Atlan vs Data Hub comparison

Why PeerSpot?

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 8.2. Atlan holds a 4.3% mindshare in MM, compared to DataHub’s 2.7% 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

3,253 Views
1,399 Comparison Views

100% willing to recommend

Data Hub

Read 22 Data Hub reviews

2,349 Views
728 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: August 2026).

Buyer's Guide

Atlan vs. Data Hub

August 2026

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Helped 912,930 peers since 2012

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 (13th)

Data Hub

Ranking in Metadata Management

4th

Average Rating

8.2

Reviews Sentiment

4.8

Number of Reviews

Ranking in other categories

AI Observability (8th)

Mindshare comparison

As of September 2026, in the Metadata Management category, the mindshare of Atlan is 4.3%, down from 4.6% compared to the previous year. The mindshare of Data Hub is 2.7%, up from 0.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Metadata Management Mindshare Distribution
Product	Mindshare (%)
Data Hub	2.7%
Atlan	4.3%
Other	93.0%

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 AI part is integrated for enriching metadata engagement, making it a comprehensive tool."

"Atlas is quite intuitive."

"Overall, I rate Atlan a nine out of ten."

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

"The technical support services are good."

"Overall, I rate Atlan as a ten out of ten."

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

"As a senior analytics engineer, Atlan's ability to show end-to-end data lineage is the most important feature for me."

More Atlan pros

"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."

"Data Hub has positively impacted my organization as teams can now be directly dependent on one source of truth for all their data needs."

"Data Hub has positively impacted our organization by centralizing and co-locating all data through metadata, and we have made this our enterprise metadata catalog rather than having disorganized information across different teams."

"Data Hub helped us by making it clear who owned which data and who needed to make changes to clean the deprecated data models and infrastructures we had, which was the most significant benefit."

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

"Acryl Data helps with processing large amounts of data as it is a very good tool that gives good flexibility to store a huge amount of data and is easier to use."

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

"Data Hub has positively impacted our organization by reducing the knowledge transition period from three months to one month for new team members, enabling them to refer to the complete lineage without depending heavily on others, which is a substantial improvement."

More Data Hub pros

Cons

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

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

"Customer support for Atlan is not so proactive."

"Atlan can be improved by integrating agents that can support users in finding assets of interest."

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

"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."

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

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

More Atlan cons

"Sometimes when it goes out of memory due to multiple jobs in progress, some records are dropped because the executor is dropped without completing the entire process."

"We encountered some issues when we wanted to connect our streaming infrastructure to Data Hub, which was somewhat problematic."

"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."

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

"In terms of ROI, I would say that Atlan is better. The way Data Hub is implemented at the moment, Atlan is much better; it's much, much faster."

"We are using the free version of Data Hub with Docker Compose, so it is somewhat difficult to find out the lineage."

"From our understanding, we could not really enjoy the scalability of the data."

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

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

Energy/Utilities Company

Insurance Company

Financial Services Firm

16%

Outsourcing Company

13%

Construction Company

Manufacturing Company

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?

Data Hub can be improved with easy accessibility. I think integration with other environments is needed to enhance accessibility.

See all answers

What is your primary use case for Data Hub?

My main use case for Data Hub is for data governance, specifically the use of data lineage and data catalog. I use Data Hub for data governance and data lineage in my day-to-day work by checking th...

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What advice do you have for others considering Data Hub?

I do not have any advice to give to others looking into using Data Hub. I found this interview valuable and do not think anything needs to change for the future. My overall review rating for Data H...

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Comparisons

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Compared 28% of the time

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Compared 27% of the time

Informatica Intelligent Data Management Cloud (IDMC) vs Data Hub

Compared 15% of the time

Datadog vs Data Hub

Compared 10% of the time

VyOS Platform vs Data Hub

Compared 4% of the time

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Product Reports

Buyer's Guide

Atlan

September 2026

Download Atlan product report

Buyer's Guide

Data Hub

September 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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