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Splink review

Free#20 of 27 in Data Deduplication Software

A configurable, open-source toolkit for probabilistic linkage across analytical data environments.

7.4/10Editor score
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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Splink is an open-source Python package for probabilistic record linkage and entity resolution. It is designed for analysts working with records that lack unique identifiers, including teams handling government, academic, private-sector, and large-scale data-linkage projects. The package supports deduplication within a single dataset as well as linking across multiple datasets, making it suitable for pipelines where exact matching is insufficient and configurable comparison logic is important.

Its strongest technical advantage is the combination of probabilistic modelling and practical data-preparation controls. Splink uses a Fellegi-Sunter-based model, supports user-defined fuzzy matching logic, and provides SQL-based blocking rules to reduce candidate comparisons. Unsupervised model training helps develop linkage models without requiring labelled training data, while clustering produces estimated entity identifiers. Interactive visualisations support model diagnosis and evaluation, giving analysts a way to examine how linkage decisions behave. These capabilities make Splink a fit for complex entity-resolution work rather than only straightforward duplicate removal.

Execution flexibility is another important consideration. DuckDB and Spark backends support different processing environments, while optional PostgreSQL installation and integrations with SQLite and Athena broaden its database fit. Splink remains a library rather than a hosted SaaS product, so adoption is oriented toward teams that can build and operate Python- and SQL-based analytical workflows. It should appeal to organisations seeking an open-source, configurable linkage pipeline with no license cost. Teams wanting verified merge outcomes, duplicate-prevention workflows, or a managed hosted experience should choose an alternative designed around those requirements.

Splink pros and cons

  • Where it wins
    • Supports within-dataset deduplication and cross-dataset record linkage
    • Combines fuzzy logic, blocking, clustering, and unsupervised training
    • Runs with DuckDB, Spark, PostgreSQL, SQLite, or Athena
  • Where it doesn't
    • Requires analytical implementation rather than a hosted SaaS workflow
    • Does not verify merging decisions or duplicate-prevention workflows
    • Its breadth may exceed the needs of simple deduplication tasks

Splink fact sheet, pricing and score →

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