In this project, I wanted to have an idea of the performance of some Amazon best-selling book authors, best-selling books with the highest price, etc. So I decided to get a Kaggle dataset in a CSV format for the data analysis operation.
Process:
Microsoft Power BI Desktop was used for this analysis. The data was imported into the Power BI desktop app.
ETL (Extract, Transform, and Load) operations were performed. The data was extracted into the Power Query Editor.
In the Power Query Editor which I call the kitchen of Power BI, a lot of data transformations like deleting unnecessary columns, changing of data types, merging of data tables, splitting of columns were performed.
From the fact table, I was able to develop a data table for date that will serve as a dimensional table for time intelligence analysis.
After necessary transformations in the Power Query Editor, the data tables were closed and applied to the Power BI desktop app.
In the Power BI desktop app, I was able to create some relations between different data columns. This is done in order to enable slicing and dicing of the Power BI report.
In short, the dimensional tables also known as the lookup tables were connected to the fact table. I went ahead to create some calculated columns and measures for the data model. The data model is ready!
In the visualization pane, I selected appropriate visuals for each comparison and for showing timeline analysis. The background used in this report was designed in the Microsoft PowerPoint environment. I finally published the report into the Power BI service.
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