The iSynth approach:
treat data as code
Set up the object types
In iSynth, data is described in code. This starts with the object types that form an object-oriented data model.
The key here is to deliberately keep the object types application-neutral and simple, and to focus only on business-relevant aspects. This enables good maintainability over time.
Create data constellations and define data variance
Data constellations are built on top of the data model by combining object types like Lego® bricks into an object graph. In doing so, we draw inspiration from archetypical data cases from the real world.
And since we also describe constellations as code, arbitrarily complex entities can be built algorithmically.
Data configs are a powerful instrument to define precise data variance for your specific test case. They rely on data constellations and allow easy modification of the entire object graph, down to the attribute level.
Synthesize data
Synthesizing data feels like placing an order in a web shop: just select from available data constellations, define the quantity you need and run the data order.
Now all the magic happens in the background: synthetic data is generated in the variance and quantity you defined. It doesn't matter whether you need small, precise data sets for development or functional testing, or if very large volumes are required for performance engineering.
iSynth allows short feedback loops by browsing the generated data records and by visualizing them again as object graph.
Provisioning workflows and pipeline integration
Once synthetic data has been generated, it has to be loaded into the target applications. This is covered by iSynth’s provisioning workflows that orchestrate the execution of application-specific export scripts.
These export scripts transform synthetic data into application-specific formats and utilize the APIs provided by the target applications to load the data.
iSynth also offers a powerful REST API to fully automate the process. This allows easy integration into CI/CD pipelines or job schedulers.
Learn more about iSynth
Do you still have questions? We love to tell you more about the iSynth approach and how you can improve your process with synthetic data.