Data Modelling in Power BI

Model and Optimize data in Power BI : Learn the key concepts of data modelling on Power BI.

Proper data modelling is the foundation of data analysis and creating reports in Power BI. This course lets you explore a toolbox of data cleaning, shaping, and loading techniques, which you can apply to your data.

What you’ll learn

  • Create relationships between your data sources.
  • Create a new field with calculated columns.
  • Optimize data by hiding fields and sorting visualization data.
  • Create a measure to perform calculations on your data.
  • Use a calculated table to create a relationship between two tables.
  • Format time-based data so that you can drill down for more details.

Course Content

  • Power BI Setup –> 7 lectures • 24min.
  • Model Data in Power BI –> 7 lectures • 42min.

Data Modelling in Power BI

Requirements

  • Basic knowledge of excel advised.

Proper data modelling is the foundation of data analysis and creating reports in Power BI. This course lets you explore a toolbox of data cleaning, shaping, and loading techniques, which you can apply to your data.

The process of creating a complicated data model in Power BI is straightforward. If your data is coming in from more than one transactional system, before you know it, you can have dozens of tables that you have to work with. Building a great data model is about simplifying the disarray.

Creating a great data model is one of the most important tasks that a data analyst can perform in Microsoft Power BI. By doing this job well, you help make it easier for people to understand your data, which will make building valuable Power BI reports easier for them and for you.

Providing set rules for what makes a good data model is difficult because all data is different, and the usage of that data varies. Generally, a smaller data model is better because it will perform faster and will be simpler to use. However, defining what a smaller data model entails is equally as problematic because it’s a heuristic and subjective concept.

Typically, a smaller data model is comprised of fewer tables and fewer columns in each table that the user can see. If you import all necessary tables from a sales database, but the total table count is 30 tables, the user will not find that intuitive. Collapsing those tables into five tables will make the data model more intuitive to the user, whereas if the user opens a table and finds 100 columns, they might find it overwhelming. Removing unneeded columns to provide a more manageable number will increase the likelihood that the user will read all column names. To summarize, you should aim for simplicity when designing your data models.

 

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