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 various Dataiku DSS resources. Specifically for the Streaming Endpoint resource, it supports updating streaming endpoint settings among other actions like creating, listing, deleting, and managing schemas.
The Update Streaming Endpoint Settings operation allows users to modify the configuration of an existing streaming endpoint within a specified project. This is useful when you need to change how data streams are handled or adjust parameters related to streaming endpoints in your Dataiku DSS projects.
Common scenarios:
- Adjusting streaming endpoint configurations to optimize data flow.
- Updating authentication or connection details for a streaming endpoint.
- Managing streaming endpoints programmatically as part of automated workflows.
Practical example:
You have a streaming endpoint that ingests real-time data into your Dataiku project. You want to update its settings to change buffer sizes or enable/disable certain features without manually accessing the Dataiku UI. Using this node operation, you can automate that update via n8n.
Properties
| Name | Meaning |
|---|---|
| Project Key | The unique key identifying the Dataiku DSS project where the streaming endpoint exists. |
| Streaming Endpoint ID | The identifier of the streaming endpoint whose settings you want to update. |
| Request Body | A JSON object containing the new settings/configuration to apply to the streaming endpoint. |
Output
The node outputs the response from the Dataiku DSS API after performing the update operation. The output is structured as JSON and typically contains the updated streaming endpoint object or confirmation of the update.
If the operation involves downloading files (not applicable here), the node would output binary data representing the downloaded content. For this operation, the output is JSON only.
Example output structure (simplified):
{
"id": "streaming-endpoint-id",
"name": "Updated Streaming Endpoint Name",
"settings": {
// Updated settings fields here
},
"status": "updated"
}
Dependencies
- Requires an active Dataiku DSS API credential configured in n8n, which includes:
- The URL or IP address of the Dataiku DSS server.
- A valid user API key for authentication.
- The node makes HTTP requests to the Dataiku DSS REST API endpoints.
- No additional external libraries beyond those bundled with n8n are required.
Troubleshooting
Common issues:
- Missing credentials: If the API key or server URL is not set, the node will throw an error indicating missing credentials.
- Invalid or missing parameters: The node validates required parameters such as Project Key and Streaming Endpoint ID. Omitting these will cause errors.
- API errors: Errors returned by the Dataiku DSS API (e.g., unauthorized, not found) will be surfaced as node errors with descriptive messages.
Error messages and resolutions:
"Missing Dataiku DSS API Credentials": Ensure you have configured the API credentials correctly in n8n."Project Key is required": Provide the project key parameter; it is mandatory for this operation."Streaming Endpoint ID is required": Provide the streaming endpoint ID parameter."Error calling Dataiku DSS API: <message>": Check the API key validity, network connectivity, and that the endpoint exists.
Links and References
- Dataiku DSS API Documentation
- Dataiku Streaming Endpoints API Reference
- n8n Documentation on Creating Custom Nodes
Summary
This node operation enables updating the settings of a streaming endpoint in a Dataiku DSS project by sending a properly authenticated request with the desired configuration changes. It is ideal for automating management of streaming endpoints within Dataiku DSS environments.