Remove the unused column and table for the optimization and model downsize while retaining usability. Explain the same using the view as table and performance analyzer to identify the unused fields. Summary aggregation should be used instead of detailed raw data wherever possible, especially for large tables. The data table needs to be optimized as well and has, at most, the necessary date range and columns.
Switch to a more efficient type. For example, where applicable, Text would be replaced by integers or booleans instead. Filter out the unnecessary rows in ETL, such as pre-aggregation of data in Power Query. Other things will be schematized using star schema modeling. It makes relationships easy and improves performance. Check for model size from time to time and the performance metrics to maintain the optimization.