Entify
6.6#7 in data deduplication softwareFree planFree planWebAPISelf-hosted
VisitEntify vs Zingg
What both publish
| Fact | Entify | Zingg |
|---|---|---|
| File formats | It reads CSV, TSV, Parquet, JSON, and Excel files. | The connector layer supports formats including Parquet, Avro, JSON, XLSX, CSV and TSV. |
| Integrations | Optional sign-in and shared storage use Clerk authentication and Supabase persistence. | Documented connectors include Databricks, Snowflake, Cassandra, S3, Azure, Elastic, Exasol, relational databases and Spark-supported sources. |
| Intended users | The project describes itself as intended to make record linkage usable by data stewards and teams without specialist record-linkage expertise. | The maker describes Zingg as a tool for analytics engineers and data scientists to integrate data silos and build unified views at scale. |
| Matching | It supports exact, Jaro-Winkler, Jaccard, and Levenshtein comparisons, with semantic blocking available as an optional extra. | It combines machine learning matching with rule based deterministic matching, which can use identifiers such as email, SSN, and passport ID. |
| Purpose | Entify is an early open-source workspace for entity resolution, record linkage, and data deduplication. | Zingg is open source, warehouse native master data management software for resolving entities and building unified views from siloed data. |
Entify plans
- Open-sourceFree
Zingg plans
- CommunityFree
- Community (Open Source)Free
- EnterprisePriced on request
- Enterprise LitePriced on request
- Zingg EnterprisePriced on request
- Zingg Enterprise LitePriced on request
Pick Entify if…
Entify is suited to data stewards and teams that need record linkage without specialist experience. It may also suit teams that want to run the workflow locally with Docker.
Pick Zingg if…
You want what its maker publishes above — check its page for the details.