Amundsen vs. DataHub: Which Metadata Platform Should You Choose in 2026?

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What is the difference in the architecture of Amundsen and DataHub?

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Amundsen vs. DataHub: An overview

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DataHub and Amundsen are tools developed as internal projects at LinkedIn and Lyft to address their organization’s data cataloging and discovery use cases.

DataHub was open-sourced in 2020, and the original authors founded a company, AcrylData, that maintains the project.

Amundsen, on the other hand, was open-sourced in 2019 and donated to LF AI & Data for incubation. Stemma, the company that offered a managed version of Amundsen, was acquired by Teradata in 2023.

Both DataHub and Amundsen have a microservice-based architecture, in which different services handle the frontend, the backend, metadata ingestion, full-text search, and other components.

Amundsen vs. DataHub: Architecture

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DataHub has a more complex architecture, partly because it is designed to scale to support enterprise use cases. It has four external dependencies:

1. MySQL
2. Elasticsearch
3. Kafka
4. ZooKeeper

Amundsen, on the other hand, has a simpler architecture and fewer external dependencies with:

1. Neo4j/Apache Atlas
2. Elasticsearch

This makes Amundsen deployments easier, as compared to DataHub.

Key takeaway: Amundsen, while easier to use and deploy, lacks certain features and isn’t enterprise-ready. In contrast, DataHub, while it may be slightly complex to manage, is enterprise-ready and scales well with a larger user base.

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What are the key features and focus areas of DataHub and Amundsen?

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Both DataHub and Amundsen were created as internal projects to solve very similar problems; hence, there’s a significant overlap in the feature set between the two tools.

While they support the same set of core features, Amundsen and DataHub exhibit different levels of maturity.

Some features, such as data asset popularity and user statistics, are available out of the box in Amundsen. The same features are available in DataHub, but may require additional setup. The same is true for other features that work natively in DataHub but not in Amundsen.

It’s also important to call out that while DataHub is under active development, Amundsen isn’t. Now, let’s look at the key features and how they compare.

What’s the difference?	Amundsen	DataHub
Architecture	Uses either Neo4j or Apache Atlas as the metadata storage backend, and Elasticsearch for text-based discovery.	Uses MySQL as the primary database, Kafka for communication between services (besides REST APIs), and Elasticsearch for full-text search. Has more external dependencies than Amundsen.
Search and discovery	Simpler, but limited, search interface. Has less contextual search and discovery capability than DataHub.	Many UX-enhancing features for search and discovery are available, but some of them require configuration.
Data assets supported	Mainly supports relational database, data warehouse, or data lake-type data assets in schemas, tables, and files. It also supports dashboards, but in a very limited way	Supports all types of data assets, including schemas, tables, data pipelines, dashboards, AI/ML models. Also supports data products as a concept.
Ingestion methods	With a Neo4j backend, Amundsen only supports batch ingestion workflows using Airflow as the orchestrator and Databuilder as the underlying connector library. Streaming ingestion works only when you’re using Apache Atlas as the metadata storage engine.	Supports more than 50 connectors for metadata ingestion. Uses Kafka for streaming ingestion.
Data quality	Can integrate with external data quality tools, but only to a very limited extent.	Can integrate with third-party data quality tools like dbt and GX.
Data lineage	Supports table-level lineage natively. It can also support column-level lineage, but requires fairly complex configuration and setup.	Offers column-level lineage natively, especially by extracting lineage information from tools like dbt and Airflow.
Data governance	Offers very basic support for tagging and descriptions, which doesn’t help create a better data governance experience than DataHub.	Data is secured using multiple lines of defence using sensitive data tagging, access policies, fine-grained access control, and data domains.

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What are the deployment and maintenance considerations for DataHub and Amundsen?

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While DataHub is a more complex tool to deploy, it offers more streamlined, thoroughly tested deployment patterns for both on-premises and cloud-based environments. Here’s a quick comparison of these options for both tools:

Deployment Method	DataHub	Amundsen
Local or On-Premises	Docker, k8s/Helm	Docker, k8s/Helm
AWS	EKS (official)	ECS (uses Docker)
Azure	AKS (official)	No official support
GCP	GKE (official)	No official support

Amundsen has limited deployment options. DataHub, on the other hand, supports all mainstream deployment options for local development and testing and for production environments, both on-premises and in the cloud.

All of DataHub’s deployment options are well-maintained and receive official support. Amundsen’s official deployment options have been tested but are not up to date. For Azure, GCP, and AWS EKS, Amundsen may have community support, but using those solutions isn’t usually recommended for production environments.

Key takeaway: DataHub is harder to deploy but production-ready across clouds; Amundsen is simpler upfront but less reliable for enterprise use.

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How mature are both DataHub and Amundsen, and what does their future roadmap look like?

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There are stark differences between the maturity and roadmaps of the two tools. The following table captures those differences as they stand on the 14th of December 2025.

Future roadmap criteria	DataHub	Amundsen
Project status	Active	Dormant (did not graduate)
Maintainer	Acryl Data	None (formerly Stemma)
Documentation	Up-to-date	Stale (docs still state “Vision for 2021”)
Latest release date	13 November 2025	14 August 2024

It’s clear that Amundsen’s future roadmap doesn’t exist, but DataHub’s release schedule and roadmap are frequently updated. Pre-release notes for the next release candidate, v1.4.0, are already out.

