Atlan vs Data Hub comparison

Atlan and DataHub are both solutions in the Metadata Management category. Atlan is ranked #5 with an average rating of 8.3, while DataHub is ranked #4 with an average rating of 8.3. Atlan holds a 4.3% mindshare in MM, compared to DataHub’s 2.5% mindshare. Additionally, 100% of Atlan users are willing to recommend the solution, compared to 100% of DataHub users who would recommend it.

Atlan

Read 10 Atlan reviews

2,625 Views
1,129 Comparison Views

100% willing to recommend

Data Hub

Read 22 Data Hub reviews

1,592 Views
541 Comparison Views

100% willing to recommend

Atlan

Data Hub

Comparison Buyer's Guide

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Executive SummaryUpdated on Mar 8, 2026

Atlan and Data Hub are competing in the data management platforms category. Atlan has an upper hand in affordability and quick deployment, while Data Hub stands out for its extensive features and integration capabilities.

Features: Atlan offers robust collaboration tools, intuitive data lineage, and seamless integration with platforms like Amazon and Tableau. Data Hub provides customizable metadata management, strong data integration capabilities, and clear lineage visualization.

Room for Improvement: Atlan could enhance its feature richness and scalability. Data Hub may improve its deployment process and user interface simplicity. Both could benefit from more streamlined support services.

Ease of Deployment and Customer Service: Atlan is known for rapid deployment and proactive customer support, making onboarding simple. Data Hub's deployment might require a more complex setup due to its extensive configuration options but offers comprehensive documentation and community support.

Pricing and ROI: Atlan is cost-effective, offering rapid ROI with its easy setup and minimal training needs. Data Hub's setup might be more expensive initially, but its rich features justify the investment, promising substantial long-term returns.

To learn more, read our detailed Atlan vs. Data Hub Report (Updated: June 2026).

Buyer's Guide

Atlan vs. Data Hub

June 2026

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Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

Atlan

Ranking in Metadata Management

5th

Average Rating

8.4

Reviews Sentiment

5.8

Number of Reviews

Ranking in other categories

Data Governance (12th)

Data Hub

Ranking in Metadata Management

4th

Average Rating

8.2

Reviews Sentiment

4.8

Number of Reviews

Ranking in other categories

AI Observability (7th)

Mindshare comparison

As of July 2026, in the Metadata Management category, the mindshare of Atlan is 4.3%, up from 3.7% compared to the previous year. The mindshare of Data Hub is 2.5%, up from 0.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Metadata Management Mindshare Distribution
Product	Mindshare (%)
Data Hub	2.5%
Atlan	4.3%
Other	93.2%

Metadata Management

Featured Reviews

Peter Neumann

IT consultant at Pathfinder

Has struggled to meet business needs but supports technical data exploration and transparency

Atlan can be improved by concentrating more on business data since it is developed from developers for developers, and it needs to be more business relevant. For instance, when re-importing data model diagrams, Atlan provides some diagram automation that is not connected to the business glossary, which I consider a significant fault. Atlan needs to improve by focusing more on the business side of data, not only on technical aspects.If you want to focus on technical considerations, it would be beneficial to have an interface with a real business data modeling tool such as Erwin or other business data tools, since data modeling is not the same as Draw.io. Additionally, Atlan can improve its workflows, which are hard to understand. Working with templates, Excel import, export, and running automations is not self-explanatory, and you always need help from Atlan support team. If business people want to use it and run their own reports, it must be easier to customize for their business needs.

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Akashkhurana Hirana

Senior Software Engineer 2 at Porch

Metadata management has streamlined lineage tracking and data discovery for our teams

