Data diffing is a crucial process in data transformation that involves comparing two datasets to understand the impact of code changes on their shape and content. Depending on the format of the datasets, different approaches are recommended: using git diff for local files, dbt tests or Datafold Cloud's in-database data diffing for tables in a database, and Datafold Cloud's cross-database data diffing for tables across databases. A fictional scenario at a real estate listings company, Yillow, illustrates the application of data diffing, where a code adjustment to handle changes in address delimiters led to missing listings. This issue was identified using git diff, highlighting the importance of data diffing in debugging and ensuring data integrity. The text also discusses the limitations of git diff when dealing with unsorted data and suggests alternatives like using the diff command with additional options for more effective data comparison.