Question

 What is the best practice for handling “null” and “error” rows during transformations?
“Null” values and transformation errors—such as type conversion failures or missing fields—can disrupt data model accuracy and DAX calculations. This question investigates practical approaches to managing these issues in Power Query, including the use of Table.RemoveRowsWithErrors, conditional replacements, error-trapping functions, and strategies for preserving critical rows while cleaning incomplete or faulty data.

Answer 1

Use Table in Power BI transformations to deal with "null" and error rows. Error-prone rows can be removed using RemoveRowsWithErrors, or try...otherwise, it can be used to catch and replace errors safely. Use Table for "null" values. Change either Table or Value. To flag problems or replace defaults, use ReplaceErrorValues. Use conditional logic to prioritize maintaining important rows, and always use Column Quality tools to examine data so that you can make well-informed cleanup decisions.