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, allowing users to perform a wide range of operations on Dataiku DSS resources. Specifically for the Data Quality resource and the Update Rule Configuration operation, it enables updating the configuration of a specific data quality rule on a dataset within a project.
Common scenarios where this node is beneficial include:
- Automating updates to data quality rules as part of a data pipeline.
- Managing data quality configurations programmatically without manual intervention in the Dataiku DSS UI.
- Integrating data quality rule updates into broader workflows that involve dataset management or monitoring.
For example, you might use this node to update thresholds or parameters of a data quality rule after analyzing recent data trends, ensuring your data validation remains accurate and relevant.
Properties
| Name | Meaning |
|---|---|
| Project Key | The unique identifier of the Dataiku DSS project containing the dataset. |
| Dataset Name | The name of the dataset on which the data quality rule is configured. |
| Rule ID | The identifier of the specific data quality rule to update. |
| Request Body | A JSON object representing the new configuration settings for the data quality rule. |
Output
The node outputs the response from the Dataiku DSS API after attempting to update the rule configuration. The output is structured as JSON and typically contains details about the updated rule configuration or confirmation of the update.
If the API returns binary data (not typical for this operation), it would be provided as binary output, but for the Update Rule Configuration operation, the output is JSON.
Example output structure (simplified):
{
"ruleId": "string",
"status": "updated",
"configuration": {
// Updated rule configuration details
}
}
Dependencies
- Requires an active connection to a Dataiku DSS instance.
- Needs valid API credentials including the DSS server URL and a user API key.
- The node expects these credentials to be configured in n8n under a generic API key credential type (referred generically as "an API key credential").
- The Dataiku DSS API must be accessible from the environment where n8n runs.
Troubleshooting
- Missing Credentials Error: If the node throws an error about missing credentials, ensure that the Dataiku DSS API credentials are properly set up in n8n.
- Required Parameter Errors: The node validates required parameters such as Project Key, Dataset Name, and Rule ID. Missing any of these will cause an error. Double-check that all required fields are filled.
- API Request Failures: Network issues, incorrect API keys, or insufficient permissions can cause API call failures. Verify network connectivity, API key validity, and user permissions in Dataiku DSS.
- Invalid JSON in Request Body: The request body must be valid JSON. Invalid JSON syntax will cause errors. Use proper JSON formatting.
- Unexpected Response Format: If the API response cannot be parsed as JSON, the node attempts to return raw text. This may indicate an issue with the API or the request.
Links and References
- Dataiku DSS API Documentation
- Dataiku DSS Data Quality Rules
- n8n Documentation - Creating Custom Nodes
This summary focuses on the Data Quality resource and the Update Rule Configuration operation as requested, based on static analysis of the provided source code and property definitions.