Book Image

Extending Power BI with Python and R

By : Luca Zavarella
Book Image

Extending Power BI with Python and R

By: Luca Zavarella

Overview of this book

Python and R allow you to extend Power BI capabilities to simplify ingestion and transformation activities, enhance dashboards, and highlight insights. With this book, you'll be able to make your artifacts far more interesting and rich in insights using analytical languages. You'll start by learning how to configure your Power BI environment to use your Python and R scripts. The book then explores data ingestion and data transformation extensions, and advances to focus on data augmentation and data visualization. You'll understand how to import data from external sources and transform them using complex algorithms. The book helps you implement personal data de-identification methods such as pseudonymization, anonymization, and masking in Power BI. You'll be able to call external APIs to enrich your data much more quickly using Python programming and R programming. Later, you'll learn advanced Python and R techniques to perform in-depth analysis and extract valuable information using statistics and machine learning. You'll also understand the main statistical features of datasets by plotting multiple visual graphs in the process of creating a machine learning model. By the end of this book, you’ll be able to enrich your Power BI data models and visualizations using complex algorithms in Python and R.
Table of Contents (22 chapters)
Section 1: Best Practices for Using R and Python in Power BI
Section 2: Data Ingestion and Transformation with R and Python in Power BI
Section 3: Data Enrichment with R and Python in Power BI
Section 3: Data Visualization with R in Power BI

Chapter 7: Logging Data from Power BI to External Sources

As you've learned from previous chapters, Power BI uses Power Query as a tool for extract, transform, load (ETL) operations. The tool in question is really very powerful – it allows you to extract data from a wide variety of data sources and then easily transform it with very user-friendly options in order to persist it into the Power BI data model. It is a tool that is only able to read information from the outside. In fact, the most stringent limitation of Power Query is its inability to write information outside of Power BI. However, thanks to the integration of analytical languages such as Python and R, you'll be able to persist information about Power Query loading and transformation processes to external files or systems. In this chapter, you will learn the following topics:

  • Logging to CSV files
  • Logging to Excel files
  • Logging to Azure SQL Server