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Manual Trigger Data Key Renaming Workflow

This workflow automatically renames specified key names in a set of initial data through a manual trigger function, helping users quickly achieve data field conversion and standardization. It is suitable for use in scenarios such as development debugging and data preprocessing, effectively addressing the issue of inconsistent field naming. This reduces the tediousness of manual modifications, enhances the efficiency and accuracy of data organization, and facilitates the use of subsequent processes.

Workflow Diagram
Manual Trigger Data Key Renaming Workflow Workflow diagram

Workflow Name

Manual Trigger Data Key Renaming Workflow

Key Features and Highlights

This workflow is executed via manual trigger, starting with setting an initial set of data key-value pairs, followed by automatically renaming specified key names. The process is streamlined and efficient, ideal for quickly transforming and standardizing data fields.

Core Problem Addressed

It resolves issues related to inconsistent or non-standardized field naming during data processing, eliminating the complexity and error risks of manual data structure modifications, thereby enhancing automation and accuracy in data organization.

Application Scenarios

  • Rapid adjustment of test data field names during development and debugging
  • Standardizing fields of incoming data during data preprocessing
  • Simple data transformation tasks to facilitate the use of unified field names in subsequent workflows
  • Scenarios requiring manual trigger execution for flexible control over the timing of data transformation

Main Workflow Steps

  1. Manual Trigger: User initiates the workflow by clicking the "Execute" button
  2. Set Data: Define the initial key-value pair data (e.g., key=somevalue)
  3. Rename Keys: Rename the "key" field in the data to "newkey"

Involved Systems or Services

  • Built-in n8n nodes: Manual Trigger, Set, Rename Keys
    No external system integration; purely local data processing.

Target Users and Value

  • Data handlers, developers, and automation workflow designers who need to quickly adjust data structures
  • Suitable for business scenarios requiring flexible control over data transformation timing
  • Simplifies data field management, improving efficiency and accuracy in subsequent data processing and integration