Datafold's data diffing is a process that compares two tables to identify changes, similarities, additions, or removals of values, akin to a git code diff but for data tables. This technique is particularly useful for validating parity between development and production environments or across different data warehouses, offering both high-level overviews and detailed value-level insights. Data diffing helps users swiftly detect structural differences such as schema and primary key variations, as well as row-level discrepancies, enabling them to understand the impact of code changes on data. Although traditional testing frameworks like dbt catch expected changes, they may overlook unexpected modifications; therefore, data diffing complements existing tests by highlighting unforeseen differences that manual checks might miss. This practice is increasingly adopted by teams engaged in data migrations or replication to ensure data accuracy and integrity, providing a clear and efficient way to manage data quality amidst growing data and project complexity.