling with Power BI - Second Edition

Product type Book
Published in Apr 2023
Publisher Packt
ISBN-13 9781803246246
Pages 698 pages
Edition 2nd Edition
Languages
Author (1):
Soheil Bakhshi Soheil Bakhshi
Profile icon Soheil Bakhshi

Table of Contents (22) Chapters

Preface Section I: Data Modeling in Power BI
Introduction to Data Modeling in Power BI Data Analysis eXpressions and Data Modeling Section II: Data Preparation in Query Editor
Data Preparation in Power Query Editor Getting Data from Various Sources Common Data Preparation Steps Star Schema Preparation in Power Query Editor Data Preparation Common Best Practices Section III: Data Modeling
Data Modeling Components Star Schema and Data Modeling Common Best Practices Section IV: Advanced Data Modeling
Advanced Data Modeling Techniques Row-Level and Object-Level Security Dealing with More Advanced Data Warehousing Concepts in Power BI Introduction to Dataflows DirectQuery Connections to Power BI Datasets and Analysis Services in Composite Models New Options, Features, and DAX Functions Other Books You May Enjoy
Index

Use query parameters

As we discussed in Chapter 3, Data Preparation in Power Query Editor, in the Understanding query parameters section, query parameters are one of the most valuable features of Power Query that can help us in many scenarios. The following sections explain some of them.

Parameterizing connections

It is best to parameterize all data source connections, especially if we have different environments. The parameterized connections become handy when configuring the deployment pipelines.

Restricting the row counts in development for large tables

Sometimes, we deal with large tables with several million rows, which can be time-consuming in development. Imagine a scenario in which we transform the data in Power Query Editor and then load it into the data model. If the table in the source system is large, it takes a long time to load the data into the data model, decreasing our productivity. In most cases, we do not need to load all the data into the data model...

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