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 various Dataiku DSS resources. Specifically for the Dataset resource and the List Tables operation, it enables listing tables in a schema of a SQL connection within a specified project. This is useful when you want to programmatically retrieve available tables from datasets managed in Dataiku DSS, facilitating automation workflows that depend on dataset structure or metadata.
Common scenarios include:
- Automating data pipeline steps that require knowledge of available tables.
- Dynamically fetching table lists to drive further processing or validation.
- Integrating Dataiku DSS dataset metadata into other systems or dashboards.
Example: You have a project in Dataiku DSS and want to list all tables available in its datasets' SQL schemas to validate or document them automatically.
Properties
| Name | Meaning |
|---|---|
| Project Key | The unique identifier of the Dataiku DSS project where the dataset resides. |
| Query Parameters | Optional key-value pairs to filter or paginate the results. For example, limit, page. |
The Query Parameters collection supports many optional parameters (e.g., limit, page, filter, schemaName) that can be used to refine the request. For the "List Tables" operation, relevant parameters might include:
limit: Max number of results to return.page: Page number for paginated results.schemaName: Name of the schema to list tables from.- Other filtering options as supported by the API.
Output
The output is a JSON array containing the list of tables retrieved from the specified project's dataset schemas. Each item in the array represents a table with its associated metadata as returned by the Dataiku DSS API.
If the operation involves downloading files (not applicable for List Tables), binary data would be returned accordingly, but for this operation, the output is purely JSON.
Dependencies
- Requires an active connection to a Dataiku DSS instance.
- Needs valid API credentials (an API key) configured in n8n to authenticate requests.
- The node uses HTTP requests to communicate with the Dataiku DSS REST API endpoints.
Troubleshooting
- Missing Credentials Error: If the API key credential is not set or invalid, the node will throw an error indicating missing credentials.
- Required Parameter Missing: The node validates required parameters such as
Project Keyand will throw errors if these are not provided. - API Request Failures: Network issues, incorrect project keys, or insufficient permissions may cause API call failures. Check the error message for details.
- Unexpected Response Format: If the API returns unexpected data, parsing errors may occur. Ensure the Dataiku DSS API version is compatible.
Links and References
- Dataiku DSS API Documentation
- Dataiku DSS Dataset Management
- n8n Documentation - Creating Custom Nodes
This summary focuses on the Dataset resource's List Tables operation, describing how the node constructs the API request, handles input properties, and processes the response.