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 node integrates with the Dataiku DSS API, enabling users to perform a wide range of operations on various Dataiku DSS resources. Specifically for the Job resource and the Run Job operation, this node allows starting a job run within a specified project in Dataiku DSS. This is useful for automating job executions as part of data workflows or pipelines.
Common scenarios include:
- Triggering batch jobs or data processing tasks programmatically.
- Automating job runs based on external events or schedules.
- Integrating Dataiku DSS job execution into broader automation workflows.
Example: You can use this node to start a job that builds a dataset or triggers a machine learning model training process in Dataiku DSS, then continue downstream processing based on the job's output.
Properties
| Name | Meaning |
|---|---|
| Project Key | The key identifier of the Dataiku DSS project where the job will be run. |
| Request Body | JSON object containing parameters or configuration for the job run request body. |
These properties are used to specify which project the job belongs to and any additional details required by the API to run the job.
Output
The node outputs the response from the Dataiku DSS API after attempting to run the job. The output is structured as JSON and typically contains information about the started job, such as job ID, status, and other metadata returned by the API.
If the operation involves downloading files or binary content (not applicable specifically for "Run Job"), the node would output binary data accordingly. For the "Run Job" operation, the output is JSON only.
Dependencies
- Requires an active connection to a Dataiku DSS instance.
- Needs an API authentication token credential configured in n8n to authorize requests to the Dataiku DSS API.
- The node uses HTTP requests to communicate with the Dataiku DSS REST API endpoints.
Troubleshooting
- Missing Credentials Error: If the node throws an error about missing credentials, ensure you have configured the required API authentication token for Dataiku DSS in n8n.
- Project Key Required: The "Project Key" property must be provided; otherwise, the node will throw an error indicating it is required.
- API Errors: Errors returned by the Dataiku DSS API (e.g., invalid job parameters, unauthorized access) will be surfaced as node errors with messages from the API. Check the API response and verify your inputs.
- Invalid JSON in Request Body: Ensure the "Request Body" JSON is well-formed; malformed JSON will cause parsing errors.
- Network Issues: Connectivity problems to the Dataiku DSS server will result in request failures. Verify network access and server availability.
Links and References
- Dataiku DSS API Documentation — Official API reference for understanding available endpoints and payloads.
- Dataiku DSS Jobs API — Specific documentation on managing and running jobs via the API.
This summary focuses on the "Job" resource and "Run Job" operation as requested, describing how the node constructs the API request, handles input properties, and returns the job run response.