Identify Fuzzy Duplicates in a Million Records

Data duplication is a significant issue for many organizations, but traditional methods like Pandas' `df.drop_duplicates()` only handle exact duplicates. For fuzzy duplicates, which are not exact copies but appear similar, a naive approach of pairwise comparison is computationally infeasible at large scales. By leveraging the property of lexical overlap and applying bucketing techniques, unnecessary comparisons can be drastically reduced, optimizing the deduplication process. This approach can yield accurate results in hours rather than years, making it highly efficient for large datasets.