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 interact programmatically with various Dataiku DSS resources and operations. Specifically, for the Data Quality resource and the Get Data Quality Project Current Status operation, it retrieves the latest data quality status of each dataset within a specified project.
This functionality is beneficial in scenarios where you want to monitor the health and quality of datasets across a project automatically, such as triggering alerts or downstream processes based on data quality metrics. For example, a data engineer might use this node to fetch current data quality statuses daily and log or visualize them for compliance and monitoring purposes.
Properties
| Name | Meaning |
|---|---|
| Project Key | The unique identifier of the Dataiku DSS project whose data quality status you want to retrieve. |
| Query Parameters | Optional additional parameters to filter or modify the request. Available options include: Active (boolean), Activity (string), All Users (boolean), Archive Path (string), Catalog Name (string), Column Name (string), Limit (number), Page (number), Partition (string), Tags (string), Wait (boolean), With Scenarios (boolean), and many others. These parameters allow fine-tuning of the API call. |
Note: The full list of query parameters is extensive; they provide granular control over filtering, pagination, and other aspects of the API request.
Output
The node outputs JSON data representing the current data quality status of each dataset in the specified project. The structure typically includes details about datasets and their associated data quality metrics and statuses.
If the operation involves downloading files (not applicable for this specific operation), the node can output binary data representing those files.
Dependencies
- Requires an active connection to a Dataiku DSS instance.
- Requires valid API credentials (an API key) for authentication with the Dataiku DSS API.
- 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 an error about missing credentials, ensure that the API key credential for Dataiku DSS is properly set up in n8n.
- Project Key Required: The operation requires a valid project key. Omitting this will cause an error.
- API Request Errors: Errors returned from the Dataiku DSS API (e.g., 404 Not Found, 401 Unauthorized) indicate issues like invalid project keys, insufficient permissions, or expired API keys. Verify the inputs and credentials.
- Network Issues: Ensure that the n8n instance can reach the Dataiku DSS server URL.
- Invalid Query Parameters: Using unsupported or incorrectly formatted query parameters may cause API errors. Refer to Dataiku DSS API documentation for valid parameters.
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 Data Quality Project Current Status" operation, describing how the node constructs the API request, required inputs, and expected outputs based on static code analysis.