Windocks

Database virtualisation in Windocks serves virtualised copies (clones) of databases for the purposes of testing, either within a Docker container or a standard database instance. The former is one of the major selling points of the platform, and enables a clear path to Kubernetes-based test pipelines. Virtualisation in Windocks can either be delivered via storage volumes or Windows Virtual Hard Drives (popular for removing storage dependencies) and in either case creates and delivers clones in seconds. Moreover, the ability to choose between the two methods is a differentiator within the TDM space.

Fig 1 - Configuring a data subsetting process in Windocks

Windocks’ data subsetting is very highly automated, and includes the automatic resolution of circular dependencies, composite keys, and other challenges that would otherwise need to be addressed manually. As you would expect, referential integrity is always maintained. This is all accomplished by a bespoke, patented subsetting algorithm.

The platform’s synthetic data capability is also highly automated, and can create synthetic data sets that accurately represent your production data in their overall makeup while still being fabricated in the details. This results in essentially realistic data that is nevertheless entirely fake. This data is generated using an in-platform engine as well as external Python libraries, incorporating various AI and machine learning techniques and models. A ‘fast’ synthetic model is available, which accelerates the creation of synthetic data but results in a somewhat less representative data set. In addition, Windocks supports the Synthetic Data Vault open-source project, and can work with your own Python libraries or other custom code. Notably, the product’s synthetic data capability is also highly scalable: it can generate everything from an individual table to an entire database. This allows it to serve a variety of different use cases, even outside of the testing space – supplying training data for AI models, for instance.

In terms of data access, the Windocks platform is primarily available through a web UI. For starters, this UI is designed to abstract out many of the complexities of managing Docker containers. Accordingly, manual coding and configuration is kept to an absolute minimum. In fact, only very simple configuration files (Dockerfiles, specifically) are required to build your Docker images, and image builds are one-step processes that can include data subsetting, masking, and synthetic data generation. Integration with source control (Git, for instance) is built-in as part of this single step, and no staging server is needed at any point. When combined with the product’s database virtualisation functionality, the result is a versioned, test-ready database repository. The platform also supports incremental image updates, meaning that you can keep your database clones up to date whenever the underlying data changes without needing to completely rebuild and redeploy them.

Data subsetting and synthetic data generation are similarly easy to action. In both cases it is almost literally as simple as telling the platform what data you want to use as your source, where you want to put the result, and how large the result should be as a percentage of the initial data set (see Figure 1). Practically everything else, barring the initial (very minimal) configuration of each database, is automated, resulting in a highly streamlined process for generating test data. Note that the aforementioned percentage can be set to 100% (useful if, for example, you want to migrate the data set without changing it) or even over it, which allows you to generate a synthetic data set larger than the original data set (which can, at your option, also include the original data set). You can also incorporate SQL statements into this process if you want to, say, add edge cases that may not be covered by your production data.

All that said, the web UI is not the only way to interact with Windocks. It also offers a CLI (Command Line Interface) for use by developers (and other more technical users) as well a restful API.  In particular, it provides an open interface that is accessible to other products, notably the company’s partners, to add additional functionality to the build process. REST APIs are also provided to integrate outgoing images with various CI/CD pipelines, source control, and orchestration systems, such as Jenkins and Azure DevOps.

Database virtualisation is increasingly in-demand within the TDM space, and it’s not difficult to see why: it allows for extremely fast provisioning of entire (virtual) databases for the purposes of testing without needing to worry about whether they are representative (as would be the case for subsets or synthetic data). Moreover, it allows each tester (or developer, for that matter) to rapidly provision their own data sets that they can play around with and modify as they wish without negatively impacting anyone else. The fact that Windocks offers this capability at all is a large point in its favour. What’s more, compared to the other database virtualisation tools we are aware of in the TDM space, it is either significantly more widely applicable or vastly more cost effective.

Containers are also increasingly in-demand, not just in regards to TDM but in general. Moreover, database virtualisation and containers form a natural fit: database clones are usually created, delivered, and torn down repeatedly and en masse, a lifecycle that is well suited to (and enhanced by) deployment as a container. To put a point on it, Docker is able to deliver virtual database instances in less than 30 seconds and supports up to 100 containers per virtual machine. In other word, it helps to maximise both speed and utilisation. It then almost goes without saying that Windocks, a platform that combines these two technologies, should not be dismissed lightly. To add to this, Windocks claims that its customers have been able to reduce lower-level database environment costs by up to 50% by utilising fewer VMs and minimising storage consumption. This stands in contrast to the other vendors in the space that primarily offer database virtualisation as part of a server/client architecture and hence cannot easily reap the benefits of containerisation.

The product’s other capabilities – data subsetting, sensitive data discovery, data masking, and perhaps most of all synthetic data generation – make up the other major features that are often desired in the TDM space, and make Windocks one of the few TDM solutions that can, at least in principle, serve all comers. More than that, its subsetting and synthetic data capabilities are automated to a very impressive degree.

In addition, the product is highly portable and offers an extremely fast time to return. Its web UI is perhaps a little plain, but it is certainly functional, and can be subsumed by other front-ends if necessary.

The bottom line

Windocks is an effective TDM solution that offers several standout features, including database virtualisation, very substantial container support, and highly automated data subsetting and synthetic data generation. In short, it poses a compelling and forward-thinking alternative to many of the players in the space.