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
The data profiling feature was introduced in Power BI desktop in April 2019 and it sits in the power query function. In this section, we will look at what data profiling does, how you use it, and why you would choose to use it. The advantage of data profiling is that you can review the structure of your data sets and then determine if it's the best source of data to load into the model. I will list some of the benefits of doing this.
Reviewing the structure of data enables you to make changes to columns that could ultimately improve performance, compression, and file size. You could also use data profiling to identify errors in your data set. Or, you could use data profiling to identify blank cells or identify outliers in the data set. In the viewing section of power query, you have three options you can select. These are column quality, column distribution, and column profile, and I will be showing you the use and benefits of each of these.
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.