Data Model Features
Data Model Configuration
Power BI has changed the business intelligence (BI) landscape forever, enabling BI professionals and regular Excel users alike to work with big data and build beautiful and insightful dashboards. Integral to the Power BI platform is the data model.
This course discusses fundamental concepts of data modeling design within Power BI. We explore Microsoft's recommended best practices as well as common data modeling complications and the solutions to them. This course will prepare learners to develop data models strategically to achieve the best performance.
- Identify the primary components of data models and describe flat schemas and star schemas
- Diagnose common Power BI data modeling issues within models and recommend appropriate solutions to them
- Identify common challenges in Power BI data models, implement smart solutions, and avoid common mistakes
- Business professionals whose job requires them to design and build data models in Power BI
- Anyone preparing to take the Microsoft DA-100 exam
To get the most out of this course, you should be familiar with preparing data using Power BI.
Hello and welcome to this course on Designing a Data Model in Power BI. Just as important as building a data model is knowing how to deliberately design the data model for best performance, high-quality insights, and user-friendliness. My name is Chelsea Dohemann and I am a Certified Microsoft Office Master and have been teaching about Power BI since before its inception, when it was a suite of Excel add-ins. Feel free to connect with me at the link on the screen, or if you have any questions about this course or any other Cloud Academy courses, direct these queries to firstname.lastname@example.org.
The only prerequisite for this module is a familiarity with preparing data using Power BI. When you have completed this course, please do take a moment to rate it as this valuable feedback helps us deliver high-quality training to you.
Let's start by defining a data model. A Power BI data model describes a collection of data brought together and made relatable so that analysis can be performed. Data models often include data from various different sources. Because that data often comes from different sources, most data models require logical relationships to be established between the data sources so that data from those different sources can be analyzed together.
Most of the time, we will need to define or program the relationships between the data ourselves, and the way we define these relationships is extremely important as it has various downstream impacts. Furthermore, not all data will automatically play nice with the other data in the model. So, much of data modeling competency involves being aware of common problems, recognizing problem indicators within our datasets, and having the tools to resolve those items before they manifest as issues downstream for the user. Which is why this module is so important.
Now, all data is different. So one size does not fit all when it comes to data models, but all data models do seek to have a design that enables fast navigation through the data, the design of simple measures and calculations, aggregation accuracy, of course, ease of report creation, and simple report maintenance.
In this course, we will begin by defining the elements of an ideal data model. We will approach data model's various components starting with tables, relationships, and explore the impact different relationships will have on our reports. We will learn how and when to adjust table and field settings to ease information flow between the tables, and create some quick measures to help us get started building reports. We will talk about the natural occurrence of hierarchies in our data, how they can be used, and how to flatten them when needed.
Date tables are also covered as a solution to common date analysis issues. The important concept of data granularity is discussed so that we can make the best performance decisions for our data models. And we will round all this out by covering other performance considerations and how to set ourselves up for success. Let's begin.
Chelsea Dohemann is a Senior Technical Trainer and Microsoft Certified Master with almost a decade of experience in technology training. She has taught an array of applications from Microsoft products including Office 365 web apps, Microsoft Office Suite, Power BI, VBA for Excel, and SharePoint to Adobe Acrobat Pro and Creative Cloud. Being a persistent learner herself, Chelsea is acutely in-tune with the challenges of learning. She presents her topics in plain language, with real-world examples, reducing complex concepts down to their simple parts.