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title: Fuzzy Matching in Snowflake: A Non-Exhaustive Guide
description: A step-by-step guide to building a 4-stage fuzzy matching pipeline entirely within Snowflake, designed to match messy or misspelled records to canonical names....
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# Fuzzy Matching in Snowflake: A Non-Exhaustive Guide

**[Snowflake Community](https://daily.dev/sources/snowflake_comm)** · 13 min read · 0 upvotes · 0 comments

## Summary

A step-by-step guide to building a 4-stage fuzzy matching pipeline entirely within Snowflake, designed to match messy or misspelled records to canonical names. The pipeline progresses from cheap to expensive: (1) regex-based string normalization for exact matches, (2) Cortex Search hybrid semantic/keyword search to filter candidates without a costly cross join, (3) Jaro-Winkler similarity scoring on the top 5 candidates returned by Cortex Search, and (4) LLM inference via AI_COMPLETE with Claude for the hardest remaining cases. Each stage filters out matched records before passing the remainder to the next, optimizing for both cost and accuracy. Low-confidence LLM matches are routed to a manual review queue.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://medium.com/snowflake/fuzzy-matching-in-snowflake-a-non-exhaustive-guide-b4487174269a>

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Tags: [#data-analysis](https://daily.dev/tags/data-analysis), [#snowflake](https://daily.dev/tags/snowflake)

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