Data Hub Reviews

We adopted Data Hub in the context of a large enterprise customer operating in a regulated industry with a strong focus on data governance, data discoverability, and ownership clarity across multiple cloud-native platforms. The solution was deployed on AWS, and the main business problem was the lack of a centralized, reliable view of data assets, including poor data discoverability, unclear data ownership and stewardship, limited lineage visibility across ingestion and transformation layers, and high dependency on tribal knowledge held by a few individuals. Data Hub was selected as an enterprise data catalog and metadata backbone with the goal of enabling both technical teams and business users to easily understand, trust, and reuse data.

We used Data Hub to create very good data discoverability, assign data ownership and stewardship, improve data quality processes, and establish good data governance for our customer in terms of data catalog, data lineage, and metadata management in general.

Our key benefits that we achieved include centralized metadata management across multiple AWS services and data platforms and improved data discoverability, significantly reducing the time required to find relevant data sets. Clear data ownership and stewardship improved accountability and collaboration between teams. End-to-end lineage visibility enabled faster impact analysis and safer changes, and faster onboarding of new data users through self-service access to documentation and metadata. From a governance perspective, Data Hub became a single source of truth for metadata, supporting both compliance requirements that are very important in a data governance environment and day-to-day operational needs.

The main strengths we experienced with Data Hub are a strong metadata model and its extensibility because Data Hub offers a rich and flexible metadata model that adapts well to complex enterprise scenarios. Excellent lineage capabilities are provided because the lineage visualization is clear, actionable, and extremely useful for impact analysis and governance workflow. The open source foundation with enterprise readiness is significant because the open architecture avoids vendor lock-in while still being suitable for production-grade environments.

Data Hub is very effective for us because we build the data lineage from the beginning, from origination to visualization, to the final use of the data. We follow and track a path of the data, which improves analysis and enables us to find where data is used and the impact of deleting data. This is also very important in a regulatory environment.

The impact is very positive, and there are many benefits for us using Data Hub because it was easier to make data governance, create centralized metadata management, improve data discoverability, and manage data in general. The areas for improvement, in my opinion, are the initial setup and configuration that can be complex without prior experience, especially in large-scale environments. User experience for non-technical users could be further simplified, particularly around advanced metadata concepts. The out-of-the-box governance workflow, for example, approvals and certification, could be more prescriptive for customers at early maturity stages.

Data Hub can be improved in the initial setup and configuration that is somewhat complex, and also in operational monitoring that could benefit from more native dashboards and alerts. However, these are not blockers, but areas where additional guidance or product enhancement would further accelerate adoption.

I have been using Data Hub since 2023.

Based on internal measurement and feedback from the data teams, there are many impacts. Time to locate and understand a data set was reduced by approximately 40-50 percent. Manual documentation effort was reduced by around 40 percent. Dependency on senior data engineers for data explanation dropped significantly. Data onboarding time for new team members decreased from weeks to days.

I would rate this product a 9 out of 10. I chose nine because Data Hub proved to be a robust, scalable, enterprise-ready data catalog that is well-suited for AWS-based architecture and complex organizational environments. It is always possible to improve and useful to maintain space for further optimization.

My advice is to use Data Hub to move from fragmented metadata and manual processes to a modern, governed, and self-service data ecosystem, delivering clear value in terms of efficiency, cost saving, and data trust. We would confidently recommend Data Hub to organizations looking to improve data governance, data discovery, and metadata management on AWS.

Public Cloud

Amazon Web Services (AWS)

My main use case for Data Hub is to enrich the metadata to classify for PII data. As an administrator, I crawl a number of data sources and bring the metadata into a single place, then assign the ownership, such as a data owner or steward, for all the data assets. With their help, we classify the data into PII direct and indirect, sensitive, non-sensitive, and so on. We add tags and glossary terms onto the data elements. The main use case is for DSAR compliance; for GDPR DSAR compliance, we try to identify the PII data in the catalog so that we know where the PII data is in our data inventory.

The catalog helps with metadata discovery and to find the owners/ stewards of data sources. Without a data catalog, we scramble around and speak to multiple teams, which is time consuming.

The best features that Data Hub offers include the management of ownership, with standard out-of-the-box ownership such as business owner, data steward, or technical owner, which is relevant for us. It also integrates with Active Directory. In our Active Directory, we maintain certain roles based on the scrum teams related to a team member, and by integrating with Active Directory, we are able to bring the same roles and map them to the corresponding ownership roles within Data Hub. Data Hub has integrations with Slack, Snowflake, BigQuery, and so on, which we use.

