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 management, among many other resources. Specifically for the Project Deployer resource and the Get Deployment Status operation, it retrieves the current status of a specified project deployment on the Dataiku Project Deployer service.
This is useful in automation workflows where you need to monitor or check the state of a deployment, such as verifying if a deployment is active, completed, failed, or in progress before triggering subsequent steps.
Practical example:
- After initiating a deployment of a project via the Project Deployer, use this node operation to poll and get the deployment status periodically until it reaches a desired state (e.g., "deployed" or "failed").
- Integrate deployment status checks into CI/CD pipelines to automate deployment monitoring and alerting.
Properties
| Name | Meaning |
|---|---|
| Deployment ID | The unique identifier of the deployment whose status you want to retrieve. |
The node also requires credentials for accessing the Dataiku DSS API, specifically an API key and server URL, but these are configured separately and not exposed as input properties here.
Output
The output JSON contains the deployment status information returned by the Dataiku DSS API for the specified deployment ID. This typically includes details such as:
- Current state of the deployment (e.g., running, succeeded, failed)
- Progress or percentage completion
- Any error messages or logs related to the deployment
- Metadata about the deployment like timestamps, version, etc.
If the operation involves downloading files (not applicable for this operation), binary data would be returned accordingly, but for "Get Deployment Status," the output is purely JSON.
Dependencies
- Requires a valid connection to a Dataiku DSS instance.
- Requires an API key credential for authenticating requests to the Dataiku DSS API.
- The node uses HTTP requests to the Dataiku DSS REST API endpoints.
- Proper permissions on the Dataiku DSS instance to access project deployer deployments.
Troubleshooting
- Missing Credentials Error: If the API key or server URL is not set or invalid, the node will throw an error indicating missing credentials. Ensure the API key credential is properly configured in n8n.
- Deployment ID Required: The node throws an error if the Deployment ID property is empty when calling this operation. Make sure to provide a valid deployment ID.
- HTTP Errors: If the deployment ID does not exist or the user lacks permission, the API may return 404 or 403 errors. Verify the deployment ID and user permissions.
- Parsing Errors: If the API response is malformed or unexpected, JSON parsing might fail. Check the API server health and network connectivity.
Links and References
- Dataiku DSS API Documentation - Project Deployer
- Dataiku DSS REST API Reference
- n8n Documentation - Creating Custom Nodes
This summary focuses on the Project Deployer resource and the Get Deployment Status operation as requested, based on static analysis of the provided source code and input properties.