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DQE One Unify vs Entify

DQE One Unify

dqe.tech

Entify

github.com

Side by side

FigureDQE One UnifyEntify
Price from—Free
Free plan—Yes
Free trialYes—
Open source——
WebYesYes
WindowsYes—
APIYesYes
Self-hostedYesYes
Plans published11
Spec score · Data Deduplication Software7.9 (#13)8.4 (#2)
DeploymentThe engine can run on company platforms or AWS, Azure, GCP, and Heroku; a SaaS option hosts curative processing in DQE’s cloud.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 usersDQE describes DQE One as serving technical, sales, and marketing departments, and says it supports projects involving tens of millions of records.The project describes itself as intended to make record linkage usable by data stewards and teams without specialist record-linkage expertise.
PurposeUnify identifies, merges, and prevents duplicate contacts and accounts across customer databases.Entify is an early open-source workspace for entity resolution, record linkage, and data deduplication.

Plans

DQE One Unify

  • Unify SaaSPrice not published

DQE One Unify pricing →

Entify

  • Open-sourceFree

Entify pricing →

In their own words

DQE One Unify

DQE One Unify identifies, merges, and helps prevent duplicate contacts and accounts across customer databases. Duplicate handles existing records in bulk, while Look-Up checks for an existing contact before a new record is created. Processing can be batch or real time, and the engine can run on company platforms or AWS, Azure, GCP, and Heroku. A SaaS option hosts curative processing in DQE’s cloud. For deployments in a customer’s environment, DQE says information is processed there and does not leave the customer’s IT system. The Data Stewardship interface is available in Salesforce through AppExchange, and API access is supported. A free 30-day trial is available, with limits of 200,000 database records, the first 20 duplicate groups in visualization, and the first 20 records for standardization. DQE describes the product for technical, sales, and marketing departments, including projects involving tens of millions of records. Pricing is on request.

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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.

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