How to Fuzzy-Match 1 Million Rows in BigQuery in under 10 minutes

Duplicate records rarely look like a priority at first — until they start breaking reporting, outreach, or reconciliation workflows. From slightly different versions of "Acme Inc" in a CRM to inconsistent supplier names across systems or messy post-merger datasets, fuzzy matching becomes essential whenever identical strings are no longer a reliable signal of the same real-world entity. The scaling wall: why warehouse-native fuzzy matching breaks at scale Fuzzy matching looks simple on a 1,000-row sample. But at real scale, the math changes. A naive all-to-all comparison grows at O(N²). Once you hit 100k+ rows, comparison space explodes, and local scripts or warehouse-native approaches become slow, expensive, or brittle. In practice, teams usually try a sequence of approaches before realizing the real complexity. They might start with warehouse similarity functions (such as edit distance or token similarity), hit performance limits, then switch to a quick Python script — only to discove