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. Users can ask questions via Slack to understand how a data element is defined and get the answers back. This definitely saves time; without a data catalog in place, users need to ask around to find out what a particular data element means and to find out the owners. Now, with the data catalog, searching and discovering data elements and the corresponding owners is easier, saving approximately thirty to forty percent of the time that would have been spent finding out the owners and definitions of the data elements.

Data Hub can be improved with more automation; there are some inbuilt automations, such as documenting definitions of data elements using AI, which is useful. I wonder if it can automate the classification exercise, possibly using AI to auto-classify PII direct and indirect items.

Just started using it.

Data Hub is stable.

Data Hub shows scalability in terms of the number of users and the number of new databases and data elements.

We have not gone that far with customer support; as far as the POC is concerned, we received good support from the team and the sales team that helped us evaluate the tool.

Neutral

We previously used OvalEdge as our data catalog before switching to find a tool that has more AI capability and allows extension of usage to non-technical users, seeking a tool that is less clunky and more intuitive.

slightly techical. But there is enough documentation available.

No

I have not yet seen a return on investment, and I do not have that information to share.

Regarding experience with pricing, setup cost, and licensing, I think if we have a budget of one hundred thousand US dollars, we will be able to deploy a reasonable version and connect to a number of data sources.

Before choosing Data Hub, we compared it with Atlan and Alation.

I chose seven out of ten because there are better catalogs available in the market that offer more features. The UI, especially when setting up new data sources and crawling them, is a little cumbersome, but it is a one-time activity, so it is manageable; however, the UI could be improved concerning administration.

My advice to others looking into using Data Hub, also known as Acryl, is that it is a reasonably stable product that satisfies most data catalog use cases; however, Atlan appears to be the closest competitor, while Alation is the market leader among the three. Data Hub has an open-source version I believe, and it may be worth considering that option as well.

I rated this review seven out of ten.

Public Cloud

Amazon Web Services (AWS)

Our main use case for Data Hub is to build our enterprise data catalog within Visa using the open-source version.

We use Data Hub to build pipelines and to construct our enterprise data catalog to see where the data is coming from, how the lineage flows, where the lineage of the data originates from, and how the metadata propagation occurs. With this metadata information and the description of all the fields, we have built a layer on top of this that performs natural language querying for people to find where and how the data comes from.

The best features that Data Hub offers include metadata propagation and lineage propagation.

These features have specifically helped my team and our workflows by enabling people to find the right data. We have different sets of data that include business data and application data. People who are new, including data analysts, machine learning scientists, or data scientists, can easily find the specific data they are looking for because it is all centralized in one place.

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. It has saved time for many data analysts and data scientists to find the right data.

I have no comments on how Data Hub can be improved at this time.

I have been using Data Hub for the past four years.

On a scale of one to ten, I rate Data Hub a nine.

I chose nine out of ten because Data Hub is a single solution that we could adopt easily and build our platform on top of it. It provided all the features that we needed, which is why I gave that rating.

Regarding Data Hub's AI capabilities, I have not explored its governance and security features, but I would like to explore them. I have not gone through the AI features of Data Hub concerning the accuracy and reliability of output.

My advice to others looking into using Data Hub is that it is a fantastic tool for people who want to centralize and keep all the data discoverable in one single place. I would highly recommend using it. I give this review an overall rating of nine out of ten.

On-premises

My main use case for Data Hub is that we use it as a library for all the data assets that we generate. It serves as an internal data mart where people can search for whatever data they need, and they can search by tags, by roles, and then add more metadata to it. This provides visibility to the data.

A specific example of how my team uses Data Hub in a real-world scenario is that we collect and manipulate a bunch of data layers. Because we have huge teams, the exposure to data that we have already manipulated can sometimes be hindered when using traditional systems. Data Hub acts as a search engine for all of the data. One example would be when the marketing team was looking for specific data around marketing. They discovered that once they searched it on Data Hub, it was easily visible. They did not have to retrieve it from the raw layer and manipulate it for their usage because another team had already built it.

Regarding how my teams interact with Data Hub, we use Data Hub with a self-hosted system. We have connectors which look into multiple data sources, manipulation engines, and orchestration layers to gather the metadata, and then that is pulled into Data Hub. This is how we get data assets in Data Hub.

