Fuzzy Join
Upload a dataset to begin
Supported formats: .csv,.xlsx,.xls,.xlsm,.xlsb,.tsv
Sign in for free for unlimited files, no payment required.
About Fuzzy Join
Join tables even when spelling doesn't match perfectly.
Instead of requiring identical key values, this tool scores string similarity (using edit-distance style matching) between key values across the two files and pairs up the closest matches above a threshold you set. That makes it possible to join records like "Jon Smith" with "John Smith," or "Acme Corp." with "Acme Corporation," where a normal exact join would find zero matches.
Category: Transformation & LogicCommon Use Cases
- Matching a hand-typed customer list against a CRM export where names are spelled slightly differently
- Joining company names from a scraped dataset against an official registry with abbreviation differences
- Reconciling two address lists where street names have inconsistent abbreviations (St. vs Street)
Key Features
- Similarity-scored key matching with adjustable threshold
- Handles typos, abbreviations, and formatting inconsistencies
- Reports match confidence score alongside each joined row
Use Join Tables first if your keys are already clean and exact, save Fuzzy Join for the messier merges; afterward run Fuzzy Dedupe on the combined result to catch any near-duplicate rows the join introduced.
Frequently Asked Questions
It uses similarity scoring to match rows even when the join key isn't spelled identically across both files.
Whenever your two data sources were entered by hand or by different systems and the same entity might be spelled slightly differently in each.
Built for anyone merging data entered by different people or systems, where an exact-match join would miss most records.
Read the related guide →