You're reading from Extending Power BI with Python and R Ingest, transform, enrich, and visualize data using the power of analytical languages

Product type Paperback

Published in Nov 2021

Publisher Packt

ISBN-13 9781801078207

Length 558 pages

Edition 1st Edition

Languages

Python

Tools

Power BI

Concepts

Business Intelligence

Author (1):

Luca Zavarella

View More author details

Table of Contents (22) Chapters

Preface

1. Section 1: Best Practices for Using R and Python in Power BI

2. Chapter 1: Where and How to Use R and Python Scripts in Power BI FREE CHAPTER

3. Chapter 2: Configuring R with Power BI

4. Chapter 3: Configuring Python with Power BI

5. Section 2: Data Ingestion and Transformation with R and Python in Power BI

6. Chapter 4: Importing Unhandled Data Objects

7. Chapter 5: Using Regular Expressions in Power BI

8. Chapter 6: Anonymizing and Pseudonymizing Your Data in Power BI

9. Chapter 7: Logging Data from Power BI to External Sources

10. Chapter 8: Loading Large Datasets beyond the Available RAM in Power BI

11. Section 3: Data Enrichment with R and Python in Power BI

12. Chapter 9: Calling External APIs to Enrich Your Data

13. Chapter 10: Calculating Columns Using Complex Algorithms

14. Chapter 11: Adding Statistics Insights: Associations

15. Chapter 12: Adding Statistics Insights: Outliers and Missing Values

16. Chapter 13: Using Machine Learning without Premium or Embedded Capacity

17. Section 3: Data Visualization with R in Power BI

18. Chapter 14: Exploratory Data Analysis

19. Chapter 15: Advanced Visualizations

20. Chapter 16: Interactive R Custom Visuals

21. Other Books You May Enjoy

Loading complex log files using regex in Power BI

Log files are a very useful tool for developers and administrators of computer systems. They record what happened to the system, when it happened, and which user actually generated the event. Thanks to these files, you can find information about any system failure, thus allowing a faster diagnosis of the causes of these faults.

Logs are often semi-structured data, that is, information that cannot be persisted in a relational database in the format in which it is generated. In order to be analyzed with the usual tools, first, this data must be transformed into a more suitable format.

Since they are not structured data, it is difficult for them to be imported into Power BI as is, unless someone has developed a custom connector to do so. It is in these scenarios that using a regex in languages such as Python or R can help us get the desired results.