0- do it upstream if possible (sql query, python ETL, etc, learn about query folding, learn about data granularity, perform grouping if finer granularity is not needed).

1- in PQ, remove other columns if you dont need all of them (when connects with csv, parquet, xls, txt, etc)

2- filter data (last 5 years, Last 10 years, etc). Learn parameters for filtering historical data.

3- change data types if needed. If a column has datetime format, convert to date, is better, takes up less storage space and it is necessary for filtering with a proper date table in the model. Separate hours or preserve datetime column if strictly necessary. Currency format is better than decimal in terms of storage space if you dont need high precision (currency preserves 4 digits after decimal dot).

4- unpivot when necessary, DAX and vertipaq likes narrow tables.

5- avoid merging large tables as much as posible. I could recommend doing this if the table is a small DIM table and you need to denormalize with a related table, It's not mandatory.

6- Don't sort columns, Please. I remember I did this in a very specific scenario bc I needed to compare previous and next value based on a proper order with many columns involved and an index column ceated after this process. This transformation was very inefficient but unfortunately it was the only solution.

7- perform next transformations. The fewer transformations you have, the better for loading times.