march 12, 2026
Data catalog: comparison of solutions
In the context of the exponential growth in data volume and complexity, modern organisations are facing challenges related to data manageability. A data catalog has evolved from simple documentation into a centralised platform that enables data governance, discovery, understanding, and trust. Its primary goal is to transform fragmented data into a structured and well-documented asset accessible to a wide range of users — from data engineers to business analysts.
The market offers a wide range of tools, with the most popular open-source solutions including OpenMetadata, DataHub, Open Data Discovery (ODD), Marquez and Amundsen. The choice in favour of open-source solutions is driven by the need for flexibility, the absence of licensing restrictions, and the possibility of deep customisation to meet the requirements of a specific team.
When selecting a data catalog, it is critical to evaluate how well it meets the key requirements:
- Breadth of data source support: data warehouses, BI services, and ETL platforms.
- Capability to track data lineage.
- Availability of a unified business glossary with the ability to link business terms to data objects.
- Change tracking for metadata and data schemas.
- Integration with data quality testing frameworks and the ability to display data quality metrics within the catalog context.
- Participation in the emerging open MCP standard.
Integration ecosystem
The breadth of integration with existing infrastructure is a primary factor in successful adoption. The list of available connectors varies significantly across the different solutions.
OpenMetadata has the most comprehensive ecosystem of connectors. The solution provides deep integration with all major data warehouses (MS SQL, PostgreSQL, ClickHouse), cloud data platforms (Snowflake, BigQuery, Databricks), BI services (Power BI, Tableau, Superset), and ETL platforms (Airflow, dbt, Dagster).
DataHub demonstrates a level of maturity comparable to OpenMetadata. Its ecosystem also covers key data warehouses, popular BI tools, and offers a broad range of integrations with ETL platforms.
Amundsen provides stable support for major data sources (BigQuery, Snowflake, PostgreSQL); however, its integrations with BI and ETL platforms often require additional customisation and are less developed.
Marquez occupies a niche position, being focused primarily on lineage. Its main strengths are deep integration with Airflow, Dagster, Spark, and dbt for tracking ETL pipelines. Support for BI systems and data warehouses is limited.
ODD (Open Data Discovery), as an evolving project, is actively expanding its list of connectors. At present, it supports the main data warehouses and provides integrations with Airflow and dbt, but its ecosystem still lags behind the market leaders in terms of breadth of coverage.
Thus, OpenMetadata and DataHub demonstrate the most mature and comprehensive connector ecosystems.
Data lineage
Lineage visualises dependencies between objects and makes it possible to understand the impact of changes in the data schema. All the reviewed solutions support lineage. OpenMetadata, DataHub, and Amundsen support lineage down to the field level of objects, while ODD supports lineage only at the data object level. Marquez has historically been strong in data lineage tracking.
Business glossary
A business glossary helps formalise interaction between teams. It allows technical specialists to communicate with business users in their language and understand the business significance of a data object. OpenMetadata, DataHub, ODD, and Amundsen support a business glossary and provide the ability to link terms to data objects. OpenMetadata, DataHub, and ODD additionally support linking terms to object fields. Marquez is not designed for business users and does not include a business glossary.
Metadata management: versioning and quality
Tracking changes to data objects makes it possible to respond quickly to incidents and sudden declines in data quality. A complete change history is implemented in OpenMetadata, DataHub, ODD, and Marquez. Amundsen does not provide this capability.
Data Quality monitoring enables testing of the reliability and performance of information systems. Integration with Data Quality frameworks is supported in OpenMetadata, DataHub, and ODD. Amundsen and Marquez do not include built-in data quality monitoring.
Support for the Model Context Protocol (MCP)
The Model Context Protocol (MCP) is an open protocol designed to standardise the interaction of AI services with external systems. It enables them to search for information within the data catalogue. At present, only OpenMetadata and DataHub are actively implementing and supporting this protocol.
Conclusion
As a result, the following comparative analysis was obtained.
| Parameter | OpenMetadata | DataHub | Amundsen | Marquez | ODD |
|---|---|---|---|---|---|
|
Integration ecosystem |
Extensive connector support |
Extensive connector support |
Only core data sources |
Limited support |
Only core data sources |
|
Data lineage |
Advanced lineage support |
Advanced lineage support |
Basic lineage support |
Advanced lineage support |
Lineage at the data object level |
|
Business glossary |
Advanced business glossary support |
Advanced business glossary support |
Basic business glossary support |
No business glossary |
Advanced business glossary support |
|
Change history |
Supported |
Supported |
Not available |
Supported |
Supported |
|
Quality monitoring |
Supported |
Supported |
Not available |
Not available |
Supported |
|
MCP integration |
Supported |
Supported |
Not available |
Not available |
Not available |
The most mature and functionally complete solutions are OpenMetadata and DataHub. Both tools address all key requirements and offer the most developed integration ecosystems.
Amundsen remains a specialised solution for scenarios where the priority is efficient data search and discovery, and advanced metadata management is not required. Marquez is a narrowly focused tool for engineering teams concentrating on lineage. ODD represents a promising but still less mature project.
