GenRocket

Generating synthetic data in GenRocket requires you to create a model for your test data, consisting of domains, attributes, generators, receivers and scenarios. Domains are roughly equivalent to database tables: you might have a ‘user’ domain, for example. Domains have attributes, which are similarly analogous to columns. In the aforementioned example, the user domain might have attributes for first name, last name, date of birth, and so on.

Fig 1 GenRocket Project Dashboard

Each attribute is equipped with one or more generators, which are methods for generating synthetic data. 222 generators are provided out of the box across a wide variety of categories. Some of these are straightforward, such as NameGen, which generates names. Others are more complicated, but also more powerful, such as ConcatGen, which concatenates the values of other attributes; SwitchGen, which acts as a switch statement; and EdgeCaseGen, which generates and inserts edge cases into your test data. All have parameters that allow for data customisation: for example, NameGen can be configured to generate any combination of first, last, male or female names. Notably, GenRocket provides several generators specifically to support machine learning via the creation of training data. It also has the ability to blend production data with synthetic data, as well as generate data feed data.

Generators can be linked together on an ad hoc basis, allowing you to chain the output from one generator into the next. Any number of generators can be linked in this fashion, and sequences of linked generators can be saved as ‘presets’. When you first create an attribute, an appropriate generator is automatically assigned to it, if one is available. Furthermore, many generators are designed to create data that is internally consistent. For example, if you have an ‘address’ domain with attributes for street, city, state, and so on, the generators for each of those attributes will communicate automatically in order to create addresses which could actually exist – you might get addresses in ‘Boston, Massachusetts’, but never in ‘Boston, Hawaii’.

In addition to attributes, each domain has access to a variety of different receivers which determine the output format of your test data. For example, if a domain has an XML receiver then test data generated from it will be outputted as an XML file. Receivers exist for more than 44 different output formats, including CSV, JSON, SQL, REST, SOAP and the aforementioned XML, and you can generate test data in several different formats simultaneously by attaching multiple receivers to a given domain.

Finally, scenarios are sets of instructions for generating your test data, created by combining all of the above information into a single specification. You can configure the quantity of test data to create, and also include multiple domains in a single scenario chain. Moreover, you can automatically include multiple domains by creating relationships between them – either parent-child or sibling-sibling – which also guarantees referential integrity between those domains. Once you have created your scenario, it can be used (and reused) to create test data either centrally or on a local machine. The former is assisted by the GenRocket Multi User Server (GMUS), which allows large volumes of users to generate synthetic data centrally and simultaneously. In addition, scenarios can be modified in real time using the GenRocket API (perhaps as part of a dynamic testing workflow).

While you can create your model manually, this could become laborious, especially for large systems. Fortunately, GenRocket provides a feature called XTS (Extract Table Schema) that will scan a database and automatically build out an appropriate test data model, including domains, attributes, generators and scenarios. Relationships between domains are established via a wizard, and you can proceed to either use or customise the generated model as you require. Similarly, GenRocket can automatically generate synthetic data for documents by leveraging a relevant document’s XSD file.

GenRocket’s most important advantage is its position in the market. Most synthetic data offerings come as one part of a broad test data management platform, which due to reasons of expense and time to implement is often far from ideal if you are only interested in synthetic data generation. GenRocket’s small size and laser focus on synthetic data means that it avoids this problem. Likewise, if you have a traditional test data management solution which does not offer synthetic data generation, adding GenRocket to your existing solution will likely be far easier than moving to a new platform. This is enhanced by the range of integration capabilities offered by GenRocket, including the APIs it exposes and the variety of output formats it offers.

GenRocket also boasts some significant synthetic data capabilities that few other products share, including parent-child-sibling referential integrity (for which it holds a patent) and distributable and reusable test data generation (via scenarios) that can be performed either centrally or on a local machine. A notable omission is a lack of data profiling, which may limit your ability to create datasets that are representative of your production data. On the other hand, representative data can sometimes be a false ideal: for example, you will still want to test edge cases even if your production data doesn’t contain any.

The Bottom Line

Whether you need a traditional data subsetting solution, a synthetic data solution, or both, will depend on your circumstances and requirements. However, if you are interested in synthetic data, you should certainly be considering GenRocket.

