Senior Data Engineer at a tech vendor with 10,001+ employees
Real User
Top 10
Jun 22, 2026
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 on how you want to use it; you can choose the open-source version or the paid version and subscription-based model. The paid versions have more features, but open-source Data Hub, which most people will try to go for, has some limitations, such as the missing column-level lineage with Spark. You need to consider those points, but overall, it is good. I would rate this product an 8 out of 10.
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. Using a tool that Data Hub provided made us visible to the faults and bugs in our different data sources. I would recommend that organizations considering Data Hub adopt GitOps practices, as we implemented it where every single ingestion or transformation was triggered by GitLab CI/CD, making it straightforward for everyone to use. That was the most innovative approach we took by running every single ingestion job as a Cron job in Kubernetes through our GitOps. I would rate this product a nine out of ten.
data platform at a tech vendor with 1,001-5,000 employees
Real User
Top 10
May 30, 2026
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.
My advice for others considering Data Hub is to utilize it, as it is free and can significantly reduce time for production support and addressing data issues, while simpler data models can benefit from the inbuilt functionalities of their respective databases. I would rate this product eight point five out of ten.
I have experience with Data Hub to some extent. I believe Data Hub uses a lot of APIs, but I don't think I'm the right person to answer that because it relies a lot on a technical aspect that I don't understand. I cannot provide you with a curated answer about it, but I know that the software development team that works with this customized solution uses APIs; I just don't know how to speak about their performance, whether it's good or not. Real-time batch processing is very important for me and my organization because some datasets are very critical for the business. If we have batch processing, it's good for the organization to set up a very large dataset, for example, and have it available on the data catalog in a short time. I agree that this is important. In both experiences I had, the integration with the catalog was with GCP. I don't have experience working with another data warehouse, so even in Atlan or now in Data Hub, it is connected with GCP. I don't use anything else like CRM, storage, or any architecture management tools; just Data Hub. I would give Data Hub a score of seven out of ten, summarizing everything that I've discussed about the product.
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.
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.
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...
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 on how you want to use it; you can choose the open-source version or the paid version and subscription-based model. The paid versions have more features, but open-source Data Hub, which most people will try to go for, has some limitations, such as the missing column-level lineage with Spark. You need to consider those points, but overall, it is good. I would rate this product an 8 out of 10.
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. Using a tool that Data Hub provided made us visible to the faults and bugs in our different data sources. I would recommend that organizations considering Data Hub adopt GitOps practices, as we implemented it where every single ingestion or transformation was triggered by GitLab CI/CD, making it straightforward for everyone to use. That was the most innovative approach we took by running every single ingestion job as a Cron job in Kubernetes through our GitOps. I would rate this product a nine out of ten.
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.
My advice for others considering Data Hub is to utilize it, as it is free and can significantly reduce time for production support and addressing data issues, while simpler data models can benefit from the inbuilt functionalities of their respective databases. I would rate this product eight point five out of ten.
I have experience with Data Hub to some extent. I believe Data Hub uses a lot of APIs, but I don't think I'm the right person to answer that because it relies a lot on a technical aspect that I don't understand. I cannot provide you with a curated answer about it, but I know that the software development team that works with this customized solution uses APIs; I just don't know how to speak about their performance, whether it's good or not. Real-time batch processing is very important for me and my organization because some datasets are very critical for the business. If we have batch processing, it's good for the organization to set up a very large dataset, for example, and have it available on the data catalog in a short time. I agree that this is important. In both experiences I had, the integration with the catalog was with GCP. I don't have experience working with another data warehouse, so even in Atlan or now in Data Hub, it is connected with GCP. I don't use anything else like CRM, storage, or any architecture management tools; just Data Hub. I would give Data Hub a score of seven out of ten, summarizing everything that I've discussed about the product.
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.
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 advice to others looking into using Acryl Data is that they can use it. I gave this product a rating of 9.
My advice to others looking into using Acryl Data is to start faster with the analytic insights. I would rate this product a 10.