Data8 Duplicare vs Python Record Linkage Toolkit
Data8 Duplicare
data-8.co.ukPython Record Linkage Toolkit
recordlinkage.readthedocs.ioSide by side
| Figure | Data8 Duplicare | Python Record Linkage Toolkit |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | Yes | — |
| Open source | — | — |
| Web | Yes | — |
| Windows | Yes | — |
| Linux | — | Yes |
| Self-hosted | — | Yes |
| Plans published | 1 | 1 |
| Spec score · Data Deduplication Software | 8.0 (#10) | 7.9 (#17) |
| Purpose | Duplicare detects, merges, and helps prevent duplicate records in Microsoft Dynamics 365. | The library links records within or between data sources and supports deduplication. |
| Support | Data8 offers dedicated support to help configure Duplicare around a customer's data and objectives. | The project repository directs users with questions to contact the maintainer by email. |
Plans
Python Record Linkage Toolkit
- Python Record Linkage ToolkitFree
In their own words
Data8 Duplicare
Data8 Duplicare has no write-up on Specifiction yet; its figures are above.
Python Record Linkage Toolkit
Python Record Linkage Toolkit is a free Python library for linking records across data sources and finding duplicates within one source. It is intended for research involving small or medium-sized files. Its workflow covers cleaning and standardizing data, generating candidate pairs, comparing records, classifying matches, and evaluating results. Cleaning tools include string standardization and phonetic encoding. Blocking and sorted neighbourhood indexing create candidate pairs. Comparisons cover strings, numbers, dates, and geographic data, with string methods including Jaro, Jaro-Winkler, Levenshtein, Damerau-Levenshtein, q-gram, and cosine similarity. Supervised and unsupervised classifiers include logistic regression, Naive Bayes, and support vector machines. Evaluation tools and built-in datasets are included. Users can add indexing algorithms, comparison measures, similarity measures, and classifiers. The toolkit uses pandas and NumPy and can fit existing pandas workflows. It is self-hosted, offers fuzzy matching and API access, and installs with pip or from GitHub. The stable documentation requires Python 3.6 or higher. The repository identifies a BSD-3-Clause license. Full indexing can become slow on large DataFrames because comparisons grow quadratically.