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 users to perform a wide range of operations on Dataiku DSS resources directly from n8n workflows. It supports managing projects, bundles, API services, datasets, dashboards, machine learning models, plugins, and many other Dataiku DSS entities.
For the "Upload Bundle" operation under the "Bundles Automation-Side" resource, the node uploads a bundle to an existing project in Dataiku DSS. This is useful for automating deployment or updating of project bundles programmatically, such as when integrating CI/CD pipelines or synchronizing project states across environments.
Practical examples:
- Automatically upload a new version of a project bundle after building it in a CI system.
- Integrate bundle uploads into a workflow that triggers downstream automation or testing.
- Manage multiple project bundles across different Dataiku DSS instances via n8n.
Properties
| Name | Meaning |
|---|---|
| Project Key | The key identifier of the Dataiku DSS project where the bundle will be uploaded. |
The node also accepts a property named data (string) which is used to provide the content of the bundle file to upload (binary data can be referenced).
Output
The output of this operation is the JSON response returned by the Dataiku DSS API after uploading the bundle. This typically contains information about the upload status or details of the imported bundle.
If the operation involves binary data (e.g., uploading files), the node handles the binary content appropriately, but for "Upload Bundle" specifically, the input is expected as binary data and the output is JSON confirming the upload.
Dependencies
- Requires valid Dataiku DSS API credentials, including the server URL and an API key.
- The node uses HTTP requests to communicate with the Dataiku DSS REST API.
- The user must configure the Dataiku DSS API credentials in n8n before using this node.
Troubleshooting
- Missing Credentials Error: If the API credentials are not set or invalid, the node throws an error indicating missing credentials.
- Required Parameter Errors: The node validates required parameters like Project Key and throws errors if they are missing.
- HTTP Request Failures: Network issues, incorrect URLs, or permission problems may cause request failures. Check the API key permissions and network connectivity.
- File Upload Issues: Ensure the bundle file is correctly provided as binary data in the input named "data".
- Unexpected Response Format: If the API returns non-JSON responses or errors, the node attempts to parse them; failure to parse may result in raw text output.
Links and References
- Dataiku DSS API Documentation
- Dataiku DSS Bundles Automation API
- n8n Documentation - Creating Custom Nodes
This summary focuses on the "Upload Bundle" operation within the "Bundles Automation-Side" resource, describing its purpose, inputs, outputs, dependencies, and common troubleshooting points based on static analysis of the node's source code and provided properties.