The best features that Data Hub offers include primarily data discovery and data governance. Data Hub has data catalogs, which helps with the business glossary, ownership tracking, and lineage. Lineage is something that we are strongly using at this point in time. It helps us understand the impact analysis, such as what breaks if I change this column. Data Hub also provides data observability, helping us understand what data is fresh, what is not, and what has changed schema recently. Additionally, it makes our system AI and LLM ready.

The lineage feature has changed the way my team works and collaborates significantly. Because we now have data lineage through Data Hub, if we have a really huge dependent pipeline with multiple layers of upstream and downstream dependency, and something breaks in the downstream system, we can exactly pinpoint what all data assets would be affected. Having that lineage functionality helps us drill down what needs to be debugged and fixed and what exact part is breaking. It saves us time in remedying the issue.

I really like the integrations that Data Hub provides. Data Hub has a very large set of integrations that we can do with Snowflake, Databricks, BigQuery, Redshift, DBT, and Airflow.

Data Hub has positively impacted my organization as teams can now be directly dependent on one source of truth for all their data needs. The time spent finding information has become significantly smaller, which is the real productivity improvement that I have seen, impacting multiple teams throughout the organization. I estimate that we save about thirty to forty percent of the time now since we do not have to read documents or message people for specific data assets. This results in a productivity increase of around thirty to forty percent in terms of time and efficiency.

I think Data Hub can be improved by supporting the open source version better. Many features have moved to the paid version now, making it difficult for small-scale companies to operate on Data Hub because we are required to pay, even though it started as an open source project that is now essentially behind a paywall.

One needed improvement for Data Hub would be stronger AI-powered metadata discovery. I understand Data Hub has been investing in AI, but the natural language processing power on Data Hub search is not that good. The search itself is not accurate many times. Another improvement could be enhancing the DBT developer experience, such as surfacing DBT test failures directly in lineage. Additionally, when we change schema, if it could provide a risk scoring of some sort, that would also be beneficial. Lastly, automated cleanup recommendations would help because managing orphan data assets on Data Hub currently takes a lot of manual time.

I have been using Data Hub for a year.

Data Hub is pretty stable in my experience with no downtime or issues.

Data Hub's scalability has been effective, handling our organization's growth and data volume well.

I have not had to reach out to customer support.

I did not previously use a different solution before Data Hub.

My experience with pricing, setup cost, and licensing has been pleasant, and I have no complaints.

Before choosing Data Hub, we evaluated Atlan and decided on Data Hub because it has a cleaner UI and also a decent open source community to support it.

Data Hub does most of the job it is designed to do, but there could still be improvement as the industry progresses, particularly around metadata discovery. Regarding Data Hub's AI capabilities, its governance and security do the job really well as of right now. I do not have any complaints, especially around data classification, as it allows us to have control over whatever data we are displaying, including customization for PII, sensitive, and financial data. Data Hub has met our expectations regarding its accuracy and reliability of output, and there have not been any issues.

My advice to others looking into using Data Hub is that it is a pretty nice product right now with easy integration. The pricing model could be negotiated, so it is essential to keep that in mind. I would rate Data Hub a solid eight on a scale of one to ten.

Private Cloud

Amazon Web Services (AWS)

My main use case for Data Hub is for a catalog system because we are integrating all of the data sources to Snowflake and then we want to catalog and share business glossary terms with our company employees.

A quick specific example of how I use Data Hub in my daily workflow is that we have all of the data in Snowflake and all of the employees using Snowflake did not know what kind of data is in Snowflake. They did not know all of the tables and what kind of columns and metrics, KPI definitions exist, so we are using Data Hub for searching the data in Snowflake and identifying who is using Snowflake.

My main use case is covered.

Data Hub has positively impacted my organization because there are many data analysts in each team, and the time to Q&A has significantly decreased since we started using Data Hub. This improvement is also seen in our KPI tracking.

I cannot provide specific time savings, but for example, we used to have 100 user requests for questions, which required searching Snowflake tables to determine what tables should be used, but now it is down to almost 10 questions.

In my opinion, the best features Data Hub offers are the searching function and tagging function. If I add a tag for some of the tables or columns, it is very easy to find people who need that information.

I am trying to use the tagging function for all of our data, but we are currently developing it, so we have covered almost 70% of our data.

We are using the free version of Data Hub with Docker Compose, so it is somewhat difficult to find out the lineage. If we are using Data Hub free version, then we can only figure out the tables' lineage, but we cannot search the column lineage, which is why I would like to add the columns-level lineage.

