Splink review
A flexible Python package for probabilistic matching, deduplication, and entity resolution.
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 data scientists and analytical teams working with person, business, and other structured records. The package can deduplicate records within one dataset, link records across datasets, and support incremental or real-time matching when unique identifiers are unavailable. It runs in the user's environment across Windows, macOS, and Linux, and is distributed under the MIT license.
Its main strength is the depth of its matching workflow. Splink uses the Fellegi-Sunter model, configurable blocking rules, and fuzzy comparisons including Jaro, Jaro-Winkler, Jaccard, and Levenshtein. Unsupervised parameter estimation through expectation maximisation can support model development, while pairwise match probabilities provide a basis for reviewing predicted links. Clustering then groups linked records into estimated entity identifiers. Interactive charts help diagnose linkage models, making Splink a strong fit for teams that need to inspect and refine matching logic rather than rely on a fixed matching recipe.
Platform fit is another important consideration. Computations can run through DuckDB, Spark, AWS Athena, SQLite, or PostgreSQL, with support for Pandas and PyArrow. That range suits batch file imports, analytical pipelines, and workloads that need incremental processing or real-time matching. The trade-off is operational: Splink is a Python package rather than a hosted commercial service, so teams manage the environment and model configuration themselves. Choose it for open-source, configurable linkage projects with analytical expertise; choose an alternative if a managed hosted product or a more guided setup is the priority.
Splink pros and cons
- Where it wins
- Supports deduplication and cross-dataset linking without unique identifiers
- Offers fuzzy comparisons, clustering, and interactive diagnostic charts
- Runs with DuckDB, Spark, Athena, SQLite, PostgreSQL, Pandas, and PyArrow
- Where it doesn't
- Requires a Python environment managed by the user
- Model setup and interpretation suit data science teams
- Does not provide a hosted commercial tier
Splink fact sheet, pricing and score →
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