Data Hub Reviews

Name: Data Hub
Brand: DataHub
Rating: 4.3 (10 reviews)

Vendor: DataHub

4.3 out of 5

10 reviews
100% willing to recommend

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What is Data Hub?

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

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Data Hub is the #6 ranked solution in top Metadata Management solutions and #11 ranked solution in top AI Observability solutions. PeerSpot users give Data Hub an average rating of 8.6 out of 10. Data Hub is most commonly compared to Datadog: Data Hub vs Datadog. Data Hub is popular among the large enterprise segment, accounting for 45% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a outsourcing company, accounting for 14% of all views.

Helped 899,052 peers since 2012

Featured Data Hub reviews

Akashkhurana Hirana

Senior Software Engineer II at Porch.com, Inc.

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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Matheus Costa

Data Engineer at CI&T

The best features that Data Hub offers include the capability to make conversational questions inside the platform, which I believe is the best thing that they built in the past year. It is also easy to connect different data sources. Since data lakes, I have connectors to some databases and also to some business analyst tools and other tools. I can connect many types of data inside Data Hub and see what is going on and how we govern the data. Data Hub is a pretty good tool for that. I also value very much the open-source version because it is free and everyone can use it. I do not have much experience using the conversational questions feature, but I do not need to go to the asset to see from where the data is coming from and where it is going. I can simply ask, 'How can we calculate the sales in this month?' and Data Hub will identify which table will be used and from where this data is coming from and where this data is going. This is very effective. Data Hub has impacted my organization positively by helping us build a data governance environment and share the knowledge about the data for the entire company. As we used the open-source version, we have no limitation in how many people can use the tool, which is excellent. I can conduct many tests and test as quickly as possible. It is very good for building POCs, for example. Data Hub also helps to give this understanding of the data for the entire company. Everyone in the company can see the data and know where the data is coming from and where it is going. I believe it is very effective and all the people in the organization, not only the data field personnel, can understand more about the data and also help to build better products.

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Henrique dos Anjos

Data Quality Engineer at truelogic

I know that the integrations are not easy to do, and I believe it happens because it's a customized solution. There always needs to be software developers to work on this. It's complicated; every time we want to integrate new things or new sources, we need to generate a ticket or a request to another department. When I had my experience with Atlan, for example, I was able to connect different sources in a very user-friendly way. I just needed to set up some configurations and connect to the source without having to be a software developer or develop any code in the back end. It was just a feature in the data catalog that enabled me to connect with different kinds of sources. That's why I think the disadvantage of having a customized solution. Although I think Data Hub itself is a very good tool, years ago I had the opportunity to work with it, but with a clear interface and the open-source solution, which was very clear and easy to connect. At Uber, we need to have a request when we want to integrate new sources. Regarding Data Hub's intuitiveness, regarding analytics, I would say that some quality dimensions are available for us. For example, for each field name or each column in a table, it's possible to see the frequency, how many values we have for a specific type or category, and we can see if there are new or null values, whether the columns are empty or not, along with some metrics. This is regarding the data quality dimensions, such as nullables and things of that nature. That is all we have for features. I remember when I was working with Atlan, there was a feature I liked very much—the possibility to have a sample. When I clicked on a table, I could see a short sample without needing SQL skills. I just clicked the table and could see some values or what the table represents; the data catalog would show a screen with some rows of the table. This feature was very good, but we don't have it in Data Hub the way it is implemented at Uber. I think it would be a very good feature for analytics, and we don't have it at the moment. The integration part could be better, but again, it's because it's a customized solution. I think if they used the native version of the tool, it would be simpler. The integration part and the process of setting up new data quality rules would be important for data governance players like me.

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PeerResearch reports based on Data Hub reviews

Type	Title	Date
Category	Metadata Management	Jun 11, 2026	Download
Product	Reviews, tips, and advice from real users	Jun 11, 2026	Download
Comparison	Data Hub vs Collibra Platform	Jun 11, 2026	Download
Comparison	Data Hub vs Informatica Intelligent Data Management Cloud (IDMC)	Jun 11, 2026	Download
Comparison	Data Hub vs Alation Data Catalog	Jun 11, 2026	Download

Valuable Features

Data Hub's most valuable features include role-based access ensuring consistency and security, end-to-end lineage for efficiency, and integration with tools like BigQuery and Apache Airflow. It supports metadata management, enhances data discoverability, and provides centralized data access. The platform is praised for its ability to streamline processes, save time in identifying data lineage and ownership, boost collaboration, and improve decision-making by offering a single, accurate data source.

"Data Hub has impacted my organization positively by helping us build a data governance environment and share the knowledge about the data for the entire company."
"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."
"Data Hub positively impacts my organization by enhancing collaboration as previously, we had to ask the team to provide the schema information."

Room for Improvement

Data Hub users desire built-in analytics and custom dashboards, seamless integration, simpler configuration for non-technical users, stronger security, and faster metadata testing. Enhanced AI for metadata discovery and improved DBT integration are needed. Users find the initial setup complex and seek better support for the open-source version. Improved automation and more intuitive user interfaces are also requested alongside stronger prescriptive guidance for governance workflows.

"I believe Data Hub could provide more functionalities in the free version."
"For improvements to Data Hub, I feel the security is a bit on the weaker side."
"I think Data Hub can be improved by supporting the open source version better."

ROI

Users observed ROI from Data Hub through time and resource savings. The role-based system reduces errors, and time is saved during incident management and schema lookups. Classification centralizes data, preventing unnecessary PII disclosure. Financial metrics are unspecified, but many appreciate the impact. However, some found Atlan faster, favoring it for quicker results. Certain users haven't quantified ROI yet, suggesting varied experiences with Data Hub's impact on efficiency and error management.

