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, enabling users to perform a wide range of operations on Dataiku DSS resources directly from n8n workflows. Specifically, for the Project Folder resource and the Create Sub Project Folder operation, it allows creating a sub-project folder within an existing project folder in Dataiku DSS.
Common scenarios where this node is beneficial include:
- Automating project organization by programmatically creating nested folders.
- Structuring projects dynamically based on workflow logic or external triggers.
- Managing large projects with complex folder hierarchies without manual intervention.
Practical example:
- Automatically create a new sub-folder inside a specified project folder when a new project phase starts, helping maintain organized project structures.
Properties
| Name | Meaning |
|---|---|
| Folder ID | The identifier of the parent project folder under which the new sub-project folder will be created. |
| Query Parameters | Optional additional parameters as key-value pairs to customize the API request. |
| Sub-Folder Name (name) | The name of the new sub-project folder to create inside the specified Folder ID. |
Note: The "Sub-Folder Name" property is part of the query parameters collection and must be provided to specify the new folder's name.
Output
The node outputs the JSON response returned by the Dataiku DSS API after creating the sub-project folder. This typically includes details about the newly created folder such as its ID, name, and metadata.
If the API returns binary data (not typical for this operation), the node would output it as binary data prepared for further use in the workflow.
Dependencies
- Requires an active connection to a Dataiku DSS instance.
- Needs valid API credentials (an API key token) configured in n8n to authenticate requests.
- The node uses HTTP requests to interact with the Dataiku DSS REST API.
Troubleshooting
- Missing Credentials Error: If the API credentials are not set or invalid, the node throws an error indicating missing credentials. Ensure that the API key credential is properly configured.
- Folder ID Required: The operation requires a valid Folder ID; if omitted, the node will throw an error. Verify that the Folder ID input is correctly provided.
- API Errors: Any errors returned by the Dataiku DSS API (e.g., permission denied, invalid folder name) will be surfaced as node errors. Check the error message for details and verify permissions and input values.
- Network Issues: Connectivity problems to the Dataiku DSS server will cause request failures. Confirm network access and server availability.
Links and References
- Dataiku DSS API Documentation - Project Folders
- Dataiku DSS REST API Reference
- n8n Documentation - Creating Custom Nodes
This summary focuses on the "Project Folder" resource and the "Create Sub Project Folder" operation as requested, based on static analysis of the provided source code and properties.