DataMasque Data Masking + Synthetic Data

I am using DataMasque to explore how we can develop and optimize our old data infrastructure, and we set up Microsoft Fabric. My main use case for DataMasque at the moment is its enterprise data masking and test data management solution, designed for anonymizing sensitive information. We work on our finance data and some sensitive pilot data and how it is performing. Working on that keeps the data very confidential and also complies with GDPR and HIPAA.

A specific example of how I am using DataMasque with our finance and sensitive pilot data is that we ingested the raw data which was more confidential and a kind of test data. When we processed and did the ELT approach with all the compliance requirements such as GDPR and HIPAA applied, it actually worked well and we can use that data because it makes the schema design exposed. This allowed us to work on and train our AI models. It has benefits including referential and cross-system integrity, configuration as code integrations, which is very helpful, and it also abides with data privacy compliance. The licensing part is perpetual licensing, so it is never a situation where a large bill is coming up.

Regarding how DataMasque is fitting into my current projects, I find that the configuration as code integration is very amazing. The masking is defined with a YAML file and managed directly in the Git side, allowing development teams to integrate masking seamlessly using CI/CD jobs. More of the data privacy compliance also helps for abiding by our data on HIPAA and GDPR compliance.

DataMasque has positively impacted my organization by ensuring that the data is getting secured, it is getting compliant, and the overhead cost has been reduced as we were using Fabric before. The resulting data set always remains mathematically and structurally valid. These are two things that demonstrate it is working in our organization, though it is currently a pilot project.

Regarding the overhead cost reduction, we evaluated the overhead cost comparison before. As we are Fabric partners, we see that DataMasque uses a deployment-based and consumption-based pricing model available in the cloud marketplace. It processes runs entirely within the private cloud on-premises infrastructure with unlimited role runs. It avoids punitive per gigabyte or per record cost scaling. Legacy alternatives such as Delphix and Informatica often feature heavy, complex license fees coupled with massive infrastructure overhead, leading to significantly higher total cost of ownership. DataMasque helps us to virtualize hardware or large computer clusters as well. Custom scripts may feel straightforward initially but carry hidden operational drag due to continuous engineering hours spent fixing scripts, large schemas, and compute and storage efficiency issues.

The best features DataMasque offers really stand out. Starting with the fact that when the pipeline structure is captured, that feature is really nice. Customer names, emails, and all of the sensitive data are getting preserved because it masks the data and works on all the compliance requirements that we set up. The resulting data set always remains mathematically and structurally valid, which is really amazing.

The pipeline add-on feature helps my team by fitting into our workflow for the developers who set up an ELT setup we have for DataMasque as well. The main part is about confidentiality. In Fabric, we used to set up RLS and other security compliance, but in DataMasque, we have our own tool which keeps it safe. It is good for our data engineers to work on that.

Regarding the features and collaboration, as it sets up with Git, we easily get the configuration as code and masking rules. It provides a very collaborative approach with other software as well, so it is helping us to keep the data compliant.

DataMasque can be improved by focusing on having a virtualization setup. Unlike heavy enterprise alternatives such as Delphix, DataMasque handles static masking but does not offer built-in data virtualization, thin provisioning, or rapid dynamic data masking, which is a disadvantage for us. Another area for improvement is the single instance development, as it lacks a distributed or native scale-out engine for massive multi-terabyte clusters, which can limit raw performance compared to distributed systems. If DataMasque works on this, it would be really great, provided that the cost does not increase significantly. The GUI and UX complexity can be made more simple and intuitive for non-technical users.

I would add more about needed improvements by introducing native self-service portals because it actually builds a robust, role-based UI layer that lets developers, stakeholders, business analysts, or QA leads to request and spin up masked data securely without relying entirely on engineering writing of YAML rule sets. Expanding distributed performance scaling by implementing native parallel partitioning or scale-out execution runners to reduce processing times for ultra-large enterprise databases would be a game-changer for DataMasque. Enhanced out-of-the-box synthetic generators for advanced template-driven synthetic data generation for edge case scenarios where production data is sparse would also be beneficial. These three improvements would help DataMasque grow and prosper in the IT field.

Regarding DataMasque's AI capabilities, I think its governance and security yet need to be included.

In my organization, I have been using DataMasque as a recent addition that is currently in pilot stage.

In my experience, DataMasque is stable.

DataMasque's scalability is going well, but as it is a pilot project, we will not be able to draw conclusions yet.

The customer support team is quite helpful. When we were trying to set up, they helped us in a very supportive and collaborative way. I would rate the customer support as nine out of ten.

I previously used Microsoft Fabric before DataMasque and switched because of the costing overhead.

We have seen the early signs of return on investment.

My experience with pricing, setup cost, and licensing was more of an on-the-go kind of thing. It was quite simple, though I was involved in the detail part.

Before choosing DataMasque, I evaluated other options such as Microsoft Fabric. We started working on that and then switched to DataMasque. We are still in a pilot stage and exploring other options as well.

I would rate DataMasque overall as seven out of ten. I gave it a seven because there are a few points that need improvement. As I mentioned, three points that need focus are the non-virtualization part, the single instance limits, and the UI/UX complexity. If I were to rate it a ten, I would cut three points for these areas.

Regarding DataMasque's AI capabilities, I think its accuracy and reliability of output is great.

DataMasque is deployed in my organization primarily as a containerized application, meaning it is a cloud-agnostic approach. It will be more on the Microsoft Azure side. Currently, it is deployed on Microsoft Azure.

My advice to others reading reviews on PeerSpot looking into using DataMasque is to be open and supportive of the project because it is still in early stage. The CI/CD alignment should be done properly. The drawbacks of the UI/UX friction are there. Edge case validation hurdles are one of the things that might cause issues. Be flexible and supportive regarding DataMasque software.

I use Datamasque for data compliance, profiling, and masking.

I was able to profile all 400+ databases in our environment, identify compliance data that was not known or identified previously, and provide the teams with a self-service option to restore to lower environments. I have also allowed our Security and Compliance team access to assist with audits.

Datamasque offers ease of use, lower cost than competitors, and the ability to scan both structured and unstructured data. The development teams are willing to work with the customer and make improvements.

Datamasque is always improving with each release. They listen to their customers and are quick to implement enhancement requests or bug fixes.

I have used Datamasque for 1 year.

I previously used Delphix.

Datamasque has the majority of the same features for a much lower cost in terms of both licensing and infrastructure.

I created a staging environment with production-like data.

We can now have a staging environment very much like a production environment, which improves our testing and product quality.

The auto-generation of masking rules, integration with Git, support for supplementary masking files, the ability to run raw SQL, and partition support all make it easier to create and maintain a masking configuration. Additionally, worker settings are needed as they help improve performance.

UUID generation could be improved. Although UUIDs were masked correctly, some validation rules were not supported, requiring a switch from validators to pattern matching. Expanding native UUID validation support would be beneficial.

I have used the solution for about 3-4 months.

This is my first time using a masking tool.

As we are still in the early stages, I can't say anything definitive about pricing yet, but I believe it will be usage-based, which should work well.

Thank you for the product and the great support throughout this journey. The team has been responsive and helpful, and it has been a positive experience overall.

The testing scenarios are more complete.

Being able to generate the masking rules directly from the tool based on what it finds during the data discovery helps ensure that all sensitive fields are identified and masked.

I have used the solution for one year.

This is the first time I am using this tool.

The DM team was very happy to work with us when we were determining the pricing and licensing model we wanted to use.

The tech support is top notch!