I need the lineage function for more column-level lineage and I think more example documents that are essential for our company would be very useful because there are many glossary terms and features in Data Hub, but I did not know which are more essential for us.

Additionally, I also have one more concern regarding using Docker Compose for Data Hub; the memory issues come up sometimes and consume a lot of memory resources, so I need a more efficient way to use Data Hub without these issues.

I have been using Data Hub for almost one year.

We are using private clouds in AWS, and we have deployed Data Hub on the AWS EC2 server with Docker Compose.

The cloud provider we use is AWS.

I did not purchase Data Hub through the AWS Marketplace; I am just using the EC2 server and deploying it with Docker Compose.

My advice for others looking into using Data Hub is that if there is no catalog system or data dictionary system and if there are many KPIs or metrics within their company, then I recommend Data Hub to those kinds of teams.

I give Data Hub an overall rating of 8.

Private Cloud

Amazon Web Services (AWS)

Since many developers were developing and creating data models, and there were no DDLs left in the company, we had to recreate all the descriptions of tables and clarify which columns meant what. 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. We primarily used Data Hub for sharing information and increasing the data literacy of our company.

The data injection feature was valuable to me. If comments were inserted in the tables, the data would automatically gather and enter all necessary data into Data Hub. Additionally, the data lineage graphs were really helpful in showing how data flowed to data marts and which columns were used for creating other columns. Those were helpful but somewhat hard to manage features.

I am not familiar with how people use Data Hub as a knowledge system for developing LLM models or RAG, so I am uncertain what improvements could be made in that sense. However, the way we used it, the data was quite heavy because it consisted of multiple components such as graph DBs, Elasticsearch, Kafka for data injection, and MySQL for metadata storage. This made it somewhat bulky on our server when we deployed Data Hub, and we had difficulty managing the memory constraints and disk usage.

When we used Data Hub, we attempted to provide different servers for different components, but we could not find good manuals on how to use it in a more productive server manner. Having guidelines or manuals on this could be helpful.

I used Data Hub for about a year.

Other than out-of-memory issues, Data Hub was stable because we did not have to restart services much except for memory issues. The overall service itself was stable, but it was very bulky.

For growing data graphs or data lineage, scalability depended on our manpower. From that understanding, Data Hub's scalability was not as great as we expected because we could not enter or obtain better data for all our tables because it was too much for our data teams. We had to be selective in that matter. From our understanding, we could not really enjoy the scalability of the data.

We were fortunate not to need customer service, but we did not know that technical support for Data Hub was possible. If we had been aware of that, we could have asked for support or guidance.

The initial deployment of Data Hub itself was easy because we could get the Docker Compose YAML file and run the Docker command to get the service up. However, since Data Hub uses various types of components, we had trouble assigning each server for each component and connecting them via network. We overcame that problem.

We searched for other use cases and how other companies were developing their own solutions for using data, but we have not been able to use other products similar to Data Hub itself.

I am not familiar with how the data is priced, so I cannot answer that question. Since we were running all the components on one server, there were issues when data injection was occurring too frequently, and sometimes the server could run out of memory or the disk storage could become full. We had maintenance issues that we had to handle in terms of memory and disk storage. We had to alter some injection strategies and timelines so that the data would not grow from too many tables at the same time. These maintenance jobs took place regularly. I would rate this product a 7.

I provide services related to Data Hub and data management. In total, I can consider my career using Data Hub to be approximately two to two and a half years. I tried to use it on several occasions in previous experiences, and I even started an initiative in my team to have it installed and used broadly in the company because everyone was always lost when it comes to data. They didn't know where the data comes from, which transformation it suffered, and where that data lands. These types of questions were always being asked, and I had the idea to use Data Hub, the open-source version, to give people these analytical capabilities and also integrate with Data Catalog and glossary. I tried to lead that initiative in the past, and then I left the company, so I couldn't see what the end result was.

Right now, Data Hub is already in place, and I use it sometimes, not too frequently because it's maintained by another team, but I use it as an end-user. My company is migrating off the open-source version and is moving to the Atlan version, and I think they use the same tool, but Atlan has more functionalities because they contribute to their private repository.

I used some built-in catalogs on Snowflake, for example, and Databricks, which serve well for that purpose inside their tools. In those cases, you could really see the lineage in Databricks or Snowflake, but at that time, there was no global capability. When we were evaluating Data Hub, there were other companies we were also checking, such as proprietary ones, including Atlan. Although I don't remember all of them, I haven't used those other private companies enough to be confident in my statements about the competitors.

