Where to Use Junk Dimension in Power BI?

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If you are dealing with multiple files to combine and each file is large, with millions of records and many columns to slice and dice, there are different options available for you to consider.

Option 1: Load and combine all files.

This option involves loading all the files and then combining them into a single table. While this solution is simple, it can lead to big problems. Each dimension or slicer could take a long time to generate. More importantly, we want the slicer to show values with a specific sequence. The solution is to add calculated columns for each dimension with a large number of records, which will result in poor performance. This option may not be feasible if the combined records are a few hundred million.

Option 2: Create multiple dimensions from the combined file.

To overcome the problems from the above option, we can create multiple dimensions from the combined file, with each dimension representing a different slice of the data, and then make a perfect star schema. While this solution is better than option 1, it has its own problems. Each dimension will need to load all files, even with a single column, which can make Power Query extremely slow. Additionally, this solution can cause serious file congestion problems, making dataset refresh impossible. This option can also result in complex relationships between the different dimensions, which can make the data model more difficult to understand and maintain.

Option 3: Create a single junk dimension to connect the main fact table.

The junk dimension should contain distinct records for the 20 columns. To implement this solution, you can follow these steps:

• Step 1: Use a similar process to load all files but only load the 20 columns.
• Step 2: Distinct this Power Query.
• Step 3: Group by dimension 1, sort, then make an index, along with a customized sort order column.
• Step 4: Use the same process for dimensions 2 through 20.

The advantages of this solution are numerous:

1. Each dimension is small, so performance issues should be minimal.
2. Custom sort orders can be created for each dimension, allowing for greater control and customization.
3. The load will not cause performance problems with Power Query, as the junk dimension is loaded once alongside the main load.
4. There will be no problem with dataset failure, ensuring consistent and reliable data.
5. All slicers will be context sensitive, meaning that all slicers will change accordingly if one or more values are selected. This ensures a smooth user experience and easier data analysis.

Compared to the previous options, creating a single junk dimension is the best way to handle a large number of columns and records in Power BI. It offers better performance, greater customization, and fewer technical problems.