Share your experience using Redgate Data Masker

My main use case is the CI/CD pipeline itself, which is used across the entire organization, ranging from developer tools to production-related tools.

I have been using Perforce Delphix, the Helix version, for more than four years since I joined Synopsys. Perforce Delphix is the primary CI/CD and source control version management system here.

Perforce Delphix is used for our products, and even though Git is also available, we primarily use Perforce Delphix for one set of products. The best aspect of Perforce Delphix is the multi-modality and the ability to handle a large number of files along with decades of commit history since the inception of Synopsys. This gives us a huge advantage of something well-established compared to other tools, which makes Perforce Delphix a significant advantage. An example would be the standard CI/CD pipeline that goes from development to production branches.

The branching and merging process in our CI/CD pipeline does not have anything particularly unique; however, the advantage is that Perforce Delphix is able to handle a large number of files. I would say that it is able to handle thousands of files.

The most standout feature of Perforce Delphix is its ability to have multiple change lists, which is something Git does not have in a single sandbox version in a single client. Perforce Delphix has this ability and the capability to move changes from one client to another client. These are some of the standout features.

One area where Perforce Delphix could be improved is the ability to handle thousands of files more efficiently.

During the cleanup process, the challenge I face is that residual files remain, which makes it not as effective as I would prefer.

I have been using Perforce Delphix, the Helix version, for more than four years since I joined Synopsys.

I chose the rating of nine because Helix has the features it offers, the scalability solutions it offers, and the support it provides.

Positive

I did not previously use a different solution before Perforce Delphix.

My organization did not use a different solution before Perforce Delphix either.

I believe there is a return on investment with Perforce Delphix, but since I am not involved in those aspects, I cannot provide specific details.

We have covered the main highlights, but I would like to mention that there are some things Perforce Delphix lacks. Something lacking in the product is the CI/CD pipeline built by Git itself. For example, in Git, there is an entire CI/CD pipeline, but for us, there is no in-house CI/CD pipeline tool available that is as easy to use compared to Git. That is one thing I wanted to add.

Without the in-house CI/CD pipeline, what we do is build our own framework of CI/CD, encompassing everything. It includes all types of logging, DBRs, integration with all internal APIs, and internal tools. Everything is done with an in-house tool developed by internal Synopsys itself for decades, and it is quite effective.

I would not say that using Perforce Delphix has reduced errors; it is just an in-house advantage.

The learning regarding the features is all normal, and nothing unusual stands out to me in my day-to-day work. I have assigned an overall review rating of nine to this product.

My main use case for DataMasque is masking PHI data to use in a staging environment for production testing.

I decided to start masking PHI data specifically for staging due to a compliance requirement and to have better end-to-end testing with real production loads.

The best features DataMasque offers that stand out to me are the flexibility to create rule sets and mask UUIDs, which have been really beneficial. Additionally, their customer service and technical support have been very helpful in getting this set up.

The flexibility in creating rule sets has helped my team specifically because we have lots of dependencies on different databases and different database types. We have an RDS instance and an OpenSearch instance. Having those rule sets really allowed us to connect data between the two because the same data can be across multiple datasets. Creating these rules enabled us to not think too much about which ones need to be unique or which ones need to be the same across different tables. Whenever we run into issues or questions, we are able to talk to the DataMasque team, and they give us a reasonable technical response within a good timeframe that allows us to solve our issues pretty quickly. We have not had huge delays or blockers because of the communication between us and DataMasque.

I appreciate the consistency to continue to improve the application, and there are new features coming out this year that will be tools we use. DataMasque is doing a great job of adding new features, and we will definitely be using them in the future.

DataMasque has positively impacted our organization by already showing visible benefits in security and reduced risks for compliance, even though we are still in the process of setting up production and using it to the full extent. It will allow our developers to troubleshoot issues easily without the risk of viewing PHI or anything similar. We are seeing the benefits already of that, but we still need to complete the full integration and incorporate it into our workflows to see the full benefits, which we are currently in the process of. So far, it has been really good.

By just masking production data, we can go into this masked database set to understand edge cases and issues as they come up from support without the developers having to look at any production data. They can go into this masked dataset and see the problem firsthand, something we would not have caught with our dev dataset because it is such a smaller amount of data, whereas production has a vast amount of data that we can look through.

It is too early to tell how DataMasque can be improved since we are still in the implementation phase. We do like everything that the company offers, and they perform check-ins monthly to see if we have any issues. Right now there is not anything we would like to improve.

I have been using DataMasque for about eight months.

DataMasque is stable.

DataMasque's scalability is pretty good; we have not had any issues, and we have a pretty large dataset that it has handled well.

Customer support from DataMasque is excellent.

I did not previously use a different solution.

It was quite easy to deploy DataMasque in my environment.

My experience with the configuration process was good; it was a smooth process with no issues.

We have not seen a return on investment yet, but we expect to save money on testing.

My experience with the procurement process was easy.

My experience with pricing, setup cost, and licensing was easy. They gave us a trial license that we used, and when it expired, they extended it for us so we could finish our implementation and testing. The contracting was done with our CTO, and he signed off on it, so that part seemed to be a smooth transaction because everything was done through AWS, with no issues at all.

I did not evaluate other options before choosing DataMasque, as we had a referral to DataMasque.

I suggest trying DataMasque out and seeing how it fits your needs. Ask questions because DataMasque is great at responding to questions and making sure it fits your requirements. I give DataMasque a rating of five out of five.