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 interact programmatically with various Dataiku DSS resources and operations. Specifically for the Data Quality resource and the Get Last Rule Results operation, it retrieves the most recent results of all data quality rules defined on a specified dataset partition within a project.
This is useful in scenarios where you want to monitor or audit the quality of your datasets automatically, such as:
- Fetching the latest data quality rule outcomes after a data pipeline run.
- Integrating data quality checks into automated workflows or alerts.
- Reporting or visualizing data quality metrics in dashboards or external systems.
Example use case: After running data ingestion and transformation jobs, you can use this node to get the last rule results for a dataset partition to verify if the data meets quality standards before proceeding.
Properties
| Name | Meaning |
|---|---|
| Project Key | The unique identifier of the Dataiku project containing the dataset. |
| Dataset Name | The name of the dataset on which to retrieve the last rule results. |
| Query Parameters | Optional additional query parameters to filter or modify the request (e.g., partition info). |
The Query Parameters collection allows adding key-value pairs that are appended as URL query parameters to the API call. This can include filters like partition identifiers or limits.
Output
The node outputs the JSON response from the Dataiku DSS API corresponding to the last results of all data quality rules applied to the specified dataset partition.
- The output is an array of JSON objects, each representing the result of a data quality rule.
- If the operation involves downloading files or binary content (not applicable here), the node would output binary data accordingly.
- For this operation, the output is purely JSON data detailing rule results, including statuses, metrics, and possibly timestamps.
Dependencies
- Requires a valid connection to a Dataiku DSS instance.
- Needs an API authentication token credential configured in n8n to authorize requests.
- The node constructs HTTP requests to the Dataiku DSS REST API endpoints based on user input.
Troubleshooting
- Missing Credentials Error: If the API credentials are not set or invalid, the node will throw an error indicating missing credentials.
- Required Parameter Errors: The node validates required parameters like Project Key and Dataset Name; missing these will cause errors.
- API Request Failures: Network issues, incorrect project/dataset names, or insufficient permissions may cause API errors. Check the error message for details.
- Parsing Errors: If the API returns non-JSON or unexpected responses, parsing might fail. Ensure the Dataiku DSS server is reachable and responding correctly.
Links and References
- Dataiku DSS API Documentation - Data Quality
- Dataiku DSS Official Website
- n8n Documentation - Creating Custom Nodes
This summary focuses on the "Data Quality" resource and the "Get Last Rule Results" operation as requested, extracting relevant properties and behavior from the provided source code and property definitions.