5 min readUpdated Mar 2, 2026

WorkflowWrapper Component Documentation

Overview

The WorkflowWrapper component is a crucial part of the Vantage analytics and data platform, designed to allow users to create, edit, and manage workflows visually. It integrates various elements such as node libraries, workflow editors, and property panels to facilitate a comprehensive workflow-building experience. This component also includes functionality for saving workflows, managing state, and handling service selections within the context of data analytics.

Purpose

The main purpose of the WorkflowWrapper component is to provide users with an interactive interface to build data workflows efficiently. It integrates various supporting components, manages the workflow's state, and interacts with backend services for functionality like loading and deleting workflows.

How It Works

  1. State Management: The component manages the state of the workflow, including nodes, edges, and selected nodes.
  2. Responsive Design: The component adapts its layout based on the screen size, transitioning to a compact mode for smaller displays to enhance usability.
  3. Service Management: Users can select services that the workflow will interact with, allowing for dynamic configurations based on available integrations.
  4. Node and Edge Management: Users can add nodes and edges to their workflows, modify their properties, and see real-time updates in the UI.
  5. Integrations: It is designed to integrate with various AI services and external data sources through dynamic service management capabilities.
  6. Workflow Persistence: Functions for saving and loading workflows back to the server are included, ensuring that users can maintain their projects without data loss.

Settings

1. registryEndpoint

2. title

3. description

4. apiBasePath

5. initialWorkflowId

6. isCompact

7. libraryCollapsed

8. propertiesCollapsed

9. services

10. selectedServiceId

Use Cases & Examples

Use Case 1: Automated Data Processing Workflow

In a scenario where a company needs to automate data processing tasks between various databases and data analytics tools, the WorkflowWrapper can be used to visually connect nodes representing each database and processing steps in a single workflow.

Use Case 2: ETL Pipeline Creation

When creating an ETL (Extract, Transform, Load) pipeline, users can leverage the WorkflowWrapper to design the flow of data from the source to the destination, adding transformations as required.

Example Configuration for ETL Pipeline

To set up an ETL pipeline using the WorkflowWrapper, users might configure the following:

The user would then add nodes corresponding to the MySQL source, transformation functions (like normalization), and the data warehouse destination, connecting them through edges to visualize the flow of data. This configuration would allow them to save the workflow and manage it for future use.

By properly adjusting these settings and adding the relevant nodes and edges, users can effectively handle the complexities of data transformations visually.