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CSV deduplication with quoted fields and composite keys requires a CSV-aware parser that normalizes whitespace and groups rows by key columns to remove duplicates without breaking field structures.

The best way to normalize emails and names during CSV data cleaning is to apply consistent field normalization rules, such as lowercasing emails, trimming whitespace, and applying Unicode normalization to names before comparing records.

To choose which row survives when removing duplicates from a customer list, configure winner rules that select the first, last, or maximum value based on a specific column, preserving the most relevant record per group.

Yes, you can deduplicate transaction logs using a composite key of date and ID. The deduplication process groups rows by multiple key columns and applies normalization to ensure consistent matching across your dataset.

Yes, CSV deduplication works with Unicode characters in contact imports by applying Unicode normalization to fields like names, ensuring consistent comparisons even when characters have different visual representations.