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. Specifically for the Dataset resource and the Create Dataset operation, it allows creating new datasets within a specified project in Dataiku DSS.
Common scenarios where this node is beneficial include automating dataset creation as part of data pipeline orchestration, integrating dataset management into broader ETL workflows, or programmatically managing datasets based on external triggers or events.
For example, you might use this node to automatically create a dataset in a Dataiku project when new data arrives, or as part of a deployment process that sets up required datasets before running recipes or models.
Properties
| Name | Meaning |
|---|---|
| Project Key | The key identifier of the Dataiku project where the dataset will be created. |
| Request Body | JSON object containing the dataset configuration details required by the Dataiku API. |
- Project Key: Specifies the target project in Dataiku DSS.
- Request Body: A JSON structure defining the dataset's properties such as its name, type, connection details, schema, and other settings as per Dataiku DSS API requirements for dataset creation.
Output
The node outputs the response from the Dataiku DSS API after attempting to create the dataset. The output is structured as JSON and typically contains information about the newly created dataset or error details if the creation failed.
- The
jsonfield includes the parsed JSON response from the API. - If the API returns binary data (not typical for dataset creation), it would be provided in the
binaryfield, but this operation primarily deals with JSON responses.
Dependencies
- Requires an active connection to a Dataiku DSS instance.
- Needs valid API credentials (an API key) for authentication with the Dataiku DSS server.
- The node expects the Dataiku DSS server URL and user API key to be configured in the credentials.
Troubleshooting
- Missing Credentials Error: If the node throws "Missing Dataiku DSS API Credentials," ensure that the API key credential is properly set up in n8n.
- Required Parameters Missing: Errors like "Project Key is required" indicate that mandatory input parameters are not provided. Verify that all required fields are filled.
- API Errors: If the API call fails, the node throws an error with the message prefixed by "Error calling Dataiku DSS API." Check the stack trace and ensure the request body conforms to the Dataiku API specification.
- Invalid JSON in Request Body: Ensure the JSON provided in the Request Body property is valid and matches the expected schema for dataset creation.
Links and References
- Dataiku DSS API Documentation - Datasets
- Dataiku DSS API Authentication
- n8n Documentation - Creating Custom Nodes
This summary focuses on the Dataset resource with the Create Dataset operation, describing how the node constructs the API request, handles inputs, and processes outputs accordingly.