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.

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.

My main use case for Acryl Data is analytics.

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. The UI is good.

The best features Acryl Data offers include storage. When I mention storage, I refer to its scalability.

The positive impact of Acryl Data is that it has increased efficiency.

I do not have comments on how Acryl Data can be improved.

I have been using Acryl Data for two years.

Acryl Data is stable.

Acryl Data's scalability is good.

The customer support is good.

I did not previously use a different solution.

My experience with pricing and setup was good.

I have seen a return on investment as it has saved time.

Before choosing Acryl Data, I did not evaluate other options.

My advice to others looking into using Acryl Data is that they can use it. I gave this product a rating of 9.

My main use case for Acryl Data is to extract insights from customer data. I use Acryl Data for a project in order to identify all the customers and find out which customer buys a lot of items.

The best feature Acryl Data offers is the simplicity of the UI. The UI is simple for me because it is easy to navigate. Acryl Data has positively impacted my organization by speeding up all the development. It sped up development because the team can access data faster, improving speed by approximately 50%.

The product cannot be improved in just one area. There are no points in support or documentation that require improvement. There are no improvements needed for Acryl Data that I have not mentioned yet.

I have been using Acryl Data for five months.

Acryl Data is stable.

I think the scalability of Acryl Data is a good point.

The customer support is fine; we do not need any customer support, but I think it was fine.

I did not previously use a different solution; I have no experience with any other solutions.

I have seen a return on investment through time saved and also money saved. I do not have specific numbers or examples about the time or money saved.

I did not evaluate other options before choosing Acryl Data; I evaluated only this option.

My advice to others looking into using Acryl Data is to start faster with the analytic insights. I would rate this product a 10.