Semantic Models

1. Semantic Models

Welcome to the final chapter of this course. In this chapter, we’ll examine how Fabric integrates with Power BI.

2. BI Developers

Before jumping into Fabric, let’s get a quick refresher on a BI Developer's typical tasks. One of the main responsibilities of a BI Developer is to build user-friendly reports or dashboards that allow stakeholders to explore data. Essentially, they help organizations turn raw data into actionable insights. BI developers need a blend of business acumen and data skills to understand how data can drive value for their organization. Power BI, a Microsoft product released in 2011, is a popular tool for BI Developers to create these dashboards.

3. New semantic models

We’ve hinted at how Power BI connects to Fabric, but now it’s time to see it in action. Recall when we added data to our lakehouse, we had the option of creating a new semantic model. Creating a new semantic model is our gateway to the Power BI universe.

4. What is a semantic model?

Let's define a semantic model. A semantic model organizes data from various sources into a more understandable and usable format. For example, say you have a lakehouse with various tables related to sales at your company. You have a table containing customer data, another with data about salespeople, and a third with data about the product being sold. In the real world, there’s a relationship between this data, but in the lakehouse, these tables all exist independently. Someone trying to find insight about a particular sale would need to identify how these tables coexist.

5. Semantic model relationships

This is where a semantic model comes into play. A semantic model formally defines the relationship between your tables. This formal relationship makes it easier for tools like Power BI to generate reports and insights without requiring users to identify these connections automatically.

6. Star schemas

A semantic model is typically in a star schema format, where a central table containing key data points connects to multiple related tables that contain additional information. Rather than cramming all data into one single table, the central table links to the other tables.

7. Other semantic model features

In addition to defining relationships between tables, semantic models include measures, which are calculations that help you quickly analyze your data in Power BI. This is useful if you know you often perform a calculation like a sum or an average. You can also define roles to manage who has access to the data in your semantic model.

8. Default Semantic Model

Finally, it’s worth noting that a default semantic model is created when you create a lakehouse. This model acts as a starting point for basic analytics without any additional configuration. There are a variety of settings that you can adjust to determine how and when that semantic model is updated. Again, we won’t dive too deep into the default semantic model in this course, but know that when a lakehouse is created, a semantic model is also created in the background.

9. Let's practice!

Let’s jump back into Fabric to get some experience working with semantic models.

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