The fastest method to identify duplicates involves using hashing. A hash function transforms data (like files or database records) into a unique fixed-size string of characters (a hash value). Identical data produces the same hash, while different data produces different values with very high probability. Hashing is significantly faster than comparing every element directly because comparing short hash values is computationally cheaper than comparing large data blocks. It scales efficiently for large datasets.

For example, database systems like PostgreSQL use hashing (e.g., DISTINCT or GROUP BY with hash aggregation) to find duplicate records across millions of rows efficiently. Similarly, programming languages (like Python) use hash-based sets (set()) or dictionaries (dict()) to track unique items by their hash. File deduplication tools also leverage hashes (like MD5 or SHA-256) to find identical files without byte-by-byte comparisons after the initial hash generation.

The main advantages are speed and scalability, especially for large datasets, as computation grows near-linearly with data size. However, collisions (different data generating the same hash) are rare but possible, demanding careful hash function selection. Hashing doesn't reveal where duplicates occur, only that they exist. Future advancements involve optimized hardware acceleration. This efficiency makes it foundational for tasks in data cleaning, storage optimization, and cybersecurity.