Book Image

Microsoft Power BI Cookbook - Second Edition

By : Greg Deckler, Brett Powell
Book Image

Microsoft Power BI Cookbook - Second Edition

By: Greg Deckler, Brett Powell

Overview of this book

The complete everyday reference guide to Power BI, written by an internationally recognized Power BI expert duo, is back with a new and updated edition. Packed with revised practical recipes, Microsoft Power BI Cookbook, Second Edition, helps you navigate Power BI tools and advanced features. It also demonstrates the use of end-to-end solutions that integrate those features to get the most out of Power BI. With the help of the recipes in this book, you’ll gain advanced design and development insight, practical tips, and guidance on enhancing existing Power BI projects. The updated recipes will equip you with everything you need to know to implement evergreen frameworks that will stay relevant as Power BI updates. You’ll familiarize yourself with Power BI development tools and services by going deep into the data connectivity, transformation, modeling, visualization, and analytical capabilities of Power BI. By the end of this book, you’ll make the most of Power BI’s functional programming languages of DAX and M and deliver powerful solutions to common business intelligence challenges.
Table of Contents (16 chapters)
14
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15
Index

Strengthening Data Import and Integration Processes

Many Power BI datasets must be created without the benefit of a data warehouse or even a relational database source system. These datasets, which often transform and merge less structured and governed data sources such as text and Excel files, generally require more complex M queries to prepare the data for analysis. The combination of greater M query complexity and periodic structural changes and data quality issues in these sources can lead to refresh failures and challenges in supporting the dataset. Additionally, as M queries are sometimes initially created exclusively via the Query Editor interface, the actual M code generated may contain unexpected logic that can lead to incorrect results and unnecessary dependencies on source data.

This recipe includes practical examples of increasing the reliability and manageability of data import processes including data source consolidation, error handling and comments, and accounting...