Profiling the Data
Troubleshooting in Power Query
In this course, we’ll review the Power BI Desktop interface. Then, we’ll show you how to load data into Power BI Desktop and how to save your file. We will also explain data profiling and look at the various data profiling options in Power Query like column quality, column value distribution and column profiling, and the benefits of using these.
Lastly, we will look at how to resolve cell-level errors, empties, and inconsistencies in Power Query. This section will cover how to replace errors, replace values, remove rows, and how to identify the root cause of the error using Power Query. The demos in this course will provide you with practical examples that will help you to troubleshoot when encountering issues while loading data into Power BI.
- Understand how to load data into Power BI and how to optimise functionality and size by using data profiling
- Understand how to resolve errors, empties, and data inconsistencies in Power Query
- Anyone who would like to learn about importing data into Power BI
- Anyone who needs to resolve cell-level errors or empties in a Power BI model or who would like to understand data profiling to improve the functionality of their model
- Some basic knowledge of, or experience in, working with large datasets
- Some experience with Power BI (not mandatory)
The files used in this course can be found in the following GitHub repo: https://github.com/cloudacademy/loading-data-power-bi
Don't forget to save your file. So I select Save at the top of the screen and then find the location to save the file. I name the file, The Power BI report is saved with a ".pbix" file extension, and then I press Save. I will now go the file location. An important thing to note is that when we look at the Excel file that was loaded in Power BI, it is 3,200 kilobytes, but the Power BI report that is saved is only 620 kilobytes, which is quite a bit smaller. Everything that is in the Excel file is stored in the Power BI dashboard, so it has all the same information in there. This has to do with how Power BI stores the information, and it's quite efficient from a size perspective.
Bianca is a chartered accountant and finance business partner who works with Power BI regularly to create useful, interactive dashboards to analyze financial metrics. She has worked as a lecturer and as a financial analyst in FMCG companies assisting sales and marketing teams with reviewing and understanding their financial results and forecasts, and identifying risks and opportunities for improvement. Bianca enjoys using technology to automate and simplify financial metrics.