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

Logging to an Azure SQL server

In the vast majority of companies, business information is persisted in a Relational Database Management System (RDBMS). Microsoft's quintessential relational database is SQL Server in its on-premises version if the company adopts the Microsoft data platform. Otherwise it is Azure SQL Server, which is a Platform as a Service (PaaS), cloud-hosted database.

Generally, it is a good idea to centralize all of a company's key information in a single repository. That's why it might be useful to know how to log information from within a Power BI process into a SQL Server database or an Azure SQL database.

If you have the option to already access an instance of SQL Server on-premises or Azure SQL Server, you just need to make sure that ODBC Driver for SQL Server is installed on your machine. In fact, both Python and R will connect to (Azure) SQL Server via an ODBC connection. You have the option to install the driver on your machine directly...