dedupe vs Zingg
dedupe
github.com
Zingg
zingg.aiSide by side
| Figure | dedupe | Zingg |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | Yes |
| Windows | Yes | — |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 0 | 6 |
| Spec score · Data Deduplication Software | 7.9 (#12) | 8.1 (#8) |
| Headquarters | Chicago, Illinois, United States | Lewes, Delaware, United States |
| Integrations | The project describes dedupe as a library that can be integrated into applications or import scripts; it also documents CSV deduplication through the separate csvdedupe command-line tool. | Documented connectors include Databricks, Snowflake, Cassandra, S3, Azure, Elastic, Exasol, relational databases and Spark-supported sources. |
| Intended users | The maintainers describe the library as suitable for integration into applications or import scripts, while the former Dedupe.io service was designed as an online tool for non-developers. | The maker describes Zingg as a tool for analytics engineers and data scientists to integrate data silos and build unified views at scale. |
| Privacy | The former service’s FAQ says its privacy policy prohibited renting or selling user data and third-party access without signed consent, and limited company access to debugging and service improvement. | The Snowflake page says Zingg can run directly within Snowflake as a Snowflake Container running a Snowpark application, maintaining control over data. |
| Self-hosting | The developer page says Python developers can run the library on local machines or servers and build self-hosted entity-resolution pipelines. | The documentation describes local Spark, Spark-cluster, Docker and source-build deployments, with data processing on the user's infrastructure. |
| Support | The library documentation directs bug reports to GitHub Issues and lists a mailing list for users. | The comparison page lists open source support for Community and enterprise grade support for both Enterprise editions. |
| Training | The library uses human-labeled examples to learn rules for finding similar records, including in very large databases. | The homepage says users train Zingg on their own data by labeling 40–50 matching pairs. |
| What it does | The dedupe Python library uses machine learning for fuzzy matching, deduplication, and entity resolution on structured data. | Zingg uses machine learning and Spark to link duplicate or related records into unified entity identities for master-data and identity-resolution workflows. |
Plans
Zingg
- CommunityFree
- Community (Open Source)Free
- EnterprisePriced on request
- Enterprise LitePriced on request
- Zingg EnterprisePriced on request
- Zingg Enterprise LitePriced on request
In their own words
Zingg
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.