Tech riderRev. 29 Sept 2026
- 1Runs onLinux, Mac, self-hosted, Windows
- 2CostsFree plan
- 3Matching approachhybrid
- 4Real-time APIYes
- 5Batch file importYes
- 6Organization matchingYes
6 lines stated Written from the maker's own pages: moj-analytical-services.github.io, github.com
Overview
Splink is ranked #4 of 25 in identity resolution software on Specifiction. It runs on Linux, macOS, Self-hosted, Windows. There is a free plan.
Splink plans and pricing
All plansCompared on identity resolution software
- Free plan
- Yesmoj-analytical-services.github.io
- Matching approach
- hybridmoj-analytical-services.github.io
- Real-time API
- Yesmoj-analytical-services.github.io
- Batch file import
- Yesmoj-analytical-services.github.io
- Organization matching
- Yesmoj-analytical-services.github.io
Facts
- Purpose
- Splink is a Python package for probabilistic record linkage that deduplicates and links records without unique identifiers.moj-analytical-services.github.io · 29 Sept 2026
- Method
- Its core linkage algorithm is based on the Fellegi-Sunter model and can be trained without labeled data using an unsupervised approach.moj-analytical-services.github.io · 29 Sept 2026
- Matching
- It supports term frequency adjustments and user-defined fuzzy matching logic.moj-analytical-services.github.io · 29 Sept 2026
- Scale
- The maker says Splink can run on DuckDB or big-data backends such as Spark for linkages of 100+ million records.moj-analytical-services.github.io · 29 Sept 2026
- Backends
- The documented SQL backends include DuckDB, Spark, SQLite, and PostgreSQL; the library generates SQL for a user-chosen backend.moj-analytical-services.github.io · 29 Sept 2026
- Backend guidance
- DuckDB is recommended for most users except the largest linkages, while Spark is recommended for very large linkages or where a Spark cluster is easier to access.moj-analytical-services.github.io · 29 Sept 2026
- Data requirements
- Splink works best with standardized data containing multiple columns that are not highly correlated, and is not designed for a single bag-of-words column.moj-analytical-services.github.io · 29 Sept 2026
- Diagnostics
- Interactive visualisations help users understand and diagnose linkage models, including dashboards for examining predictions and clusters.moj-analytical-services.github.io · 29 Sept 2026
- Install
- Splink can be installed using pip or conda, with optional backend-specific installs documented for Spark and PostgreSQL.moj-analytical-services.github.io · 29 Sept 2026
- Support
- The maker directs users with questions remaining after reading the documentation to its GitHub discussion forum.moj-analytical-services.github.io · 29 Sept 2026
- Databricks support
- The development team says it lacks access to a Databricks environment and may struggle to help with Databricks-specific issues.moj-analytical-services.github.io · 29 Sept 2026
- Use cases
- The maker lists users across government, academia, and other sectors, including the Office for National Statistics, NHS England, and the Australian Bureau of Statistics.moj-analytical-services.github.io · 29 Sept 2026
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Where it ranks on Specifiction
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Sources
- moj-analytical-services.github.io/splink/· checked 29 Sept 2026
- moj-analytical-services.github.io/splink/topic_guides/splink_fundamentals· checked 29 Sept 2026
- moj-analytical-services.github.io/splink/api_docs/visualisations.html· checked 29 Sept 2026
- moj-analytical-services.github.io/splink/getting_started.html· checked 29 Sept 2026
- github.com/moj-analytical-services/splink· checked 29 Sept 2026