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, enabling automation and management of various Dataiku DSS resources. Specifically, for the "Create Project From Bundle With Archive" operation under the "Bundles Automation-Side" resource, it allows users to create a new project in Dataiku DSS from an existing bundle archive. This is useful for automating project creation workflows where projects are packaged as bundles and need to be instantiated programmatically.
Common scenarios include:
- Automating deployment pipelines by creating projects from pre-built bundles.
- Migrating or replicating projects across different Dataiku DSS instances.
- Integrating Dataiku DSS project creation into larger data orchestration workflows.
Example: Automatically create a new Dataiku DSS project from a bundle archive stored in your environment, triggered by an external event or schedule.
Properties
| Name | Meaning |
|---|---|
| Query Parameters | A collection of optional query parameters that can be added to the API request URL. Includes many possible keys such as archivePath, projectKey, wait, withScenarios, etc., allowing fine control over the creation process. |
| projectKey | The key identifier of the project to create or target. |
| archivePath | Path to the archive file containing the bundle to use for project creation. |
| wait | Boolean flag indicating whether to wait for the project creation process to complete before returning. Defaults to true. |
| withScenarios | Boolean flag indicating whether to include scenarios when creating the project from the bundle. Defaults to true. |
| exportAnalysisModels | Boolean flag to export analysis models during the bundle processing. Defaults to true. |
| exportManaged | Boolean flag to export managed datasets during the bundle processing. Defaults to true. |
| exportSavedModels | Boolean flag to export saved models during the bundle processing. Defaults to true. |
| exportUploads | Boolean flag to export uploaded files during the bundle processing. Defaults to true. |
Note: The full list of query parameters is extensive and includes many other options related to project configuration, folder IDs, deletion modes, and more, providing detailed customization of the project creation process.
Output
The node outputs the JSON response returned by the Dataiku DSS API after attempting to create the project from the bundle archive. The output structure depends on the API response but typically includes details about the created project or status information.
If the operation involves downloading files (not applicable here), binary data would be prepared accordingly, but for this operation, the output is 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 uses HTTP requests to communicate with the Dataiku DSS REST API endpoints.
- No additional external services beyond Dataiku DSS are required.
Troubleshooting
- Missing Credentials Error: If the API credentials are not provided or invalid, the node will throw an error "Missing Dataiku DSS API Credentials". Ensure you have configured the API key credential correctly.
- Required Parameter Missing: The node validates required parameters like
projectKeyand others depending on the operation. If missing, it throws errors specifying which parameter is required. - API Request Failures: Errors from the Dataiku DSS API (e.g., network issues, permission denied, invalid bundle archive) will be surfaced as node errors with messages prefixed by "Error calling Dataiku DSS API".
- Timeouts: If
waitis set to true, the node waits for the project creation to complete; if the process takes too long, consider settingwaitto false or increasing timeout settings externally. - Invalid Bundle Archive: Ensure the archive path or file used is a valid Dataiku DSS bundle archive compatible with the target DSS version.
Links and References
- Dataiku DSS API Documentation
- Dataiku DSS Bundles and Project Deployment
- n8n Documentation on Creating Custom Nodes
This summary focuses on the "Create Project From Bundle With Archive" operation within the "Bundles Automation-Side" resource, describing its purpose, input properties, output, dependencies, and common troubleshooting points based on static code analysis.