dedupe vs Splink
dedupe
github.comSplink
github.comSide by side
| Figure | dedupe | Splink |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | — |
| Windows | Yes | — |
| macOS | Yes | — |
| Linux | Yes | Yes |
| API | Yes | — |
| Self-hosted | Yes | Yes |
| Plans published | 0 | 1 |
| Spec score · Data Deduplication Software | 7.9 (#12) | 7.8 (#20) |
| Installation | The documentation gives `pip install dedupe` as the installation command. | Splink can be installed with pip or conda and supports Python 3.10 or later. |
| License | The GitHub repository identifies the project as MIT licensed. | The GitHub repository identifies Splink as MIT licensed. |
| Support | The library documentation directs bug reports to GitHub Issues and lists a mailing list for users. | The documentation directs users with questions to the project’s GitHub discussion forum. |
| Training | The library uses human-labeled examples to learn rules for finding similar records, including in very large databases. | Models can be trained using an unsupervised approach without training data. |
| What it does | The dedupe Python library uses machine learning for fuzzy matching, deduplication, and entity resolution on structured data. | Splink is a Python package for probabilistic record linkage that deduplicates and links records without unique identifiers. |
Plans
In their own words
Splink
Splink is a free Python package for probabilistic record linkage: it deduplicates and connects records without unique identifiers. Its core algorithm is based on the Fellegi–Sunter model, and it can train models without training data using an unsupervised approach. Matching supports term-frequency adjustments and user-defined fuzzy logic. Interactive outputs help users inspect models and diagnose linkage problems. Splink documents DuckDB, Spark, PostgreSQL, and SQLite backends; DuckDB is installed by default, while Spark and PostgreSQL have optional installs. The project says it can run with big-data backends such as Spark on datasets of 100 million or more records, and can link a million records on a laptop in around a minute. It works best with standardised data containing multiple columns that are not highly correlated. Installation is through pip or conda and requires Python 3.10 or later. The project lists Linux and self-hosted use and an MIT license.