I have 3 databases with similar data but different schemas. Is there a tool that can build a logical data model for me?

GPT-5.4

Vote: Yes

Yes. There are data modeling and metadata discovery tools that can reverse-engineer existing database schemas and help derive a consolidated logical data model from multiple physical databases. Because your databases have similar data but different schemas, this is a common use case for schema comparison, entity matching, and model unification tools. They usually assist rather than do everything perfectly automatically, but such tools do exist. So the correct choice is Yes.

Grok 4.1 Fast

Vote: Yes

Yes, tools exist to build a logical data model from multiple databases with similar data but different schemas. Data modeling tools like erwin Data Modeler, SAP PowerDesigner, and IDERA ER/Studio support reverse engineering schemas from various databases and allow merging into a unified logical model. These tools use schema mapping, data profiling, and entity resolution to align differences, automating much of the process while offering manual refinement for accuracy. Additionally, data integration platforms like Informatica PowerCenter, Talend, or cloud-native options such as AWS Glue and Azure Data Factory include schema discovery and logical modeling capabilities across heterogeneous sources. AI-enhanced tools like those from Atlan or Collibra further automate schema matching using ML, making it feasible even for similar data in divergent schemas.

Claude Opus 4.6

Vote: Yes

Yes, there are several tools that can help build a logical data model from existing databases with different schemas. Tools like ER/Studio, ERwin Data Modeler, IBM InfoSphere Data Architect, and SAP PowerDesigner can reverse-engineer physical database schemas and help you create unified logical data models. Additionally, modern data modeling and integration tools like Erwin, Sparx Enterprise Architect, and even some ETL/data integration platforms (such as Informatica or Talend) include features for schema comparison, mapping, and logical model generation from multiple physical sources. More recently, tools focused on schema matching and data harmonization—such as those leveraging machine learning for automated schema mapping—can analyze multiple databases with similar data but different schemas and propose a consolidated logical model. The key capability here is reverse engineering physical schemas into logical models and then merging or reconciling them. This is a well-established capability in the data modeling tool space.

Gemini 3.1 Pro

Vote: Yes

There are several enterprise data modeling and data intelligence tools designed specifically to address this challenge. Industry-standard tools like erwin Data Modeler, IDERA ER/Studio, and Hackolade allow you to reverse-engineer physical databases into logical data models. They include advanced features for comparing, mapping, and merging disparate schemas into a single, unified logical model. Additionally, modern data catalogs and AI-driven data observability platforms (such as Alation, Atlan, or Select Star) can ingest metadata from multiple database sources, automatically identify relationships and similarities across different schemas using machine learning, and help construct a unified semantic or logical layer. Because the market offers multiple established solutions for reverse-engineering, mapping, and unifying different database schemas into a cohesive logical model, the answer is definitively yes.

Final Summary

The AI Roundtable delivered a rare unanimous verdict, confirming that tools for synthesizing disparate schemas into a unified logical model are well-established industry staples. While Claude Opus 4.6 and Grok 4.1 Fast pointed to legacy heavyweights like erwin and SAP PowerDesigner, Gemini 3.1 Pro and GPT-5.4 highlighted how modern AI-driven catalogs have automated the process of identifying relationships across messy metadata.

No agreement after 1 rounds of discussion. Final vote: Yes: 4

Strongest Arguments

• Yes: Modern data catalogs and AI-driven platforms can ingest metadata from multiple sources, using machine learning to automatically identify relationships and construct a unified semantic or logical layer.