I appreciate two things about Data Hub. One thing I appreciate a great deal is the ability that it has to automatically integrate with some of your tools and work as a receptor for receiving data information from your pipeline. For example, if the pipeline starts reading this table and writing these other tables, all this lineage information is sent to Data Hub automatically for some sources, while others only allow a pull-based approach. In those cases, the software needs to push the metrics from time to time to Data Hub, but it allows us to stream all these metrics to Data Hub, which is excellent and is almost seamlessly integrated with some tools that we had.

The second one is the lineage itself, because Data Hub offers a tremendous amount of data lineage capabilities, even column-level lineage, which was very useful for us and is still very useful.

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

Regarding what I dislike about Data Hub, I think the UI is minimalistic, and I found myself lost sometimes when looking for a specific dataset. I could find it in the search bar, but multiple things appeared with the same name, making it hard to differentiate if it was a pipeline, a table, or a schema. This UI aspect could use improvement. Additionally, there was another issue regarding configuration; back when I used it, we had to manually input a JSON with all the configurations for a given source, which caused some issues with no visibility on those issues. Mostly the UI is something they could improve.

In total, I can consider my career using Data Hub to be approximately two to two and a half years.

When I used Data Hub, I did not experience any lagging, crashing, or downtime. I only had some issues with the data refresh that sometimes resulted in outdated information, but other than that, it was good.

Regarding scalability, I think it's good, but you just need to tune it properly to handle the amount of data and data sources you need. When I used it, we didn't have a lot of tables or data entities, and the major issues I experienced were related to collecting metrics. So, while I had some performance issues, they were not related to a lot of tables.

There was no technical support available; we did everything fully on our own.

I used the open-source version of Data Hub.

I think the initial deployment was straightforward. When I used it, there were Helm charts already available, so we just had to tweak some minor things, and that was it; it was quite easy.

Data Hub does require some maintenance on my end, such as updates to keep it working. For example, the connectors that are pull-based depend on the volume of the source, and they would fail sometimes. So we had to set up monitoring tools, and whenever they failed, we needed to take a look and maybe tune the pull mechanism to make it more scalable. Other than that, that was the only operational load problem I remember having.

We have significant contracts with Google, Amazon, and Microsoft, all of them and our platform, which you can visit at peerspot.com, offers a comprehensive search bar where you can put in almost any product and probably find it, even if there are not a lot of reviews for it. So, Data Hub, as I remember, is a sort of metadata management and AI observability tool.

Currently, I am an end user of Data Hub. I'm not sure about my company's relationship with Data Hub, but personally, I've used it as an enthusiastic user of the Data Catalog subject without any official partnerships. I would rate this product a nine out of ten.

My main use case for Data Hub is to implement a data catalog for one of the clients that the consultancy I work at is serving.

A specific example of how the data catalog was used for that client is that it was used to define business terms and to explore the terms from the data glossary by adding definitions. It was also used to capture all the tables and fields that were connected to a data lake, allowing me to explore the entire production data lake and tag the tables and fields, segmenting these tables by domains such as sales tables and marketing tables.

Data Hub offers several best features including the tagging capability, domain segmentation, data exploration, and creation of a data glossary, which was very interesting to me. Additionally, the ease of plugging in new data sources is exceptional. Data Hub can be easily integrated with a data lake, and the environment can be explored through the metadata via Data Hub. I found the connection part straightforward.

Data Hub had a positive impact on my organization by disclosing to the organization and to business users what existed in the data lake. The interface that the technical team has with the tables and fields is designed for professionals in the technical area. Having a data catalog helps provide a better interface for data discovery and data democratization within the organization since everyone should have access to what types of data the organization has, and that was the biggest impact.

I started using the quality part for consistency, but I had limited contact with it and we did not progress much.

I believe the data quality module can always be improved by examining what is available in the market and making appropriate improvements to the tool. The data quality part is very important and it is not always fully leveraged as it should be. I also think that providing consulting or support with professionals who are qualified to use Data Hub would be interesting, along with providing training and certifications for the tool so that those who are implementing it can specialize increasingly in its features.

I have been using Data Hub for around one year.

Data Hub is stable, and I did not have any stability problems when I was working with the tool.

Data Hub's scalability is very easy, as we were able to add users and new datasets very quickly and smoothly.

I was not previously using a different solution. The implementation was already directly part of a data governance initiative and it was done directly with Data Hub, meaning there was no previous solution.

