DataCrate Module
DataCrate is RabbitQA's test data platform. Upload the files you already have, generate realistic synthetic data from them, stand up mock services for the systems you do not control, and keep every generated artifact in one repository.
What is DataCrate?
DataCrate combines four things that testing teams usually keep in separate tools:
- A dataset library for the CSV, Excel, and JSON files your tests depend on.
- A generation engine that produces new rows from reusable patterns, value libraries, AI, or the distribution of your own data.
- A mock service runtime that serves REST and SOAP endpoints derived from specs, datasets, or an AI prompt.
- A repository where every generated artifact is stored, organized, and downloadable.
Key Capabilities
- Uploaded File Management — Upload datasets, add new versions, and clean, filter, subset, or invert the data
- Smart Data Generation — Build tables column by column, enlarge an existing dataset, or generate a single value on demand
- Method-Aware Columns — Choose per column between AI, reusable patterns, value libraries, distribution-preserving generation, or randomized resampling
- Mock Services — Create mock endpoints from an OpenAPI, WSDL, or gRPC source, from a dataset, or from a prompt
- Dynamic Forms — Define form schemas, bind their fields to dataset columns, and submit rows against a mock service
- Data Sessions — Watch generation runs progress, follow their logs and events, and open their outputs
- Data Repository — Browse folders, templates, and generated artifacts in one tree
Module Map
| Page | What you do there |
|---|---|
| Dashboard | See activity totals, recent sessions, and shortcuts |
| Uploaded Files | Upload and manage source datasets and their versions |
| Smart Data Generation | Configure and start generation runs |
| Mock Services | Create, run, and publish mock endpoints |
| Dynamic Forms | Build form schemas and submit data through them |
| Data Sessions | Monitor runs and inspect their results |
| Data Repository | Organize and download generated artifacts |
| Settings | Manage saved values used during generation |
Integration with Other Modules
| Module | Integration |
|---|---|
| AutoRunner | Import from DataCrate pulls a generated artifact into an AutoRunner environment or variable set |
Getting Started
- Upload a dataset — Follow Uploaded Files to bring in your first CSV, Excel, or JSON file.
- Generate data — Use Smart Data Generation to build a table or enlarge what you uploaded.
- Watch the run — Track progress in Data Sessions and preview the output when it completes.
- Collect the output — Find and download the artifact in the Data Repository.
tip
Start from an uploaded file rather than an empty table. DataCrate reads its columns and inferred types automatically, which gives the generation wizard a much better starting point than a schema typed from scratch.
