Data8 Duplicare vs Entify
Data8 Duplicare
data-8.co.ukEntify
github.comSide by side
| Figure | Data8 Duplicare | Entify |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | Yes | — |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | Yes | — |
| API | — | Yes |
| Self-hosted | — | Yes |
| Plans published | 1 | 1 |
| Spec score · Data Deduplication Software | 8.0 (#10) | 8.4 (#2) |
| Matching | It uses fuzzy matching to identify duplicates with spelling variations across fields such as names, addresses, and domains. | It supports exact, Jaro-Winkler, Jaccard, and Levenshtein comparisons, with semantic blocking available as an optional extra. |
| Purpose | Duplicare detects, merges, and helps prevent duplicate records in Microsoft Dynamics 365. | Entify is an early open-source workspace for entity resolution, record linkage, and data deduplication. |
Plans
In their own words
Data8 Duplicare
Data8 Duplicare has no write-up on Specifiction yet; its figures are above.
Entify
Entify is a free, open-source workspace for linking records, resolving entities, and deduplicating datasets. It uses Splink 4 and DuckDB for matching, with exact, Jaro-Winkler, Jaccard, and Levenshtein comparisons. Automatic setup estimates column types, proposes blocking rules, and explains its choices for users to adjust. It reads CSV, TSV, Parquet, JSON, and Excel files, and reports row counts, distinct values, and completeness by column. Review tools include match evidence, score and threshold analysis, and PDF audit reports. Merge exports keep one surviving record per entity and retain source IDs for tracing or undoing merges. Entify runs locally with Docker or with Python 3.11+ and Node 20+; demo mode works without optional sign-in or shared storage. Its Docker quickstart needs no accounts, API keys, or external services, and uploaded data stays on the machine. The project is intended for data stewards and teams without specialist record-linkage expertise. Optional semantic blocking requires an extra install of roughly 600 MB of Torch dependencies.