The best features Data Hub offers include its integration capability with many popular tools like Apache Airflow, Snowflake, dbt, Looker, Apache Kafka, and BigQuery. These tools provide us with data in various places, and we commonly use Apache Airflow for the DAG, while utilizing BigQuery as our database and Apache Kafka for consuming messaging queues. Data Hub easily connects with all these tools and features excellent data discovery and visualization capabilities. We can see data visibility, where it comes from, its upstream and downstream relationships. If we remove a column, we can assess the impact of that change. Furthermore, if there are duplicate datasets being used by different teams that do not communicate regularly, onboarding all data to Data Hub allows us to identify these duplicates easily. Out of all those features, I believe data discovery and impact analysis are the most valuable for my team because when we want to add or drop a column, we can assess the impact analysis to understand the downstream effects. This helps us know who owns a dataset, and we can easily contact the owner. Tracking the data lineage back to the source table is also a key benefit. Data Hub has positively impacted my organization by significantly reducing manual work that was previously needed to identify upstream and downstream data relationships, as well as recognizing duplicate datasets. If a data contract is broken, we now easily get notified of those issues, making the process much easier and more efficient. It is particularly useful for data engineers and platform teams to check for problems directly within Data Hub. Data Hub has saved our team a lot of time. For example, in a large company like Porch, if I want to know whether a specific dataset exists, I can check Data Hub, as it serves as a centralized point for managing the metadata of our data. While it does not contain all data, it does contain the metadata necessary for understanding the dataset's origin. If a dataset does not exist, I can simply see who the owner is and reach out to them, which reduces the dependency on others by providing direct access to information in Data Hub.

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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"The interfaces and automated imports have helped me with transparency, as we have different sources from different techniques such as DBT, Snowflake, and other regular databases, making it effective to connect these sources and navigate through them, filter them, and enrich the data with additional meter information."

"Atlas is quite intuitive."

"Atlan is helpful for identifying datasets and discovering PI data, such as the classification levels of datasets (gold, silver, bronze)."

"By switching to Atlan, we have increased our productivity and saved a lot of business time through automation."

"By switching to Atlan, we have increased our productivity and saved a lot of business time."

"They also offer automated lineage embedded in the connectors, allowing us to track where the data is coming from and where it's going. We find it very efficient in cataloging data sources. Additionally, the user-friendly interface is a big plus, making it easy for users to familiarize themselves with the solution. These two features, in my opinion, stand out the most."

"The best feature of Atlan is its integration with communication platforms like Microsoft Teams and Slack, so business users don't have to go into a data catalog to see metadata about data assets."

"The technical support services are good."

More Atlan pros

"Data Hub has saved approximately two million every four years, which is one of the major savings."

"Data Hub positively impacts my organization by enhancing collaboration as previously, we had to ask the team to provide the schema information."

"Data Hub has positively impacted our organization by bringing the tribal knowledge that resides with team members into a single place where users can discover and understand the data elements before they make use of it."

"We made it a place where all stakeholders in our company could log in and see which data were used for which data marts, which column values meant for which definitions, and how they were measured."

"Acryl Data has positively impacted my organization by speeding up all the development."

"I find Data Hub to be a very important data catalog tool for a company that values data."

"Data Hub positively impacts my organization and clients by making it easier to search for data, facilitating collaboration, and helping save time."

"Data Hub has positively impacted my organization by significantly reducing manual work that was previously needed to identify upstream and downstream data relationships, as well as recognizing duplicate datasets."

More Data Hub pros

Cons

"The product could be improved by offering scheduled email reports for managed assets."

"Customer support for Atlan is not so proactive."

"There are some improvements. There is a feature called Playbooks, which basically allows me to automate certain activities that would otherwise be manual. It's a very interesting feature, but there is room to improve it because, depending on the task you automate, the playbooks seem to have a hard time handling the task. So, it could be improved there. Even though it's a great feature, it can evolve further."

"Working with templates, Excel import, export, and running automations is not self-explanatory, and you always need help from Atlan support team."

"The challenge is in the lineage, where it requires improvement. Atlas needs to capture areas where organizations use less known applications."

"One of the main areas for improvement is its governance capabilities."

"One area that could be improved is the capability to find duplicates of datasets."

"In my experience, it might be less suited for collaboration across teams."

More Atlan cons

"I think Data Hub can be improved by supporting the open source version better."

"For improvements to Data Hub, I feel the security is a bit on the weaker side."

"I believe Data Hub could provide more functionalities in the free version."

"Additionally, Data Hub has a problem with column-level lineage support, especially regarding non-pro users or those without any plans."

"However, concerning data quality, it is not sufficiently equipped as it lacks components to evaluate the data quality level, which is a feature available in other data catalogs, indicating an area for improvement."

"Data Hub can be improved since the version we have in our company does not support profiling for the table side."

"Integrating Data Hub with our existing tools and systems was not very easy, which is why my rating is an eight."

"I chose seven out of ten because there are better catalogs available in the market that offer more features."