Fig 01 - GenRocket Project Dashboard

Synthetic data in GenRocket is generated using a model that consists of “domains”, “attributes”, “generators”, “receivers” and “scenarios”. Domains contain attributes, which are roughly equivalent to tables and columns respectively, and attributes are in turn equipped with one or more generators: methods for generating your synthetic data.

More than 650 generators are provided out of the box, many of which are customisable via built-in parameters. Notably, GenRocket provides several generators specifically to support machine learning via the creation of training data. It also has the ability to blend production data with synthetic data, as well as to generate synthetic data feeds. Generators can be linked together on an ad hoc basis, always create internally consistent data sets, and can even be assigned to your attributes automatically.

Each of your domains also has access to a variety of receivers, which determine the output format of your test data. Receivers exist for upwards of 70 different output formats, including such favourites as CSV, JSON, SQL, REST, SOAP and XML, and you can generate test data in several different formats simultaneously by attaching multiple receivers to a given domain.

Meanwhile, scenarios are sets of (configurable) instructions for generating your test data, created by combining domains, attributes, generators and receivers into a single specification. You can include multiple domains manually, or specify relationships that will allow GenRocket to bring in families of domains automatically. This guarantees referential integrity between those domains. You can also leverage rules-based validation to control the data (and thus the overarching structure of the data set) generated by your scenarios.

Scenarios can be grouped together into user stories, which can in turn be grouped into epics (a la Agile), all of which can be used (and reused) to repeatedly create test data via self-service, in real time, and on an essentially ad hoc basis. They can be executed centrally, facilitated by the GenRocket Multi User Server (GMUS) that allows large volumes of users to generate synthetic data simultaneously, but are also highly portable, and can be executed more or less anywhere (including on your local machine) via APIs or just by running the scenario file. Scenarios can even be modified in real time if necessary (perhaps as part of a dynamic testing workflow).

GenRocket also provides an XTS (Extract Table Schema) feature that will scan your database schema and automatically build out a test data model, including domains, attributes, generators and scenarios. Relationships between domains are established via a wizard, and you can proceed to either use the generated model as-is or customise it as you require. Similarly, GenRocket can automatically generate synthetic data for documents by leveraging a relevant XSD file.

Fig 02 - GenRocket architecture and use cases

GenRocket is an enterprise-ready, cost-effective point solution for test data management and automation that is well-equipped to handle use cases centered around synthetic data. It aims to be highly automated, to integrate with your existing pipelines, and to work almost behind the scenes to supply you with appropriate test data at the right place and at the right time. This is a laudable aim, and for the most part it achieves it.

The product offers a highly distributable, reusable, lightweight and portable system for generating synthetic data that is easily accessible and available wherever and whenever you need it. Compared to more traditional, monolithic test data management offerings, it is frequently leaner, easier to use, and – at least in terms of synthetic data – more sophisticated.

GenRocket is unique in that it takes an entirely synthetic approach to test data that aims to remove production data from your testing lifecycle completely. Under this paradigm, your synthetic data is not derived from your production data, but instead directly from your business requirements. This is no bad thing, since it means that your test data will be precisely, and reliably, what you need it to be at any given time: you do not need to fall victim to the whims of your production data. In short, GenRocket can leverage production data – for example, it can blend it into your synthetic data – but it does not require it. This makes GenRocket an ideal solution if you want to divest your testing processes of production data and instead rely 100% on the synthetic.

GenRocket does lack some functionality common to its competitors – data profiling, for instance – and its data subsetting and masking functions are fine yet hardly exceptional. But at the end of the day, these capabilities are largely irrelevant to GenRocket’s preferred mode of use. That said, if you want access to them regardless, GenRocket works excellently to augment other testing platforms with synthetic data: it’s lightweight, cost-effective, and offers a range of integration capabilities. Its recent partnership with Delphix is particularly noteworthy in this regard.

The Bottom Line

GenRocket is a fit-for-purpose test data platform that enables you to generate sophisticated synthetic data anytime and anywhere. It should be especially appealing if you want to remove production data from your testing process entirely.