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

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.

Apache access logs

Let's suppose your company has a website published...