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 interacts with the Dataiku DSS API to perform various operations related to project deployment infrastructure management, among many other resources. Specifically for the Project Deployer resource and the Delete Infra operation, it deletes an automation infrastructure (infra) on the deployer by making an authenticated HTTP DELETE request to the appropriate API endpoint.
Common scenarios where this node is beneficial include automating the lifecycle management of Dataiku projects, deployments, bundles, and infrastructures within a CI/CD pipeline or orchestration workflow. For example, you can automate deleting an obsolete or decommissioned infrastructure setup in your deployment environment directly from n8n.
Practical example:
- You have automated deployment pipelines that create and tear down cloud infrastructures for Dataiku projects. This node can be used to delete a specific infra by its ID when it is no longer needed, helping maintain a clean and cost-effective environment.
Properties
| Name | Meaning |
|---|---|
| Infra ID | The unique identifier of the automation infrastructure to delete on the project deployer. |
Note: The provided property "Infra ID" is required for the Delete Infra operation under the Project Deployer resource.
Output
The node outputs the response from the Dataiku DSS API call in JSON format under the json field. The output typically contains the result of the delete operation, which may be an empty object or confirmation message depending on the API's response.
If the API returns binary data (not typical for delete operations), it would be available in the binary field, but for Delete Infra, the output is expected to be JSON.
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 expects the base URL of the DSS server and the user API key to be configured in the credentials.
- No additional external dependencies beyond the Dataiku DSS API and n8n's HTTP request capabilities.
Troubleshooting
- Missing Credentials Error: If the node throws "Missing Dataiku DSS API Credentials," ensure that the API key credential is properly set up and linked to the node.
- Required Parameter Missing: Errors like "Infra ID is required" indicate that the necessary input parameter was not provided. Make sure to supply the Infra ID when using the Delete Infra operation.
- HTTP Errors: If the API call fails, check the network connectivity to the DSS server, verify the API key permissions, and confirm that the Infra ID exists and is correct.
- Unexpected Response Format: If the node cannot parse the response, verify that the DSS API version is compatible and that the endpoint behaves as expected.
Links and References
- Dataiku DSS API Documentation
- Dataiku Project Deployer API Reference
- n8n Documentation - Creating Custom Nodes
This summary focuses on the Project Deployer resource and the Delete Infra operation as requested, based on static analysis of the provided source code and properties.