Splink vs Zingg

Splink is a free Python package for probabilistic record linkage, used to deduplicate records and connect them when unique identifiers are absent. Its linkage method follows the Fellegi-Sunter model and can be trained without labeled examples. Matching can include term-frequency adjustments and user-defined fuzzy logic. Splink creates SQL for a selected backend, with documented support for DuckDB, Spark, SQLite, and PostgreSQL. DuckDB is recommended for most users; Spark is recommended for very large linkages or when a Spark cluster is more accessible. The maker says it can run on DuckDB or big-data backends such as Spark for linkages of 100+ million records. It works best with standardized data across several columns that are not highly correlated, rather than a single bag-of-words column. Interactive visualizations help examine and diagnose models, predictions, and clusters. Install with pip or conda, with optional backend-specific installation for Spark and PostgreSQL. It runs on Linux, macOS, and Windows, as well as self-hosted environments.

Zingg is open-source, warehouse-native master data management software for resolving entities across data silos and building unified views. It is intended for analytics engineers and data scientists integrating sources at scale, and runs on Linux in a self-hosted environment. Users train the system by labeling 40–50 matching pairs from their own data. Matching combines machine learning with deterministic rules that can use identifiers such as email, SSN, and passport ID. Listed language matching covers Chinese, Japanese, Thai, Hindi, German, French, and Greek without language-specific setup. The Community, Enterprise Lite, and Enterprise editions support millions of records. Enterprise editions can run incrementally against previously matched data, and Enterprise also supports real-time architecture. ZINGG_ID is a persistent identifier assigned to new entities that remains stable across model and configuration changes. Zingg can also run within Snowflake as a Snowflake Container using Snowpark. Community costs nothing and includes batch processing, probabilistic matching, and open-source support.