There is a whole meta-world beyond strings and numbers

Generating fake PII data is not an easy task: your standard random data generators would usually support strings, numbers, dates, and — the specialized ones— some US-centric data types, such as names, locations, and Social Security Numbers. Detail-orientation is not the forte of mock data applications as they tend to focus on the technical view of the data rather than what that data represents. If you’re a non-US company, or if you cater to customers across the globe, creating data sets for testing that realistically represent your customer base usually translates…

Random Key releases a new API to help devs mock API calls

A sample request and response generated with Random Key’s Mock Data API

Mocking backend REST servers has become the way to go for many organisations to speed up development and testing. Random Key’s Mock Data API is the latest addition to the apps available on the market: what makes our API unique is its data-centric approach.

With Random Key’s Mock API you decide the structure of your responses and which items you wish to randomize. Your schema might include static elements that you wish to keep as they are, and attributes you want to generate with Random Key. Each item marked for randomization is given a custom name, the data type to…

Names, locations, IDs, credit card numbers and more data are a couple of clicks away.

By the end of this article you will have generated a csv file with personal data for the region of your choice, with elements of your choice (names, addresses, social security numbers, credit card numbers, etc.), and the number of records that you need.

Typical requirements dictate that mock data should be realistic, and in some cases, regional: names should resemble names; social security numbers, credit card numbers must follow the standardised convention of their issuing bodies; locations must be geographical, not imaginary.

Random Key was created to do exactly that: the API publishes a variety of endpoints that can…

Postman’s collection radically simplifies random data generation

Random Data API Collection

Coming up with test data sets in your API development that resemble customers’ personal information but are completely fictional may sound like a couple of days worth of work. If you use Postman, the same requirement can be fulfilled within minutes.

A new collection shipped with Postman, called Random Data API pulls together 16 endpoints (as of the moment of writing), i.e. 16 classes of data that will respond to your calls with strings of personal information: realistic-looking, yet fictional. …

Time to get hands-on with the newly released fictional data generators

There is a new cool kid in the API town

Randomkey is a European maker of two APIs: a Random Data Generator and a Test Data Generator. The services support static data masking use cases by producing fictional personal data on demand. The former yields random values per request, while the latter generates random data that is consistent per request to achieve referential integrity.

This how-to is a first look on both APIs. The tutorial covers generating your own authorization key, generating a sample Random Data API request, and translating an input to a random — but unique — output with the Test Data API.


If you’d like to follow…

Creating realistic data sets on demand to improve data security in software development

Foreword: To support our creative community at Glolent we encourage projects like Randomkey and publish relevant and interesting guest articles on our channel. Learn more about us at

Test Data Management has been haunting developers for decades. An integral part of every software development project, test data generation is complicated and almost always, despised. A test data set has to look like a real data set, and at the same time be completely fictitious. Some Mary Boggles based in Birmingham, UK surely sounds like a real person — and she might indeed exist — but as long as nobody…

Organisations are endangering their users’ identity by using production data in software development

Testers are the involuntary gatekeepers to sensitive data in an organisation | Image credit: Adrien Olichon (Unsplash)

Test data management tends to go under radar when data security initiatives are implemented. Test data is key in software development as it serves as an assurance that an application is working as intended. More often than not, production data — so real user’s information — is used for those purposes. It’s technically challenging to create a realistic, but fictional, representation of the original data. A fake data set has to display the same characteristics as the information it is based on: including a syntactic and semantic coherence, language, data point uniqueness, and referential integrity between the data sets. As…

We are the team behind Randomkey, a developer’s toolkit for data privacy.

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