DataMatch Enterprise
6.8#4 in data deduplication softwareWebAPILinuxSelf-hosted
VisitDataMatch Enterprise vs Entify
What both publish
| Fact | DataMatch Enterprise | Entify |
|---|---|---|
| Deployment | The 2026 release ships as a Docker container that runs on Linux, on-premises infrastructure or cloud environments. | It can run locally with Docker or with Python 3.11+ and Node 20+; the README identifies FastAPI, Splink 4, DuckDB, and Next.js in its stack. |
| Intended users | The maker describes its visual interface as intended for business users, IT specialists, data analysts and scientists, and novice users. | The project describes itself as intended to make record linkage usable by data stewards and teams without specialist record-linkage expertise. |
| Matching | It supports phonetic, numeric, domain-specific, and fuzzy matching, with tunable algorithm levels and weights. | It supports exact, Jaro-Winkler, Jaccard, and Levenshtein comparisons, with semantic blocking available as an optional extra. |
| Purpose | DataMatch Enterprise is a code-free toolkit for data profiling, cleansing, matching, and deduplication across data sources. | Entify is an early open-source workspace for entity resolution, record linkage, and data deduplication. |
DataMatch Enterprise plans
- DataMatch EnterprisePriced on request
- DataMatch Enterprise + Address VerificationPriced on request
- DataMatch Enterprise + APIPriced on request
Entify plans
- Open-sourceFree
Pick DataMatch Enterprise if…
DataMatch Enterprise suits business users, IT specialists, analysts, and scientists working with data quality and duplicate records. It can also fit teams that need address verification or API-based matching in custom projects.
Pick Entify if…
Entify is suited to data stewards and teams that need record linkage without specialist experience. It may also suit teams that want to run the workflow locally with Docker.