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 Dataiku DSS resources programmatically. It is designed to interact with various Dataiku DSS entities such as projects, bundles, datasets, models, scenarios, and more.
For the Project resource with the Create Project operation, the node enables creating new projects within Dataiku DSS by sending appropriate API requests. This is useful for automating project setup workflows, integrating project creation into larger automation pipelines, or managing multiple projects dynamically.
Practical examples:
- Automatically create a new Dataiku DSS project when a new client onboarding process starts.
- Integrate project creation into CI/CD pipelines to prepare environments for data science teams.
- Bulk-create projects based on external triggers or schedules.
Properties
| Name | Meaning |
|---|---|
| Query Parameters | A collection of optional query parameters that can be added to the API request URL. These include many possible parameters like active, name, limit, tags, folderId, etc., which customize the API call behavior. |
| Request Body | JSON object representing the body of the HTTP request. For creating a project, this would contain the project details and configuration in JSON format. |
| projectKey | The unique key identifier for the project. Required for most project-related operations. |
Note: The full list of query parameters includes many options (e.g., active, allUsers, archivePath, catalogName, deletionMode, exportAnalysisModels, filter, folderId, limit, name, page, tags, wait, and many others) that control various aspects of the API calls across different resources and operations.
Output
The node outputs an array of items where each item contains:
- json: The parsed JSON response from the Dataiku DSS API corresponding to the requested operation. This typically includes the created project's metadata and status information when creating a project.
- binary (optional): For some operations involving downloads (not applicable to project creation), binary data such as files or archives may be returned.
For the Create Project operation specifically, the output will be the JSON representation of the newly created project as returned by the Dataiku DSS API.
Dependencies
- Requires an active connection to a Dataiku DSS instance.
- Requires valid API credentials (an API key credential) for authentication with the Dataiku DSS API.
- The node uses HTTP requests to communicate with the Dataiku DSS REST API endpoints.
- No additional external libraries beyond those bundled with n8n are required.
Troubleshooting
- Missing Credentials Error: If the API credentials are not provided or invalid, the node throws an error indicating missing Dataiku DSS API credentials. Ensure you have configured the API key credential correctly.
- Required Parameter Missing: The node validates required parameters such as
projectKeyfor project operations. If these are missing, it throws descriptive errors. Make sure all mandatory fields are filled. - API Request Failures: Errors from the Dataiku DSS API (e.g., network issues, permission denied, invalid payload) are caught and reported with messages prefixed by "Error calling Dataiku DSS API". Check your API endpoint URL, credentials, and request body.
- Invalid JSON in Request Body: The
requestBodyproperty must be valid JSON. Invalid JSON will cause parsing errors. - Binary Data Handling: For operations returning files, ensure the node is configured to handle binary data properly.
Links and References
- Dataiku DSS API Documentation
- Dataiku DSS Projects API Reference
- n8n Documentation - Creating Custom Nodes
This summary focuses on the Project resource and the Create Project operation, describing how the node constructs the API request, handles inputs, and processes outputs accordingly.