Popular Use Cases

Many use Data Hub for centralizing, consolidating, and cataloging data from various sources including CRMs, BigQuery, Kafka, and SQL Server. It enhances data governance, enables metadata management, tracks data lineage, and ensures compliant handling of sensitive information. Teams access centralized views to analyze cross-functional metrics, protect financial data, and streamline reporting processes. Users utilize Data Hub as an internal data library, boosting data discoverability and quality across diverse databases and platforms.

Service and Support

Data Hub's customer service is praised for being genuine, attentive, and responsive. Clients note quick responses to support tickets, usually by the next day. The presence of a proactive approach with monthly webinars and a responsive Slack community is appreciated. Several users received ample support for initial setups and specific requests like feature recommendations, while others didn't need much assistance. Some users direct their requests internally and find their support adequate when needed.

Deployment

Feedback suggests Data Hub's initial setup is user-friendly and effective, offering a great interface, SDK, APIs, and GraphQL previews. Users note swift implementation and helpful customer support. Although some find it slightly technical, available documentation aids the process. Experiences vary based on customization; however, many find it straightforward. Adopters acknowledge the role of infrastructure teams during setup and appreciate website usability.

Scalability

Data Hub effectively handles growth in data and users, showing impressive capabilities with large datasets and diverse sources. Users report seamless integration of numerous datasets without downtime. Scalability is praised, even handling extensive data from BigQuery, Kafka, SQL Server, and Snowflake. Growth and data volume are managed well, with fast scaling capability. Customization may require additional time, but its native handling of expansion is noted as efficient.

Stability

Data Hub maintains a high level of stability according to multiple users. They report no issues with reliability or downtime, describing the platform as very stable. Even during upgrades, any downtime is minimal, lasting only a few minutes. Users consistently found their interactions issue-free when accessing datasets and columns. Acryl Data, associated with the platform, is also acknowledged for its steady performance.

These insights are based on the in-depth reviews provided by peers to help you make a better buying decision.

Download our Data Hub Buyer's Guide for additional reliable information.

Review data by company size

By reviewers
Company Size	Count
Small Business	1
Midsize Enterprise	2
Large Enterprise	7

By reviewers

By visitors reading reviews
Company Size	Count
Small Business	28
Midsize Enterprise	23
Large Enterprise	42

By visitors reading reviews

Top industries

By visitors reading reviews

Outsourcing Company

14%

Financial Services Firm

12%

Manufacturing Company

10%

Construction Company

10%

Insurance Company

Computer Software Company

Wholesaler/Distributor

Comms Service Provider

Healthcare Company

Real Estate/Law Firm

Educational Organization

Non Profit

Aerospace/Defense Firm

Government

Energy/Utilities Company

University

Pharma/Biotech Company

Marketing Services Firm

Logistics Company

Individual & Family Service

Performing Arts

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Learn more about Data Hub

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.

Data Hub was previously known as Acryl Data.

Product Categories

Metadata Management

AI Observability

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Data Hub Reviews Summary
Author info	Rating	Review Summary
Senior Software Engineer II at Porch.com, Inc.	5.0	I find Data Hub excellent for data lineage, discovery, and impact analysis. It seamlessly integrates with many tools, saving significant time by centralizing metadata and automating checks, making it the best, most user-friendly solution.
Data Engineer at CI&T	4.0	I extensively use Data Hub for building data products and lineage, appreciating its conversational AI and open-source model. It significantly boosted data governance and company-wide understanding, saving time. I suggest more free features and improved data extraction, rating it 8/10.
Data Quality Engineer at truelogic	3.5	As a Data Quality Engineer, I find Uber's customized Data Hub stable and good for data lineage, but its integrations are complex and it lacks features like sample previews found in Atlan, which I consider superior for ease of use and ROI.
Software Engineer L2 at a tech vendor with 5,001-10,000 employees	4.5	I use Data Hub primarily for cataloging datasets and lineage, which helps identify impacted teams during incidents and saves significant time. Its GraphQL APIs are very useful. However, I find its security permissions inflexible and metadata tests take too long.
Manager Projects at Cognizant	4.0	Data Hub is key for my multi-database lineage and metadata governance, streamlining onboarding and troubleshooting. It's stable, scalable, and identifies schema changes well. While complex lineage sometimes lacks completeness, it's a valuable, free solution that delivers strong ROI.
Software Engineer at a tech vendor with 10,001+ employees	4.5	I found Data Hub greatly improved our data governance, discoverability, and lineage on AWS, despite its complex initial setup. It's a robust, enterprise-ready catalog, significantly reducing the time to find and understand data.
Director at a university with 1-10 employees	3.5	I primarily use Data Hub for PII classification and ownership to ensure GDPR compliance, centralizing tribal knowledge and saving significant time in data discovery. It's stable and scalable, but I desire more AI automation for classification and a better administrative UI.
data platform at a tech vendor with 1,001-5,000 employees	4.0	I use Data Hub as a data asset library for discovery, governance, and lineage, saving 30-40% time. While stable and scalable, its open-source support, AI search accuracy, and automated cleanup need improvement. It's a good, integrated solution, rated 8/10.

Title	Rating	Mindshare	Recommending
Datadog	4.3	N/A	97%	211 interviews Add to research
Informatica Intelligent Data Management Cloud (IDMC)	4.0	13.2%	92%	215 interviews Add to research

Data Hub Reviews

What is Data Hub?

Featured Data Hub reviews

PeerResearch reports based on Data Hub reviews

Valuable Features

Room for Improvement

ROI

Popular Use Cases

Service and Support

Deployment

Scalability

Stability

Review data by company size

Top industries

Compare Data Hub with alternative products

Learn more about Data Hub

Related questions

Product Categories

Popular Comparisons