Zingg vs. CluedIn
Entity resolution inside your data platform, for data teams, vs. CluedIn's Azure-native MDM.
Powering the world’s best companies.
Fortnum & MasonRedica SystemsOrthodox UnionDMG EventsCanadian Football League1.2k+ GitHub starsThree Reasons Teams Choose Zingg Over CluedIn
Your Platform, Any Cloud
Run on Databricks, Snowflake, Microsoft Fabric, Google Cloud or AWS, and keep resolved entities in the tables your teams already use.
Learns Your Matching
Label 40–50 pairs and Zingg learns how to block and match your data. No rules to write and no blocking keys to retune as data grows.
Built For Data Teams
Zingg runs in the notebooks, Python and SQL your team already uses. Keep the configuration in Git, schedule it with Airflow or Workflows, and monitor it with the logs and alerts you already watch.
Quick Comparison
| Feature | Zingg | CluedIn |
|---|---|---|
| Where it runs | Inside your Databricks, Snowflake, Microsoft Fabric, Google Cloud or AWS account, on your compute | Your Azure tenant (PaaS) or CluedIn's managed SaaS on Azure |
| Cost of moving data | None. No pipelines out, no second copy | Ingestion into CluedIn's own store and knowledge graph, a second copy, and write-back to Fabric |
| Your data | Stays in your tables. Zingg reads them in place and writes resolved IDs back | Ingested into CluedIn and unified in its knowledge graph |
| How it matches | Learns what a match means from 40–50 pairs you label; Enterprise adds deterministic rules in the same run | Deterministic and probabilistic rules you configure, plus AI-assisted duplicate discovery |
| Blocking and scale | Learns which records are worth comparing, so it scales to millions of records with no blocking rules to design or retune | You set blocking and match keys per domain and tune them as data grows |
| Platforms | Databricks, Snowflake, Microsoft Fabric, Google Cloud and AWS | Azure and Microsoft Fabric |
| Scope | Entity resolution: matching, clusters and a persistent ID on your tables | Full MDM with data quality, stewardship and golden records |
| Open source | Open source core plus Zingg Enterprise | Proprietary |
| Who runs it | Data teams, with their own notebooks, Git, orchestration and monitoring | Business users and data stewards, through CluedIn's no-code application |
| Best for | Teams on any platform, Fabric included, resolving entities in their own tables | Microsoft-centred teams that want no-code MDM tied into Fabric and Purview |
Zingg Resolves. SQL Does The Rest
Zingg is a headless product. It resolves your records and writes a ZINGG_ID for every one of them to your tables. Most Zingg users then build golden records and stewardship with their data platform's SQL interface, in the tools their data team already runs.
Resolve With Zingg
Zingg matches records across your sources and assigns a persistent ZINGG_ID to each real-world entity.
Golden Records In SQL
Survivorship rules become SQL or dbt models over the resolved tables, versioned and tested like the rest of your pipeline.
Stewardship Where You Work
Review and correct matches with SQL, notebooks or the BI and app tools your teams already use, with no separate MDM screen to learn.
Zingg vs. CluedIn Questions
Does CluedIn run in my Azure tenant?
CluedIn offers a PaaS edition through the Azure Marketplace that runs in your own Azure tenant, and a managed SaaS edition. In both, data is ingested into CluedIn to be mastered.
Does Zingg work with Microsoft Fabric?
Yes. Zingg runs natively on Microsoft Fabric, reading and writing your tables there, and also on Databricks, Snowflake, Google Cloud and AWS.
Based on CluedIn's public information, checked October 2026:cluedin.com/master-data-management · documentation.cluedin.net/deployment · cluedin.com/microsoft-fabric · documentation.cluedin.net/centralised-master-data-management
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Resolve every customer, supplier and product inside your data platform, on the tables you already have.