I believe the consultancy has some kind of commercial relationship with Data Hub to promote and offer Data Hub as a data catalog solution.

Before choosing Data Hub, the consultancy worked with some tools such as Google's DataPlex and Purview.

My advice for others thinking about using Data Hub is to have the governance initiative well-structured and to have all the documentation for data owners and data stewardship so you know who will be the points of contact when the tool starts being configured, ensuring that you have people responsible for doing reviews and approvals in the tool. I would rate this product an eight out of ten.

My main use case for Data Hub in these projects is the mapping of business glossary terms to real data for the first project and the calibration and enrichment of all the necessary information within a specific scope in the second project, which involves real data and the business glossary.

Within the engineering teams of Renault, there was a lot of data without sufficient metadata, such as descriptions of tables and columns. The objective was to complete the definitions and descriptions of business data objects within the glossary and map these descriptions to the tables and columns that comprise the data sets of this engineering department to ensure a comprehensive experience when searching for data, providing adequate definitions and descriptions of the data used in this department.

I use Data Hub within two of my clients. With Renault, the car constructor, they changed their data catalog from Zeenea to Data Hub, and I have a mission to contribute to the enrichment of this data catalog by conducting workshops with data providers, data stewards, and all the stakeholders involved in this data catalog. The aim of this mission is to map real data to the definitions and descriptions of business data objects available in the company's glossary. My second mission was with Hitachi Rail, a company that provides rail services, where the mission involved benchmarking several data catalogs including OpenMetadata and Data Galaxy. Data Hub was chosen for its available functionalities, with the task of implementing this data catalog with a specific scope and then completing the usage of this data if everything works well.

I find that my main use case for Data Hub is easy to execute because the tool is user-friendly and its functionalities are simple to understand.

The best feature that Data Hub offers in my experience is the ability to map between real data and data sets.

The mapping feature helps my team and clients significantly because it addresses the lack of metadata information about the tables and columns used in the company's data lake, enriching the data catalog considerably through this mapping.

Data Hub positively impacts my organization and clients by making it easier to search for data. It facilitates easier collaboration and helps save time. 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.

One aspect that could be improved is the ability to have more specific KPIs regarding the enrichment, completeness, and accuracy of the information.

Data Hub can be improved in several ways, primarily by enhancing the data quality evaluation capabilities. Additionally, I would suggest improving the hierarchy of business glossary terms, as understanding the characteristics of each business data object can be challenging within the current structure of business glossary terms in Data Hub.

I have been using Data Hub across these projects for approximately less than six months.

In my experience, Data Hub offers good scalability.

The customer support for Data Hub is robust. I had full support and did not use it extensively, relying primarily on Slack for questions and the documentation, which was sufficient since I utilized the open-source version.

I do not have information about Data Hub's AI capabilities. However, I can mention that the documentation of Data Hub is usable within an AI tool, specifically an LLM tool, which would simplify finding information in the documentation.

I have conducted benchmarks with OpenMetadata and Data Galaxy, but I have never used them for a mission with my clients. Before choosing Data Hub, I evaluated all the principal tools on the market, including Castor, Data Galaxy, and OpenMetadata.

I have no experience with pricing as I used the free license. My advice for others looking into using Data Hub is to consider the paid version for enhanced options related to data quality and the availability of KPIs regarding the completeness and accuracy of metadata, which results in a superior experience with this tool. I would rate this product an eight out of ten.

On-premises

Other

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 the schema and the detailed data metadata of a particular database using the data catalog and to spot the hierarchy of the data from one historical level to the present.

The best feature that Data Hub offers is its data catalog. The level of detail in the data catalog stands out to me, as it represents the column details, description, and tags, which are easily accessible whenever I want to access a particular column. Data Hub has positively impacted my organization by helping me with the data governance aspect. It has helped me reduce errors in data governance, save time, and improve compliance.

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

I have been using Data Hub for around six months.

In my experience, Data Hub is stable.

Data Hub's scalability is good.

The customer support is good, but it can be time-consuming.

I have not used a different solution before Data Hub.

I installed Data Hub on an EC2 instance and, proceeding with the database installation, accessed the complete metadata using data catalog and data lineage.

I have not seen a return on investment and cannot share any relevant metrics such as time saved or improved efficiency.

My experience with pricing, setup cost, and licensing was good.

I did not evaluate any other option before choosing 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 Hub is 8.

On-premises

Amazon Web Services (AWS)