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

Chapter 5: Designing a Data Model

Once you have connected to your data, done a bit of analysis, and organized it for reporting, the next step is to create a data model.

A good data model will provide faster and more accurate reporting. If the data model is easy to understand, report developers will take less time to generate reports, and it will make those reports easier to maintain. We'll see how to do that in this chapter.

In this chapter, we will cover the following topics – each using the same names you'll see in the exam:

  • Define the tables
  • Flatten out a parent-child hierarchy
  • Define role-playing dimensions
  • Configure table and column properties
  • Define quick measures
  • Resolve many-to-many relationships
  • Create a common date table
  • Define the appropriate level of data granularity
  • Design the data model to meet performance requirements