Actions364
- API Service Actions
- Bundles Automation-Side Actions
- Bundles Design-Side Actions
- Connection Actions
- Continuous Activity 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
- Dataset Actions
- 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
- Get Last Metric Values
- Get Metadata
- Get Schema
- Get Single Metric History
- List Datasets
- List Partitions
- List Tables
- List Tables Schemas
- Prepare Tables Import
- Run Checks
- Set Metadata
- Set Schema
- Synchronize Hive Metastore
- Update Dataset Settings
- Update From Hive Metastore
- Dataset Statistic Actions
- Discussion Actions
- DSS Administration Actions
- Flow Documentation Actions
- Insight Actions
- Internal Metric Actions
- Job Actions
- Library Actions
- LLM Mesh Actions
- Long Task Actions
- Machine Learning - Experiment Tracking Actions
- Machine Learning - Lab Actions
- 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
- Delete Visual Analysis
- Deploy Trained Model to Flow
- Download Model Documentation of Trained Model
- Generate Model Documentation From Custom Template
- 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
- Start Training ML Task
- Update User Metadata for Trained Model
- Update Visual Analysis
- Machine Learning - Saved Model Actions
- Compute Partial Dependencies of Version
- 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
- Get Version Scoring PMML
- Get Version Snippet
- Import MLflow Version From File or Path
- List Saved Models
- List Versions
- Set Version Active
- Set Version User Meta
- Update Saved Model
- Macro Actions
- Managed Folder Actions
- Meaning Actions
- Model Comparison Actions
- Notebook Actions
- Plugin Actions
- 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
- 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
- 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 Actions
- Project Deployer Actions
- Create Deployment
- Create Infra
- Create Project
- Delete Bundle
- Delete Deployment
- Delete Infra
- Delete Project
- Get Deployment
- Get Deployment Governance Status
- Get Deployment Settings
- Get Deployment Status
- Get Infra
- Get Infra Settings
- Get Project
- Get Project Settings
- Save Deployment Settings
- Save Infra Settings
- Save Project Settings
- Update Deployment
- Upload Bundle
- Project Folder Actions
- Recipe Actions
- Scenario Actions
- Security Actions
- SQL Query Actions
- Streaming Endpoint Actions
- Webapp Actions
- Wiki Actions
- Workspace Actions
Data Quality → Get Rule History
AI-generatedOverview
This node integrates with the Dataiku DSS API, enabling users to perform a wide range of operations on Dataiku DSS resources. Specifically for the Data Quality resource and the Get Rule History operation, it retrieves the history of computed data quality rules on a specified dataset within a project, filtered by timeframe and optional parameters.
This node is beneficial in scenarios where you want to monitor or audit the historical performance and outcomes of data quality rules applied to datasets in Dataiku DSS projects. For example, you might use it to track how certain data quality rules have evolved over time or to investigate past rule violations.
Properties
| Name | Meaning |
|---|---|
| Project Key | The unique identifier of the Dataiku DSS project containing the dataset. |
| Dataset Name | The name of the dataset for which to retrieve the rule history. |
| Query Parameters | Optional filters and parameters to refine the query, such as date ranges, pagination, etc. |
The Query Parameters collection can include various optional fields (e.g., limit, page, filter) to control the scope and amount of returned data.
Output
The output is a JSON array where each item represents an entry in the rule history for the specified dataset. Each entry contains details about the computed data quality rules matching the specified timeframe and filters.
If the operation involves downloading files (not applicable for this operation), binary data would be provided accordingly.
Dependencies
- Requires an active connection to a Dataiku DSS instance.
- Requires valid API credentials: an API server URL and a user API key.
- The node uses HTTP requests to interact with the Dataiku DSS REST API.
- No additional external services are required beyond the Dataiku DSS API.
Troubleshooting
- Missing Credentials Error: If the node throws "Missing Dataiku DSS API Credentials," ensure that the API key credential is configured correctly in n8n.
- Required Parameter Errors: The node validates required parameters like Project Key and Dataset Name. Missing these will cause errors indicating which parameter is missing.
- API Request Failures: Network issues, incorrect URLs, or invalid API keys may cause request failures. Check connectivity and credentials.
- Parsing Errors: If the response cannot be parsed as JSON, the raw text is returned. This might indicate unexpected API responses or errors.
- Rate Limits or Permissions: Ensure the API key has sufficient permissions to access the requested project and dataset.
Links and References
- Dataiku DSS API Documentation
- Dataiku DSS Data Quality API Reference
- n8n Documentation - Creating Custom Nodes
This summary focuses on the Data Quality resource and the Get Rule History operation, describing its purpose, inputs, outputs, dependencies, and common troubleshooting tips based on static analysis of the node's source code and provided property definitions.