Actions364
- Continuous Activity Actions
- Dataset Actions
- Get Last Metric Values
- Get Metadata
- Get Schema
- Get Single Metric History
- List Datasets
- List Partitions
- Compute Metrics
- Create Dataset
- Create Managed Dataset
- Delete Data
- Delete Dataset
- Execute Tables Import
- Get Column Lineage
- Get Data
- Get Data - Alternative Version
- Get Dataset Settings
- Get Full Info
- List Tables
- List Tables Schemas
- Prepare Tables Import
- Run Checks
- Set Metadata
- Set Schema
- Synchronize Hive Metastore
- Update Dataset Settings
- Update From Hive Metastore
- API Service Actions
- Bundles Automation-Side Actions
- Bundles Design-Side Actions
- Connection Actions
- Dashboard Actions
- Data Collection Actions
- Data Quality Actions
- Compute Rules on Specific Partition
- Create Data Quality Rules Configuration
- Delete Rule
- Get Data Quality Project Current Status
- Get Data Quality Project Timeline
- Get Data Quality Rules Configuration
- Get Dataset Current Status
- Get Dataset Current Status per Partition
- Get Last Outcome on Specific Partition
- Get Last Rule Results
- Get Rule History
- Update Rule Configuration
- DSS Administration Actions
- Job Actions
- Library Actions
- Dataset Statistic Actions
- Discussion Actions
- Flow Documentation Actions
- Insight Actions
- Internal Metric Actions
- LLM Mesh Actions
- Machine Learning - Lab Actions
- Delete Visual Analysis
- Deploy Trained Model to Flow
- Download Model Documentation of Trained Model
- Generate Model Documentation From Custom Template
- Start Training ML Task
- Update User Metadata for Trained Model
- Update Visual Analysis
- Adjust Forecasting Parameters and Algorithm
- Compute Partial Dependencies of Trained Model
- Compute Subpopulation Analysis of Trained Model
- Create ML Task
- Create Visual Analysis
- Create Visual Analysis and ML Task
- Generate Model Documentation From Default Template
- Generate Model Documentation From File Template
- Get ML Task Settings
- Get ML Task Status
- Get Model Snippet
- Get Partial Dependencies of Trained Model
- Get Scoring Jar of Trained Model
- Get Scoring PMML of Trained Model
- Get Subpopulation Analysis of Trained Model
- Get Trained Model Details
- Get Visual Analysis
- List ML Tasks of Project
- List ML Tasks of Visual Analyses
- List Visual Analyses
- Reguess ML Task
- Machine Learning - Saved Model Actions
- Compute Partial Dependencies of Version
- Get Version Scoring PMML
- Get Version Snippet
- Import MLflow Version From File or Path
- List Saved Models
- List Versions
- Set Version Active
- Compute Subpopulation Analysis of Version
- Create Saved Model
- Delete Version
- Download Model Documentation of Version
- Evaluate MLflow Model Version
- Generate Model Documentation From Custom Template
- Generate Model Documentation From Default Template
- Generate Model Documentation From File Template
- Get MLflow Model Version Metadata
- Get Partial Dependencies of Version
- Get Saved Model
- Get Subpopulation Analysis of Version
- Get Version Details
- Get Version Scoring Jar
- Set Version User Meta
- Update Saved Model
- Long Task Actions
- Machine Learning - Experiment Tracking Actions
- Macro Actions
- Plugin Actions
- Download Plugin
- Fetch From Git Remote
- Get File Detail From Plugin
- Get Git Remote Info
- Get Plugin Settings
- Install Plugin From Git
- Install Plugin From Store
- List Files in Plugin
- List Git Branches
- List Plugin Usages
- Move File or Folder in Plugin
- Add Folder to Plugin
- Create Development Plugin
- Create Plugin Code Env
- Delete File From Plugin
- Delete Git Remote Info
- Delete Plugin
- Download File From Plugin
- Move Plugin to Dev Environment
- Pull From Git Remote
- Push to Git Remote
- Rename File or Folder in Plugin
- Reset to Local Head State
- Reset to Remote Head State
- Set Git Remote Info
- Set Plugin Settings
- Update Plugin Code Env
- Update Plugin From Git
- Update Plugin From Store
- Update Plugin From Zip Archive
- Upload File to Plugin
- Upload Plugin
- Project Deployer Actions
- Get Deployment Settings
- Get Deployment Status
- Create Deployment
- Create Infra
- Create Project
- Delete Bundle
- Delete Deployment
- Delete Infra
- Delete Project
- Get Deployment
- Get Deployment Governance Status
- Get Infra
- Get Infra Settings
- Get Project
- Get Project Settings
- Save Deployment Settings
- Save Infra Settings
- Save Project Settings
- Update Deployment
- Upload Bundle
- SQL Query Actions
- Wiki Actions
- Managed Folder Actions
- Meaning Actions
- Model Comparison Actions
- Notebook Actions
- Project Actions
- Project Folder Actions
- Recipe Actions
- Scenario Actions
- Security Actions
- Streaming Endpoint Actions
- Webapp Actions
- Workspace Actions
Overview
This node integrates with the Dataiku DSS API, allowing users to perform various operations on Dataiku DSS resources. Specifically for the Managed Folder resource and the Delete Managed Folder operation, the node enables deleting a managed folder within a specified project in Dataiku DSS.
Typical use cases include automating the cleanup or removal of managed folders as part of data lifecycle management or project maintenance workflows. For example, after archiving or migrating data, you might want to delete obsolete managed folders programmatically.
Properties
| Name | Meaning |
|---|---|
| Project Key | The key identifier of the Dataiku DSS project containing the managed folder. |
| Folder ID | The unique identifier of the managed folder to be deleted within the specified project. |
Output
The output is a JSON array where each element corresponds to the response from the Dataiku DSS API for the executed request.
- For the Delete Managed Folder operation, the API typically returns no content (HTTP 204) upon successful deletion.
- In such cases, the node outputs an object with a
"Status Code": "204 No Content"field indicating success. - If the API returns any JSON response, it will be parsed and returned as JSON.
- The node does not output binary data for this operation.
Dependencies
- Requires an active connection to a Dataiku DSS instance.
- Requires an API authentication token credential for Dataiku DSS (referred generically as "an API key credential").
- The node expects the Dataiku DSS server URL and user API key to be configured in the credentials.
- No additional external dependencies are required.
Troubleshooting
- Missing Credentials Error: If the node throws "Missing Dataiku DSS API Credentials," ensure that the API key credential is properly set up and selected.
- Required Parameter Errors: The node validates required parameters like Project Key and Folder ID. Missing these will cause errors such as "Project Key is required" or "Folder ID is required." Make sure these inputs are provided.
- API Request Failures: Network issues, incorrect server URLs, or invalid API keys can cause request failures. Verify connectivity and credentials.
- Unexpected Response Format: If the API returns unexpected data, the node attempts to parse JSON; if parsing fails, raw text is returned. Check the API response for correctness.
- Permission Issues: Ensure the API key has sufficient permissions to delete managed folders in the target project.
Links and References
- Dataiku DSS API Documentation - Managed Folders
- Dataiku DSS Official Website
- n8n Documentation (for general node usage and credential setup)
This summary focuses on the Managed Folder resource and the Delete Managed Folder operation as requested.