More Data Hub cons

Pricing and Cost Advice

"We pay per-user license. It's a different classification model than with other solutions, where they usually charge you for resources. So, that was a better model for us. And because of this difference in models or classification, it was cheaper for us to go with Atlan."

Information not available

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Top Industries

By visitors reading reviews

Manufacturing Company

19%

Financial Services Firm

10%

Energy/Utilities Company

Insurance Company

Financial Services Firm

17%

Outsourcing Company

14%

Manufacturing Company

Wholesaler/Distributor

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	1
Midsize Enterprise	1
Large Enterprise	9

By reviewers
Company Size	Count
Small Business	5
Midsize Enterprise	7
Large Enterprise	15

Questions from the Community

What is your experience regarding pricing and costs for Atlan?

My experience with pricing, setup cost, and licensing is that the licensing cost is a bit flexible but not affordable for smaller organizations. It might be way out of their budget, but it is very ...

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What needs improvement with Atlan?

Atlan can be improved by enhancing their support process by reducing the resolution time. They have really bad and incomplete documentation, so they should work on the documentation as well, especi...

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What is your primary use case for Atlan?

My main use case for Atlan is to define a flexible set of metadata, including measuring the quality of said data. Atlan is also a partner of ours, so we use it to integrate with our own tool. It al...

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What needs improvement with Data Hub?

I would like to add that for the connectors, there is sometimes limited support for using wildcards to get the items or assets ingested from sources like S3; it does not support very good wildcard ...

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What is your primary use case for Data Hub?

My main use case for Data Hub is data lineage tracking. With Data Hub, we track multiple sources, ingestion sources, and different sources where the data resides in S3. We bring all that metadata i...

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What advice do you have for others considering Data Hub?

My advice for others looking into using Data Hub is that it is a good tool if you want to capture all that metadata, lineage, keep track of governance, security, and observability. It just depends ...

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Comparisons

Microsoft Purview Data Governance vs Atlan

Compared 17% of the time

Alation Data Catalog vs Atlan

Compared 12% of the time

Collibra Platform vs Atlan

Compared 12% of the time

Dawiso vs Atlan

Compared 10% of the time

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Collibra Platform vs Data Hub

Compared 35% of the time

Alation Data Catalog vs Data Hub

Compared 33% of the time

Informatica Intelligent Data Management Cloud (IDMC) vs Data Hub

Compared 19% of the time

Datadog vs Data Hub

Compared 8% of the time

More Data Hub Competitors

Product Reports

Buyer's Guide

Atlan

July 2026

Download Atlan product report

Buyer's Guide

Data Hub

July 2026

Download Data Hub product report

Also Known As

No data available

Acryl Data

Overview

Atlan is a comprehensive data management platform designed to enhance data democratization, governance, and collaboration across departments in organizations. It offers a centralized data catalog, intuitive user interface, and robust governance framework to ensure data security and compliance. Users appreciate its efficiency in streamlining processes, promoting better communication, and providing insightful analytics, which drive organizational growth and success.

Atlan

Data Hub is an advanced platform designed to streamline data management processes, enhance data accessibility, and provide comprehensive analytics capabilities for informed decision-making.

Data Hub offers a unified approach to handling large-scale datasets, empowering organizations to effectively manage, analyze, and extract insights from their data infrastructure. It provides robust features for data integration, storage, and visualization, supporting diverse business needs and driving data-driven strategies.

What are the key features of Data Hub?

Data Integration: Seamlessly combines data from multiple sources for cohesive analysis.
Scalability: Supports growing data needs without performance degradation.
Analytics Tools: Enables advanced data processing and insightful reporting.
Security: Robust security protocols to ensure data compliance and protection.
Custom Dashboards: Offers customizable interfaces for tailored analytics displays.

What benefits do users experience with Data Hub?

Increased Efficiency: Streamlines data processes, reducing time and resource spent.
Cost-Effectiveness: Minimizes need for multiple data management tools.
Improved Decision Making: Provides actionable insights through data analytics.
Enhanced Collaboration: Facilitates data sharing across departments, encouraging teamwork.

Data Hub is implemented across industries such as finance, healthcare, and retail, providing tailored solutions that meet specific demands in areas like customer data analysis, patient record management, and inventory tracking. Its ability to adapt to sector-specific requirements makes it a versatile choice for businesses seeking enhanced data capabilities.

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