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Nappai Documentation

Data Conditional Router

The Data Conditional Router is a component designed to direct data objects based on specific conditions applied to a key within those objects. It helps in sorting data into two categories: those that meet the condition and those that do not.

Relationship with Nappai Automation System

The Data Conditional Router is part of the Nappai automation system, which allows users to automate tasks and processes in their data management systems. This component specifically helps in routing data based on conditions, making it easier to manage and process data efficiently within the Nappai environment.

Inputs

  • Data Input: The data object or list of data objects that you want to process.
  • Key Name: The specific key in the data object(s) that you want to check.
  • Comparison Operator: The operator used to compare values, such as “equals” or “contains”.
  • Compare Value: The value against which the data is compared.

Outputs

The component produces two outputs:

  • True Output: Contains data objects that meet the specified condition.
  • False Output: Contains data objects that do not meet the specified condition.

These outputs can be used in workflows to further process or analyze data based on whether they meet the condition.

Usage Example

Imagine you have a list of customer orders, and you want to separate orders that have been marked as “urgent”. You can use the Data Conditional Router to check the “priority” key in each order. Orders with “priority” set to “urgent” will be routed to the True Output, while others will go to the False Output.

Templates

Currently, there are no specific templates where this component is pre-configured. However, it can be easily integrated into any workflow within the Nappai system.

  • Filter Data: Filters data objects based on a list of keys.
  • Extract Key: Extracts specific keys from data objects.
  • Merge Data: Combines multiple data objects into a unified list.

Tips and Best Practices

  • Ensure that the key name you specify exists in all data objects to avoid errors.
  • Use the appropriate comparison operator to get accurate results.
  • Test the component with a small dataset to ensure it behaves as expected before scaling up.

Security Considerations

When using this component, ensure that the data being processed does not contain sensitive information unless necessary. Always follow best practices for data privacy and security.