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Entify

github.com

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Spec score 8.4 #2 of 27 in Data Deduplication Software

What it is

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.

Spec sheet

Purpose
Entify is an early open-source workspace for entity resolution, record linkage, and data deduplication.Read 9 Oct 2026 · github.com
Intended users
The project describes itself as intended to make record linkage usable by data stewards and teams without specialist record-linkage expertise.Read 4 Oct 2026 · github.com
Deployment
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.Read 9 Oct 2026 · github.com
Integrations
Optional sign-in and shared storage use Clerk authentication and Supabase persistence.Read 9 Oct 2026 · github.com
License
The repository lists an MIT license.Read 4 Oct 2026 · github.com
API security
The README says the backend API has no authentication and assumes it is bound to localhost.Read 4 Oct 2026 · github.com
Automatic setup
Its auto-configuration reads CSV columns, infers their roles, proposes blocking rules, and explains its decisions.Read 9 Oct 2026 · github.com
Concurrency limitation
Only one resolution result is held in memory at a time, so a second concurrent run replaces the first.Read 9 Oct 2026 · github.com
Data formats
It reads CSV, TSV, Parquet, JSON, and Excel files.Read 4 Oct 2026 · github.com
Export
Its merge export produces one surviving record per entity and retains source IDs so merges can be traced or undone.Read 9 Oct 2026 · github.com
File formats
It reads CSV, TSV, Parquet, JSON, and Excel files.Read 9 Oct 2026 · github.com
Limits
Uploads have a 100 MB cap, and the README describes the engine as memory-bound beyond roughly 100,000 rows.Read 4 Oct 2026 · github.com
Local operation
The README says the default Docker quickstart requires no accounts, API keys, or external services, and uploaded data stays on the machine.Read 9 Oct 2026 · github.com
Matching
It supports exact, Jaro-Winkler, Jaccard, and Levenshtein comparisons, with semantic blocking available as an optional extra.Read 9 Oct 2026 · github.com
Matching engine
It uses Splink 4 and DuckDB to match records and review explainable clusters.Read 4 Oct 2026 · github.com
Optional semantic blocking
Semantic blocking is optional and requires an additional install that downloads roughly 600 MB of Torch dependencies.Read 4 Oct 2026 · github.com
Profiling
It reports row counts, distinct values, and completeness for each column.Read 9 Oct 2026 · github.com
Review and audit
It offers score and threshold analysis, match evidence charts, and PDF audit reports.Read 9 Oct 2026 · github.com
Review and export
Users can inspect match evidence and threshold analyses, export deduplicated data, and generate a PDF audit report.Read 4 Oct 2026 · github.com
Scale limitation
Uploads are read fully into memory with a 100 MB default cap, and the README says workloads past roughly 100,000 rows need a DuckDB file backend and job queue.Read 9 Oct 2026 · github.com
Security limitation
The API has no authentication and assumes it is bound to localhost; the README warns against exposing it to untrusted callers as-is.Read 9 Oct 2026 · github.com
Target users
The README describes the project as intended to make record linkage usable by people who do not already understand blocking rules and match weights.Read 9 Oct 2026 · github.com

Plans and prices

Open-source Free Early open-source project; uploads capped at 100 MB by default; one resolution result held in memory at a time Read 9 Oct 2026 · github.com

Every Entify plan and price →

Strong points and limits

Strong points

  • Reads CSV, TSV, Parquet, JSON, and Excel.
  • Explains proposed column types and blocking rules.
  • Offers match evidence and threshold analysis.
  • Merge exports retain source IDs.
  • Free and open source under MIT license.

Limits

  • Uploads have a 100 MB default cap.
  • README describes memory limits beyond roughly 100,000 rows.
  • API has no authentication and assumes localhost binding.
  • Semantic blocking adds roughly 600 MB of Torch dependencies.

Who it suits

Entify suits data stewards and teams that need to link or deduplicate records without specialist record-linkage expertise. Its local Docker option may suit teams that want uploaded data to stay on their machine.

Verdict

Entify pairs guided record matching with review and traceable merge exports. Its upload and memory limits, and the unauthenticated API, are important deployment constraints.

Where Specifiction places it

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