Fuzzy Join Online Guide
VLOOKUP for messy data: join two CSVs on approximately-matching keys ('Acme Ltd.' = 'ACME Limited') with Jaro-Winkler or Levenshtein scoring and an interactive match-review queue. Use Fuzzy Join in your browser with no signup, no daily limit, and no backend processing for tool input.
Problem
You need a fast browser-based workflow for Fuzzy Join without setup or account friction.
What to do
Fuzzy Join gives you a focused browser workflow for one task. Use it as a step inside a larger local data workflow.
- 1Open Fuzzy Join.
- 2Enter the values or data for the task.
- 3Review, copy, or download the result.
Use the browser tool
Fuzzy Join matches two CSVs on keys that nearly agree instead of exactly agreeing — "Acme Ltd." against "ACME Limited", "O'Brien" against "OBrien". You choose the similarity algorithm (Jaro-Winkler for names, Levenshtein for typos), set a threshold, and then review the borderline matches in a queue rather than trusting a number. That review step is the important one: every fuzzy join has a band where only a human can tell a real match from a coincidence.
People also ask
- Jaro-Winkler or Levenshtein — which should I use?
- Jaro-Winkler for names and company names, because it rewards matching prefixes and handles transpositions well. Levenshtein when you expect typos or missing characters anywhere in the string, since it simply counts the edits needed.
- What threshold should I set?
- Start around 0.85 for Jaro-Winkler on names and adjust while watching the review queue. Any single number will be wrong for some pairs, which is exactly why the queue exists.
- Why did two obviously different rows match?
- Short keys. "AB Ltd" and "AC Ltd" differ by one character out of six, which scores highly on any edit-based measure. Match on a longer or more distinctive column where you can.