Actions364
- API Service Actions
- Bundles Automation-Side Actions
- Bundles Design-Side Actions
- Connection Actions
- Continuous Activity 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
- Dataset Actions
- 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
- Get Last Metric Values
- Get Metadata
- Get Schema
- Get Single Metric History
- List Datasets
- List Partitions
- List Tables
- List Tables Schemas
- Prepare Tables Import
- Run Checks
- Set Metadata
- Set Schema
- Synchronize Hive Metastore
- Update Dataset Settings
- Update From Hive Metastore
- Dataset Statistic Actions
- Discussion Actions
- DSS Administration Actions
- Flow Documentation Actions
- Insight Actions
- Internal Metric Actions
- Job Actions
- Library Actions
- LLM Mesh Actions
- Long Task Actions
- Machine Learning - Experiment Tracking Actions
- Machine Learning - Lab Actions
- 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
- Delete Visual Analysis
- Deploy Trained Model to Flow
- Download Model Documentation of Trained Model
- Generate Model Documentation From Custom Template
- 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
- Start Training ML Task
- Update User Metadata for Trained Model
- Update Visual Analysis
- Machine Learning - Saved Model Actions
- Compute Partial Dependencies of Version
- 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
- Get Version Scoring PMML
- Get Version Snippet
- Import MLflow Version From File or Path
- List Saved Models
- List Versions
- Set Version Active
- Set Version User Meta
- Update Saved Model
- Macro Actions
- Managed Folder Actions
- Meaning Actions
- Model Comparison Actions
- Notebook Actions
- Plugin Actions
- 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
- 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
- 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 Actions
- Project Deployer Actions
- Create Deployment
- Create Infra
- Create Project
- Delete Bundle
- Delete Deployment
- Delete Infra
- Delete Project
- Get Deployment
- Get Deployment Governance Status
- Get Deployment Settings
- Get Deployment Status
- Get Infra
- Get Infra Settings
- Get Project
- Get Project Settings
- Save Deployment Settings
- Save Infra Settings
- Save Project Settings
- Update Deployment
- Upload Bundle
- Project Folder Actions
- Recipe Actions
- Scenario Actions
- Security Actions
- SQL Query Actions
- Streaming Endpoint Actions
- Webapp Actions
- Wiki Actions
- Workspace Actions
Project Deployer → Create Infra
AI-generatedOverview
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. It supports managing projects, deployments, bundles, datasets, dashboards, machine learning models, scenarios, and many other Dataiku DSS entities.
For the Project Deployer resource with the Create Infra operation, the node allows creating an automation infrastructure on the Dataiku Project Deployer. This is useful for automating deployment environments in Dataiku DSS, facilitating continuous integration and delivery pipelines.
Typical use cases include:
- Automating the creation of deployment infrastructures for Dataiku projects.
- Integrating Dataiku project deployment steps into broader workflow automations.
- Managing Dataiku DSS resources programmatically without manual intervention.
Properties
| Name | Meaning |
|---|---|
| Request Body | JSON object containing the request payload to create the automation infrastructure. |
The Request Body property is a JSON input where you specify the details required by the Dataiku API to create the infrastructure. The exact structure depends on the Dataiku Project Deployer API specification for infra creation.
Output
The node outputs the response from the Dataiku DSS API call in the json field of the output data. This typically contains the details of the created infrastructure or any relevant status information returned by the API.
If the API returns binary data (not typical for this operation), it would be available in the binary field, but for "Create Infra" operation, the output is expected to be JSON.
Dependencies
- Requires valid Dataiku DSS API credentials, including:
- The URL of the Dataiku DSS server.
- A user API key for authentication.
- The node uses HTTP requests to communicate with the Dataiku DSS REST API.
- No additional external services are required beyond access to the Dataiku DSS instance.
Troubleshooting
- Missing Credentials Error: If the node throws an error about missing credentials, ensure that the Dataiku DSS API credentials are properly configured in n8n.
- Required Parameter Errors: The node validates required parameters such as project keys, deployment IDs, and others depending on the operation. Missing these will cause errors. For "Create Infra", ensure the request body is correctly provided.
- API Errors: Errors returned by the Dataiku API will be surfaced with messages like "Error calling Dataiku DSS API". Check the message and stack trace for details. Common issues include invalid JSON in the request body or insufficient permissions.
- Network Issues: Ensure that the n8n instance can reach the Dataiku DSS server URL and that firewall or network policies allow communication.
Links and References
- Dataiku DSS API Documentation – Official API docs for understanding request bodies and endpoints.
- Dataiku Project Deployer API – Specific documentation for project deployer infra creation and management.
This summary focuses on the "Project Deployer" resource and the "Create Infra" operation, describing how the node constructs the API request using the provided JSON request body and handles the response accordingly.