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)
1
Section 1: Best Practices for Using R and Python in Power BI
5
Section 2: Data Ingestion and Transformation with R and Python in Power BI
11
Section 3: Data Enrichment with R and Python in Power BI
17
Section 3: Data Visualization with R in Power BI

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

In the previous chapter, you learned how to read from and write to a CSV file, both with Python and in R. When it comes to reading a file, whether you use Power BI's standard data import feature or the techniques shown in the previous chapter, the main limitation on the file size is due to the amount of RAM available on the machine where Power BI Desktop is installed.

In a data enrichment phase, it may be necessary to extract information needed for ongoing analysis from very large files (terabytes in size). In these cases, it is almost always necessary to implement big data solutions to be able to handle such masses of data. Very often, however, it is necessary to import files that are slightly larger than the available RAM in order to extract aggregate information and then persist it in a small table for reuse during processing. In such cases, it's not necessary to bother with demanding big data platforms...