Mock Data Splitting Workflow
This workflow is mainly used for generating and splitting simulated user data, facilitating subsequent processing. By using custom function nodes, it creates an array containing multiple user information entries and splits them into independent JSON data items. This process addresses the flexibility issues in batch data processing, making it suitable for scenarios such as test data generation, individual operations, and quickly building demonstration data, thereby enhancing the efficiency and controllability of workflow design.
Workflow Name
Mock Data Splitting Workflow
Key Features and Highlights
This workflow generates a set of mock user data (including IDs and names) and splits it into individual JSON data items, facilitating subsequent item-by-item processing or transmission. Its highlight lies in leveraging custom function nodes to flexibly generate and transform data, making it ideal for rapid test data creation or workflow demonstration.
Core Problem Addressed
It solves the challenge of batch data processing in automation workflows when the initial data is a consolidated array. By splitting the array into single JSON items that can be processed individually, it enhances the flexibility and controllability of data handling.
Use Cases
- Generating test data during automation workflow development
- Business scenarios requiring item-by-item operations on batch data
- Quickly building data inputs for workflow demonstration and debugging
Main Workflow Steps
- Mock Data Node: Generates an array of mock user information using a custom function.
- Create JSON-items Node: Splits the mock data array into individual JSON data items, outputting them as separate workflow data units.
Systems or Services Involved
This workflow is implemented solely with n8n’s built-in Function nodes, without involving any external systems or third-party services, enabling quick deployment and easy modification.
Target Users and Value
Suitable for automation developers, testers, and product managers, this workflow facilitates rapid construction and splitting of data models, lowers the barrier for development and debugging, and improves the flexibility and efficiency of workflow design.