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 a wide range of operations on various Dataiku DSS resources. Specifically for the Workspace resource and the Create Workspace operation, it enables creating new workspaces within Dataiku DSS.
Common scenarios where this node is beneficial include automating workspace management in Dataiku DSS projects, such as programmatically creating workspaces to organize datasets, applications, or HTML links relevant to a project or team.
For example, you might use this node to:
- Automatically create a new workspace when a new project is initialized.
- Organize different data science assets by creating dedicated workspaces via automation workflows.
- Integrate workspace creation into larger CI/CD pipelines managing Dataiku DSS projects.
Properties
| Name | Meaning |
|---|---|
| Request Body | JSON object representing the details of the workspace to create. This should conform to the expected API schema for workspace creation in Dataiku DSS. |
The Request Body property is a JSON input where you specify the parameters and configuration for the new workspace. It typically includes fields like workspace name, description, and other settings as required by the Dataiku DSS API.
Output
The node outputs the response from the Dataiku DSS API call in the json field of the output item. For the "Create Workspace" operation, this will generally be a JSON object representing the newly created workspace, including its unique identifier and any metadata returned by the API.
If the API returns binary data (not typical for workspace creation), it would be provided in the binary field, but this operation primarily deals with JSON responses.
Dependencies
- Requires an active connection to a Dataiku DSS instance.
- Requires valid API credentials (an API key) for authenticating requests to the Dataiku DSS API.
- The node expects the Dataiku DSS server URL and user API key to be configured in the credentials.
Troubleshooting
- Missing Credentials Error: If the node throws an error about missing credentials, ensure that the Dataiku DSS API credentials are properly set up in n8n.
- Required Parameters Missing: The node validates required parameters such as workspace key or request body content. Make sure all mandatory fields are provided.
- API Errors: Errors returned from the Dataiku DSS API will be surfaced as node errors. Check the error message for details, which may indicate issues like invalid JSON in the request body or permission problems.
- Network Issues: Ensure that the n8n instance can reach the Dataiku DSS server URL specified in the credentials.
Links and References
- Dataiku DSS API Documentation - Workspaces (official API docs for workspace management)
- Dataiku DSS User Guide (general documentation for Dataiku DSS)
This summary focuses on the "Workspace" resource and the "Create Workspace" operation as requested, based on static analysis of the provided source code and properties.