Book Image

Microsoft Power BI Data Analyst Certification Guide

By : Orrin Edenfield, Edward Corcoran
5 (1)
Book Image

Microsoft Power BI Data Analyst Certification Guide

5 (1)
By: Orrin Edenfield, Edward Corcoran

Overview of this book

Microsoft Power BI enables organizations to create a data-driven culture with business intelligence for all. This guide to achieving the Microsoft Power BI Data Analyst Associate certification will help you take control of your organization's data and pass the exam with confidence. From getting started with Power BI to connecting to data sources, including files, databases, cloud services, and SaaS providers, to using Power BI’s built-in tools to build data models and produce visualizations, this book will walk you through everything from setup to preparing for the certification exam. Throughout the chapters, you'll get detailed explanations and learn how to analyze your data, prepare it for consumption by business users, and maintain an enterprise environment in a secure and efficient way. By the end of this book, you'll be able to create and maintain robust reports and dashboards, enabling you to manage a data-driven enterprise, and be ready to take the PL-300 exam with confidence.
Table of Contents (25 chapters)
1
Part 1 – Preparing the Data
6
Part 2 – Modeling the Data
11
Part 3 – Visualizing the Data
15
Part 4 – Analyzing the Data
18
Part 5 – Deploying and Maintaining Deliverables
21
Part 6 – Practice Exams

Combining data

Data can be combined from multiple queries or even multiple source files depending on the nature of the data connected in the data model.

Power Query uses different terminology compared to technologies such as relational databases, so it's important to understand both the concepts as well as the terminology used for the PL-300 exam.

Using merge queries

Merge queries are used when we want to merge or combine columns from multiple queries where some columns are different. This often happens in data warehouse environments as data tends to be normalized or split across multiple tables (imported into a Power BI data model as a query). For more information on why data is normalized in a data warehouse, we recommend reading The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling by Ralph Kimball. Tables contain key columns that allow individual records to be merged or joined together when needed. Normalizing data is standard practice for properly...