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
The "Create Deployment" operation of the Dataiku DSS node allows users to create a new deployment in the Dataiku Project Deployer service via its API. This node interacts with the Dataiku DSS API to manage deployments, enabling automation and integration within workflows.
Typical use cases include:
- Automating the deployment of Dataiku projects to different environments.
- Integrating deployment creation into CI/CD pipelines.
- Managing project lifecycle by programmatically creating deployments as part of data workflows.
For example, a user can configure this node to create a deployment for a specific project key with custom deployment settings provided in JSON format, facilitating automated release processes.
Properties
| Name | Meaning |
|---|---|
| Request Body | A JSON object containing the details and configuration for the deployment to be created. |
Note: The "Request Body" property is used to specify the payload sent to the API when creating the deployment. It should conform to the expected schema of the Dataiku Project Deployer API for deployment creation.
Output
The node outputs the response from the Dataiku DSS API after attempting to create the deployment. The output is structured as JSON and typically contains information about the newly created deployment, such as deployment ID, status, and other metadata returned by the API.
If the API returns binary data (not typical for deployment creation), it would be provided as binary output, but for this operation, the output is JSON.
Dependencies
- Requires an active connection to a Dataiku DSS instance.
- Requires valid API credentials: specifically, an API key credential for authenticating requests to the Dataiku DSS API.
- The node uses HTTP requests to communicate with the Dataiku DSS server; thus, network access to the server is necessary.
- No additional external services or environment variables are required beyond the API credentials.
Troubleshooting
- Missing Credentials Error: If the API credentials are not set or invalid, the node will throw an error indicating missing Dataiku DSS API credentials. Ensure that the API key credential is configured correctly in n8n.
- Required Parameters Missing: The node validates required parameters such as the deployment ID or project key depending on the operation. For "Create Deployment," ensure the request body is properly formed and includes all mandatory fields.
- API Errors: If the Dataiku DSS API returns an error (e.g., due to invalid JSON, insufficient permissions, or server issues), the node will throw an error with the message from the API. Review the error message and stack trace for troubleshooting.
- Network Issues: Connectivity problems to the Dataiku DSS server will cause request failures. Verify network connectivity and server availability.
Links and References
- Dataiku DSS API Documentation
- Project Deployer API Reference
- n8n Documentation - Creating Custom Nodes
This summary focuses on the "Create Deployment" operation of the "Project Deployer" resource based on the provided source code and input properties.