A comparison of the maturity, development, and maintenance activity for both tools should rule out Amundsen for any production use, unless you’ve already been using it for years and know the ins and outs of the tool.

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How should you choose between Amundsen and DataHub?

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Based on the evidence, DataHub is the clear choice for a data cataloging and discovery tool. Although Amundsen is simple and easy to manage, it can’t be recommended for new deployments because it is an open-source project in a dormant state.

Choose Amundsen if:

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• You already run it internally and have maintained custom extensions.
• Your needs are lightweight — basic search, simple metadata documentation, minimal governance.
• You prefer minimal operational overhead and are comfortable with a dormant roadmap.

Amundsen vs DataHub decision tree. Source: Atlan.

Choose DataHub if:

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• You are solving for enterprise maturity (scalability, reliability, roadmap).
• You require column-level lineage, streaming metadata ingestion, etc.
• You need multiple deployment options
• Enterprise governance features (tags, domains, ownership, etc.)

Key takeaway: Continue using Amundsen if your organization already has the expertise to build and maintain the project. Else, pick DataHub.

A more realistic decision: It’s rarely just Amundsen vs. DataHub

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In practice, the decision you’ll likely face isn’t DataHub vs. Amundsen, but rather a comparison of all the key candidates that address data cataloging and discovery for your organization.

Both Amundsen and DataHub provide solid foundations for search and discovery, and DataHub offers more maturity.

But neither delivers advanced governance, cross-system automation, or AI-ready context at the depth modern enterprises often require.

This is where broader platforms like Atlan enter the evaluation. They unify metadata, lineage, governance, and AI context into a single experience that supports enterprise-scale trust, compliance, and cross-team adoption.

Autodesk chose Atlan to activate their data mesh with Snowflake, after having worked with Amundsen.

“When we got to our data mesh initiative in 2021, we decided to select Amundsen. Some of the drawbacks though, being open-source, were a lot of gaps in functionality. It turned out to be a lot of work adding basic features that we needed, like the ability to update metadata by a data owner. We had to build our own UI to do that, or to add things like lineage. If we wanted to do that with Amundsen, it was an investment.” - Mark Kidwell, Chief Data Architect, Data Platforms and Services

With Atlan, Autodesk’s data teams got the primary starting point to find the data they need and immediately start using it, with capabilities, such as:

• An out-of-the-box setup with Snowflake (Autodesk’s data lake)
• Custom metadata related to data quality and ownership
• Open API access to integrate their vast data ecosystem
• Strong UX driving broader adoption – technical and business users

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Ready to choose the right metadata platform for your team?

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Choosing the right tool depends on your scale, roadmap, and governance maturity, but between these two, most new deployments will find DataHub the more sustainable long-term choice.

Amundsen can still work for lightweight use cases or teams already deeply invested in maintaining it—but its dormant roadmap limits its viability for growing data estates.

And if your organization needs capabilities that go beyond open-source catalogs—such as active metadata, advanced governance, or AI-ready context—explore platforms like Atlan in a broader evaluation.

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FAQs about Amundsen vs. DataHub

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1. Which tool should you choose, Amundsen or DataHub?

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For most organizations, DataHub is the preferable choice because it is actively developed, enterprise-ready, and offers broader capabilities such as streaming ingestion, native column-level lineage, and robust governance features.

Amundsen can still work for lightweight search-and-discovery use cases or teams that already maintain it internally, but its dormant roadmap makes it unsuitable for long-term or enterprise-scale adoption.

In practice, many teams also evaluate modern commercial platforms—such as Atlan—when they require active metadata, AI-powered search, and advanced governance beyond what open-source tools provide.

2. What is Amundsen and how does it function as a data discovery tool?

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Amundsen is an open-source data discovery tool developed by Lyft. It helps organizations manage their metadata by providing a user-friendly interface for searching and discovering data assets. Amundsen utilizes an ETL framework for metadata ingestion, allowing teams to efficiently catalog and access their data.

3. How does Amundsen compare to DataHub in terms of features and usability?

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Amundsen focuses on ease of use and quick deployment, making it suitable for teams looking for a straightforward solution.

DataHub, developed by LinkedIn, offers more advanced governance features and supports a wider range of integrations.

Both tools handle metadata management but cater to different organizational needs.

4. What are the main differences between Amundsen and DataHub regarding integration capabilities?

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Amundsen supports a variety of data sources and has a straightforward integration process.

DataHub, however, offers more extensive integration options, including support for GraphQL and Kafka, making it suitable for organizations with complex data ecosystems.

5. How do Amundsen and DataHub support data lineage tracking?

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Both Amundsen and DataHub provide data lineage tracking features.

Amundsen allows users to visualize data lineage through its catalog, while DataHub offers advanced lineage capabilities, including column-level lineage tracking, which helps organizations understand data flow and transformations.

6. When should you choose a commercial tool over Amundsen or DataHub?

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You should consider a commercial metadata platform when your needs extend beyond basic discovery into enterprise governance, automation, and AI readiness. This includes requirements like:

• Active metadata management
• Automated, actionable, cross-system column-level lineage at scale
• Automated policy enforcement and tag propagation
• Faster time-to-value
• Broad adoption across business users.

Commercial platforms (like Atlan) also reduce operational burden by providing managed deployments, support, and a clear product roadmap—capabilities that open-source tools like Amundsen and DataHub typically require significant in-house engineering effort to achieve.

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